In [2]:
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
We already downloaded a subsetted MODIS-Aqua chlorophyll-a dataset for the Arabian Sea.
We can read all the netcdf files into one xarray Dataset using the open_mfsdataset function. Note that this does not load the data into memory yet. That only happens when we try to access the values.
In [3]:
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_chla9km/ModisA_Arabian_Sea_chlor_a_9km_*_D.nc')
both_datasets = [ds_8day, ds_daily]
How much data is contained here? Let's get the answer in MB.
In [4]:
print([(ds.nbytes / 1e6) for ds in both_datasets])
[534.295504, 4241.4716]
The 8-day dataset is ~534 MB while the daily dataset is 4.2 GB. These both easily fit in RAM. So let's load them all into memory
In [5]:
[ds.load() for ds in both_datasets]
Out[5]:
[<xarray.Dataset>
Dimensions: (eightbitcolor: 256, lat: 276, lon: 360, rgb: 3, time: 667)
Coordinates:
* lat (lat) float64 27.96 27.87 27.79 27.71 27.62 27.54 27.46 ...
* lon (lon) float64 45.04 45.13 45.21 45.29 45.38 45.46 45.54 ...
* rgb (rgb) int64 0 1 2
* eightbitcolor (eightbitcolor) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 ...
* time (time) datetime64[ns] 2002-07-04 2002-07-12 2002-07-20 ...
Data variables:
chlor_a (time, lat, lon) float64 nan nan nan nan nan nan nan nan ...
palette (time, rgb, eightbitcolor) float64 -109.0 0.0 108.0 ...,
<xarray.Dataset>
Dimensions: (eightbitcolor: 256, lat: 276, lon: 360, rgb: 3, time: 5295)
Coordinates:
* lat (lat) float64 27.96 27.87 27.79 27.71 27.62 27.54 27.46 ...
* lon (lon) float64 45.04 45.13 45.21 45.29 45.38 45.46 45.54 ...
* rgb (rgb) int64 0 1 2
* eightbitcolor (eightbitcolor) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 ...
* time (time) datetime64[ns] 2002-07-04 2002-07-05 2002-07-06 ...
Data variables:
chlor_a (time, lat, lon) float64 nan nan nan nan nan nan nan nan ...
palette (time, rgb, eightbitcolor) float64 -109.0 0.0 108.0 ...]
In [6]:
def fix_bad_data(ds):
# for some reason, the cloud / land mask is backwards on some data
# this is obvious because there are chlorophyl values less than zero
bad_data = ds.chlor_a.groupby('time').min() < 0
# loop through and fix
for n in np.nonzero(bad_data.values)[0]:
data = ds.chlor_a[n].values
ds.chlor_a.values[n] = np.ma.masked_less(data, 0).filled(np.nan)
In [7]:
[fix_bad_data(ds) for ds in both_datasets]
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/xarray/core/variable.py:1046: RuntimeWarning: invalid value encountered in less
if not reflexive
Out[7]:
[None, None]
In [8]:
ds_8day.chlor_a>0
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/xarray/core/variable.py:1046: RuntimeWarning: invalid value encountered in greater
if not reflexive
Out[8]:
<xarray.DataArray 'chlor_a' (time: 667, lat: 276, lon: 360)>
array([[[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
...,
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False]],
[[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
...,
[False, False, False, ..., False, False, False],
[False, False, False, ..., True, False, False],
[False, False, False, ..., False, False, False]],
[[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
...,
[False, False, False, ..., False, False, True],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, True, True]],
...,
[[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
...,
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False]],
[[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
...,
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False]],
[[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
...,
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False]]], dtype=bool)
Coordinates:
* lat (lat) float64 27.96 27.87 27.79 27.71 27.62 27.54 27.46 27.37 ...
* lon (lon) float64 45.04 45.13 45.21 45.29 45.38 45.46 45.54 45.63 ...
* time (time) datetime64[ns] 2002-07-04 2002-07-12 2002-07-20 ...
In [9]:
(ds_8day.chlor_a>0).sum(dim='time').plot()
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/xarray/core/variable.py:1046: RuntimeWarning: invalid value encountered in greater
if not reflexive
Out[9]:
<matplotlib.collections.QuadMesh at 0x1196571d0>
In [10]:
# find a mask for the land
ocean_mask = (ds_8day.chlor_a>0).sum(dim='time')>0
#ocean_mask = (ds_daily.chlor_a>0).sum(dim='time')>0
num_ocean_points = ocean_mask.sum().values # compute the total nonzeros regions(data point)
ocean_mask.plot()
plt.title('%g total ocean points' % num_ocean_points)
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/xarray/core/variable.py:1046: RuntimeWarning: invalid value encountered in greater
if not reflexive
Out[10]:
<matplotlib.text.Text at 0x13d467a20>
In [11]:
#ds_8day
In [14]:
#ds_daily
In [16]:
plt.figure(figsize=(8,6))
ds_daily.chlor_a.sel(time='2002-11-18',method='nearest').plot(norm=LogNorm())
#ds_daily.chlor_a.sel(time=target_date, method='nearest').plot(norm=LogNorm())
Out[16]:
<matplotlib.collections.QuadMesh at 0x11dc4f8d0>
/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 [28]:
#list(ds_daily.groupby('time')) # take a look at what's inside
Now we count up the number of valid points in each snapshot and divide by the total number of ocean points.
In [29]:
'''
<xarray.Dataset>
Dimensions: (eightbitcolor: 256, lat: 144, lon: 276, rgb: 3, time: 4748)
'''
ds_daily.groupby('time').count() # information from original data
Out[29]:
<xarray.Dataset>
Dimensions: (time: 5295)
Coordinates:
* time (time) datetime64[ns] 2002-07-04 2002-07-05 2002-07-06 ...
Data variables:
chlor_a (time) int64 658 1170 1532 2798 2632 1100 1321 636 2711 1163 ...
palette (time) int64 768 768 768 768 768 768 768 768 768 768 768 768 ...
In [ ]:
In [30]:
ds_daily.chlor_a.groupby('time').count()/float(num_ocean_points)
Out[30]:
<xarray.DataArray 'chlor_a' (time: 5295)>
array([ 0.01053255, 0.01872809, 0.02452259, ..., 0. ,
0. , 0. ])
Coordinates:
* time (time) datetime64[ns] 2002-07-04 2002-07-05 2002-07-06 ...
In [31]:
count_8day,count_daily = [ds.chlor_a.groupby('time').count()/float(num_ocean_points)
for ds in (ds_8day,ds_daily)]
In [32]:
#count_8day = ds_8day.chl_ocx.groupby('time').count()/float(num_ocean_points)
#coundt_daily = ds_daily.chl_ocx.groupby('time').count()/float(num_ocean_points)
#count_8day, coundt_daily = [ds.chl_ocx.groupby('time').count()/float(num_ocean_points)
# for ds in ds_8day, ds_daily] # not work in python 3
In [33]:
plt.figure(figsize=(12,4))
count_8day.plot(color='k')
count_daily.plot(color='r')
plt.legend(['8 day','daily'])
Out[33]:
<matplotlib.legend.Legend at 0x11bf9e160>
In [34]:
count_8day_clim, coundt_daily_clim = [count.groupby('time.month').mean() # monthly data
for count in (count_8day, count_daily)]
In [35]:
# mean value of the monthly data on the count of nonzeros
plt.figure(figsize=(12,4))
count_8day_clim.plot(color='k')
coundt_daily_clim.plot(color='r')
plt.legend(['8 day', 'daily'])
Out[35]:
<matplotlib.legend.Legend at 0x11da4c2e8>
In [36]:
target_date = '2003-02-15'
plt.figure(figsize=(8,6))
ds_8day.chlor_a.sel(time=target_date, method='nearest').plot(norm=LogNorm())
Out[36]:
<matplotlib.collections.QuadMesh at 0x11de1d780>
/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 [37]:
plt.figure(figsize=(8,6))
ds_daily.chlor_a.sel(time=target_date, method='nearest').plot(norm=LogNorm())
Out[37]:
<matplotlib.collections.QuadMesh at 0x11e278f28>
/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 [38]:
ds_daily.chlor_a[0].sel_points(lon=[65, 70], lat=[16, 18], method='nearest') # the time is selected!
#ds_daily.chl_ocx[0].sel_points(time= times, lon=lons, lat=times, method='nearest')
Out[38]:
<xarray.DataArray 'chlor_a' (points: 2)>
array([ nan, nan])
Coordinates:
time datetime64[ns] 2002-07-04
lon (points) float64 65.04 70.04
lat (points) float64 16.04 18.04
* points (points) int64 0 1
In [42]:
df = pd.DataFrame([[1.4, np.nan], [7.1, -4.5], [np.nan, np.nan], [0.75, -1.3]],
index=['a', 'b', 'c', 'd'], columns=['one', 'two'])
df.mean(axis=1,skipna=None) # try skipna=False, skipna=True(seems to equiv to skipna=None)
Out[42]:
a 1.400
b 1.300
c NaN
d -0.275
dtype: float64
In [43]:
ds_9day = ds_daily.resample('9D', dim='time') # see the above for doc, test case, & default behavior
ds_9day
Out[43]:
<xarray.Dataset>
Dimensions: (eightbitcolor: 256, lat: 276, lon: 360, rgb: 3, time: 589)
Coordinates:
* lat (lat) float64 27.96 27.87 27.79 27.71 27.62 27.54 27.46 ...
* lon (lon) float64 45.04 45.13 45.21 45.29 45.38 45.46 45.54 ...
* rgb (rgb) int64 0 1 2
* eightbitcolor (eightbitcolor) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 ...
* time (time) datetime64[ns] 2002-07-04 2002-07-13 2002-07-22 ...
Data variables:
chlor_a (time, lat, lon) float64 nan nan nan nan nan nan nan nan ...
palette (time, rgb, eightbitcolor) float64 -109.0 0.0 108.0 ...
In [44]:
plt.figure(figsize=(8,6))
ds_9day.chlor_a.sel(time=target_date, method='nearest').plot(norm=LogNorm())
Out[44]:
<matplotlib.collections.QuadMesh at 0x11e16bd30>
/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 [45]:
# check the range for the longitude
print(ds_9day.lon.min(),'\n' ,ds_9day.lat.min())
<xarray.DataArray 'lon' ()>
array(45.04166793823242)
<xarray.DataArray 'lat' ()>
array(5.041661739349365)
In [46]:
# in the following we deal with the data from the gdp float
from buyodata import buoydata
import os
In [47]:
# a list of files
fnamesAll = ['./gdp_float/buoydata_1_5000.dat','./gdp_float/buoydata_5001_10000.dat','./gdp_float/buoydata_10001_15000.dat','./gdp_float/buoydata_15001_jun16.dat']
In [48]:
# read them and cancatenate them into one DataFrame
dfAll = pd.concat([buoydata.read_buoy_data(f) for f in fnamesAll]) # around 4~5 minutes
#mask = df.time>='2002-07-04' # we only have data after this data for chlor_a
dfvvAll = dfAll[dfAll.time>='2002-07-04']
sum(dfvvAll.time<'2002-07-04') # recheck whether the time is
Out[48]:
0
In [49]:
# process the data so that the longitude are all >0
print('before processing, the minimum longitude is%f4.3 and maximum is %f4.3' % (dfvvAll.lon.min(), dfvvAll.lon.max()))
mask = dfvvAll.lon<0
dfvvAll.lon[mask] = dfvvAll.loc[mask].lon + 360
print('after processing, the minimum longitude is %f4.3 and maximum is %f4.3' % (dfvvAll.lon.min(),dfvvAll.lon.max()) )
dfvvAll.describe()
before processing, the minimum longitude is0.0000004.3 and maximum is 360.0000004.3
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/pandas/core/generic.py:4695: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
self._update_inplace(new_data)
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2881: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
exec(code_obj, self.user_global_ns, self.user_ns)
after processing, the minimum longitude is 0.0000004.3 and maximum is 360.0000004.3
Out[49]:
id
lat
lon
temp
ve
vn
spd
var_lat
var_lon
var_tmp
count
2.147732e+07
2.131997e+07
2.131997e+07
1.986179e+07
2.129142e+07
2.129142e+07
2.129142e+07
2.147732e+07
2.147732e+07
2.147732e+07
mean
1.765662e+06
-2.263128e+00
2.124412e+02
1.986121e+01
2.454172e-01
4.708192e-01
2.613427e+01
7.326258e+00
7.326555e+00
7.522298e+01
std
9.452835e+06
3.401115e+01
9.746941e+01
8.339498e+00
2.525050e+01
2.052160e+01
1.939087e+01
8.527853e+01
8.527851e+01
2.637454e+02
min
2.578000e+03
-7.764700e+01
0.000000e+00
-1.685000e+01
-2.916220e+02
-2.601400e+02
0.000000e+00
5.268300e-07
-3.941600e-02
1.001300e-03
25%
4.897500e+04
-3.186000e+01
1.490720e+02
1.437300e+01
-1.411400e+01
-1.044700e+01
1.290300e+01
4.366500e-06
7.512600e-06
1.435700e-03
50%
7.141300e+04
-4.920000e+00
2.153940e+02
2.214400e+01
-5.560000e-01
1.970000e-01
2.176700e+01
8.833600e-06
1.495800e-05
1.691700e-03
75%
1.094330e+05
2.756000e+01
3.064370e+02
2.688900e+01
1.356100e+01
1.109300e+01
3.405900e+01
1.833300e-05
3.627900e-05
2.294200e-03
max
6.399288e+07
8.989900e+01
3.600000e+02
4.595000e+01
4.417070e+02
2.783220e+02
4.421750e+02
1.000000e+03
1.000000e+03
1.000000e+03
In [50]:
# Select only the arabian sea region
arabian_sea = (dfvvAll.lon > 45) & (dfvvAll.lon< 75) & (dfvvAll.lat> 5) & (dfvvAll.lat <28)
# arabian_sea = {'lon': slice(45,75), 'lat': slice(5,28)} # later use this longitude and latitude
floatsAll = dfvvAll.loc[arabian_sea] # directly use mask
print('dfvvAll.shape is %s, floatsAll.shape is %s' % (dfvvAll.shape, floatsAll.shape) )
dfvvAll.shape is (21477317, 11), floatsAll.shape is (111894, 11)
In [51]:
# visualize the float around global region
fig, ax = plt.subplots(figsize=(12,10))
dfvvAll.plot(kind='scatter', x='lon', y='lat', c='temp', cmap='RdBu_r', edgecolor='none', ax=ax)
# visualize the float around the arabian sea region
fig, ax = plt.subplots(figsize=(12,10))
floatsAll.plot(kind='scatter', x='lon', y='lat', c='temp', cmap='RdBu_r', edgecolor='none', ax=ax)
Out[51]:
<matplotlib.axes._subplots.AxesSubplot at 0x11e2cceb8>
In [52]:
# pands dataframe cannot do the resamplingn properly
# cause we are really indexing on ['time','id'], pandas.dataframe.resample cannot do this
# TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'MultiIndex'
print()
In [53]:
# dump the surface floater data from pandas.dataframe to xarray.dataset
floatsDSAll = xr.Dataset.from_dataframe(floatsAll.set_index(['time','id']) ) # set time & id as the index); use reset_index to revert this operation
floatsDSAll
Out[53]:
<xarray.Dataset>
Dimensions: (id: 259, time: 17499)
Coordinates:
* time (time) datetime64[ns] 2002-07-04 2002-07-04T06:00:00 ...
* id (id) int64 7574 10206 10208 11089 15703 15707 27069 27139 28842 ...
Data variables:
lat (time, id) float64 nan 16.3 14.03 16.4 14.04 nan 20.11 nan ...
lon (time, id) float64 nan 66.23 69.48 64.58 69.51 nan 68.55 nan ...
temp (time, id) float64 nan nan nan 28.0 28.53 nan 28.93 nan 27.81 ...
ve (time, id) float64 nan 8.68 5.978 6.286 4.844 nan 32.9 nan ...
vn (time, id) float64 nan -13.18 -18.05 -7.791 -17.47 nan 15.81 ...
spd (time, id) float64 nan 15.78 19.02 10.01 18.13 nan 36.51 nan ...
var_lat (time, id) float64 nan 0.0002661 5.01e-05 5.018e-05 5.024e-05 ...
var_lon (time, id) float64 nan 0.0006854 8.851e-05 9.018e-05 8.968e-05 ...
var_tmp (time, id) float64 nan 1e+03 1e+03 0.003733 0.0667 nan 0.001683 ...
In [54]:
floatsDSAll.dims
Out[54]:
Frozen(SortedKeysDict({'time': 17499, 'id': 259}))
In [55]:
# resample on the xarray.dataset onto two-day frequency
floatsDSAll_9D =floatsDSAll.resample('9D', dim='time')
print(floatsDSAll_9D.dims) # downsampling on the 'time' dimension 17499/9/4= around 486
floatsDSAll_9D
Frozen(SortedKeysDict(OrderedDict([('id', 259), ('time', 568)])))
Out[55]:
<xarray.Dataset>
Dimensions: (id: 259, time: 568)
Coordinates:
* id (id) int64 7574 10206 10208 11089 15703 15707 27069 27139 28842 ...
* time (time) datetime64[ns] 2002-07-04 2002-07-13 2002-07-22 ...
Data variables:
var_lat (time, id) float64 nan 0.001326 6.127e-05 6.456e-05 5.404e-05 ...
vn (time, id) float64 nan 0.02706 -8.271 -14.36 -7.037 nan -1.798 ...
temp (time, id) float64 nan nan nan 27.8 28.57 nan 28.98 nan 27.62 ...
lon (time, id) float64 nan 66.53 69.91 65.04 69.92 nan 69.45 nan ...
lat (time, id) float64 nan 16.22 13.6 16.07 13.64 nan 20.08 nan ...
var_tmp (time, id) float64 nan 1e+03 1e+03 0.00362 0.08884 nan 0.001708 ...
var_lon (time, id) float64 nan 0.005002 0.0001159 0.000123 9.882e-05 ...
ve (time, id) float64 nan 7.056 12.94 11.14 11.71 nan 24.13 nan ...
spd (time, id) float64 nan 8.463 17.92 20.22 16.84 nan 25.5 nan ...
In [56]:
# transfer it back to pandas.dataframe for plotting
floatsDFAll_9D = floatsDSAll_9D.to_dataframe()
floatsDFAll_9D
floatsDFAll_9D = floatsDFAll_9D.reset_index()
floatsDFAll_9D
# visualize the subsamping of floats around arabian region
fig, ax = plt.subplots(figsize=(12,10))
floatsDFAll_9D.plot(kind='scatter', x='lon', y='lat', c='temp', cmap='RdBu_r', edgecolor='none', ax=ax)
Out[56]:
<matplotlib.axes._subplots.AxesSubplot at 0x183beb550>
In [57]:
# get the value for the chllorophy for each data entry
floatsDFAll_9Dtimeorder = floatsDFAll_9D.sort_values(['time','id'],ascending=True)
floatsDFAll_9Dtimeorder # check whether it is time ordered!!
# should we drop nan to speed up??
Out[57]:
id
time
var_lat
vn
temp
lon
lat
var_tmp
var_lon
ve
spd
0
7574
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
568
10206
2002-07-04
0.001326
0.027056
NaN
66.533639
16.219000
1000.000000
0.005002
7.056056
8.462583
1136
10208
2002-07-04
0.000061
-8.270944
NaN
69.914139
13.599500
1000.000000
0.000116
12.942722
17.918639
1704
11089
2002-07-04
0.000065
-14.363611
27.796889
65.036972
16.068778
0.003620
0.000123
11.143806
20.217250
2272
15703
2002-07-04
0.000054
-7.037222
28.572750
69.915889
13.637028
0.088844
0.000099
11.706389
16.841889
2840
15707
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
3408
27069
2002-07-04
0.000056
-1.797889
28.981389
69.445889
20.077750
0.001708
0.000102
24.130583
25.498500
3976
27139
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
4544
28842
2002-07-04
0.000099
-8.122111
27.620472
60.830556
18.624861
0.003344
0.000208
5.026861
18.067944
5112
34159
2002-07-04
0.000059
16.704556
NaN
59.736889
12.894139
1000.000000
0.000112
31.753083
37.039111
5680
34173
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
6248
34210
2002-07-04
0.000063
-17.727861
26.452806
56.763194
6.022361
0.003696
0.000124
-3.750167
26.752611
6816
34211
2002-07-04
0.000055
-14.241500
28.380222
68.565389
8.210750
0.003512
0.000102
23.260944
28.413083
7384
34212
2002-07-04
0.000055
7.009000
28.577889
65.946111
6.679556
0.003549
0.000100
42.864500
48.849444
7952
34223
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
8520
34310
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
9088
34311
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
9656
34312
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
10224
34314
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
10792
34315
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
11360
34374
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
11928
34708
2002-07-04
0.000057
2.088833
27.291500
60.658278
10.225111
0.001784
0.000109
30.589861
30.958611
12496
34709
2002-07-04
0.000094
47.817000
26.934000
52.974000
5.027000
0.002233
0.000189
-84.267000
96.889000
13064
34710
2002-07-04
0.000051
-8.140111
31.149000
50.333750
13.057111
0.001854
0.000097
0.763722
47.400444
13632
34714
2002-07-04
0.000059
4.682833
27.765694
64.694000
13.741167
0.001799
0.000111
35.870556
37.025528
14200
34716
2002-07-04
0.000057
2.213806
28.780944
66.285250
7.716750
0.001785
0.000106
34.058694
37.381000
14768
34718
2002-07-04
0.000055
-33.327528
29.063444
72.973500
15.458417
0.001695
0.000103
20.389944
39.619167
15336
34719
2002-07-04
0.000057
-20.858056
28.959417
71.378167
17.217028
0.001652
0.000107
13.919778
27.380083
15904
34720
2002-07-04
0.000060
-19.437778
28.664167
69.482472
14.142056
0.001771
0.000111
11.141528
24.609778
16472
34721
2002-07-04
0.000060
-10.328972
27.908083
65.552333
16.928722
0.001733
0.000113
5.753667
13.113972
...
...
...
...
...
...
...
...
...
...
...
...
130639
3098682
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
131207
60073460
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
131775
60074440
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
132343
60077450
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
132911
60150420
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
133479
60454500
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
134047
60656200
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
134615
60657200
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
135183
60658190
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
135751
60659110
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
136319
60659120
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
136887
60659190
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
137455
60659200
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
138023
60940960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
138591
60940970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
139159
60941960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
139727
60941970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
140295
60942960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
140863
60942970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
141431
60943960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
141999
60943970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
142567
60944960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
143135
60944970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
143703
60945970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
144271
60946960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
144839
60947960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
145407
60947970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
145975
60948960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
146543
60950430
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
147111
62321420
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
147112 rows × 11 columns
In [58]:
'''
<xarray.Dataset>
Dimensions: (id: 259, time: 568) # 259*568
'''
floatsDFAll_9Dtimeorder.lon.shape
Out[58]:
(147112,)
In [59]:
# can we try to fit the missing value at thisstep
floatsDFAll_9Dtimeorder.lon.dropna().shape # the longitude data has lots of values (3466,)
Out[59]:
(3466,)
In [60]:
# interpolation starts from here
In [61]:
floatsDFAll_9Dtimeorder
Out[61]:
id
time
var_lat
vn
temp
lon
lat
var_tmp
var_lon
ve
spd
0
7574
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
568
10206
2002-07-04
0.001326
0.027056
NaN
66.533639
16.219000
1000.000000
0.005002
7.056056
8.462583
1136
10208
2002-07-04
0.000061
-8.270944
NaN
69.914139
13.599500
1000.000000
0.000116
12.942722
17.918639
1704
11089
2002-07-04
0.000065
-14.363611
27.796889
65.036972
16.068778
0.003620
0.000123
11.143806
20.217250
2272
15703
2002-07-04
0.000054
-7.037222
28.572750
69.915889
13.637028
0.088844
0.000099
11.706389
16.841889
2840
15707
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
3408
27069
2002-07-04
0.000056
-1.797889
28.981389
69.445889
20.077750
0.001708
0.000102
24.130583
25.498500
3976
27139
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
4544
28842
2002-07-04
0.000099
-8.122111
27.620472
60.830556
18.624861
0.003344
0.000208
5.026861
18.067944
5112
34159
2002-07-04
0.000059
16.704556
NaN
59.736889
12.894139
1000.000000
0.000112
31.753083
37.039111
5680
34173
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
6248
34210
2002-07-04
0.000063
-17.727861
26.452806
56.763194
6.022361
0.003696
0.000124
-3.750167
26.752611
6816
34211
2002-07-04
0.000055
-14.241500
28.380222
68.565389
8.210750
0.003512
0.000102
23.260944
28.413083
7384
34212
2002-07-04
0.000055
7.009000
28.577889
65.946111
6.679556
0.003549
0.000100
42.864500
48.849444
7952
34223
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
8520
34310
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
9088
34311
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
9656
34312
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
10224
34314
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
10792
34315
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
11360
34374
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
11928
34708
2002-07-04
0.000057
2.088833
27.291500
60.658278
10.225111
0.001784
0.000109
30.589861
30.958611
12496
34709
2002-07-04
0.000094
47.817000
26.934000
52.974000
5.027000
0.002233
0.000189
-84.267000
96.889000
13064
34710
2002-07-04
0.000051
-8.140111
31.149000
50.333750
13.057111
0.001854
0.000097
0.763722
47.400444
13632
34714
2002-07-04
0.000059
4.682833
27.765694
64.694000
13.741167
0.001799
0.000111
35.870556
37.025528
14200
34716
2002-07-04
0.000057
2.213806
28.780944
66.285250
7.716750
0.001785
0.000106
34.058694
37.381000
14768
34718
2002-07-04
0.000055
-33.327528
29.063444
72.973500
15.458417
0.001695
0.000103
20.389944
39.619167
15336
34719
2002-07-04
0.000057
-20.858056
28.959417
71.378167
17.217028
0.001652
0.000107
13.919778
27.380083
15904
34720
2002-07-04
0.000060
-19.437778
28.664167
69.482472
14.142056
0.001771
0.000111
11.141528
24.609778
16472
34721
2002-07-04
0.000060
-10.328972
27.908083
65.552333
16.928722
0.001733
0.000113
5.753667
13.113972
...
...
...
...
...
...
...
...
...
...
...
...
130639
3098682
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
131207
60073460
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
131775
60074440
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
132343
60077450
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
132911
60150420
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
133479
60454500
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
134047
60656200
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
134615
60657200
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
135183
60658190
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
135751
60659110
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
136319
60659120
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
136887
60659190
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
137455
60659200
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
138023
60940960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
138591
60940970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
139159
60941960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
139727
60941970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
140295
60942960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
140863
60942970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
141431
60943960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
141999
60943970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
142567
60944960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
143135
60944970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
143703
60945970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
144271
60946960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
144839
60947960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
145407
60947970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
145975
60948960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
146543
60950430
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
147111
62321420
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
147112 rows × 11 columns
In [62]:
# has to do task 1 to understand the float data better!!!!!
# task 1: Look into the floatsDFAll_9Dtimeorder data in more details
# check the nan counts in each id
# plot the trajectory of {time, lat, lon, temperature,} for each float id,
# this steps helps to understand the float dataset and if there is a need, improve it.
# task 2: take the float data as it is, and do the interpolation, whenever there is a nan value use the nearest thing....
# check whether the quality of interpolataion is improved, if not, then have to fall back to to task 1
# task 3: vectorization
In [ ]:
In [63]:
# floatsDFAll_9Dtimeorder.set_index(['id','time'])
# the inverse operation # floatsDFAll_9Dtimeorder.reset_index()
In [64]:
# DataFrame panel data
# look into the data
floatsDFAll_9Dtimeorder[100:105]
# so far there is no need to convert it into a panel
# floatsDFAll_9DtimeorderPanel = floatsDFAll_9Dtimeorder.to_panel
Out[64]:
id
time
var_lat
vn
temp
lon
lat
var_tmp
var_lon
ve
spd
56800
62558
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
57368
63036
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
57936
63067
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
58504
63068
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
59072
63069
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
In [65]:
# plot the temperature for one float, the temperature do have a trend
maskid = (floatsDFAll_9Dtimeorder.id == 63069) & (floatsDFAll_9Dtimeorder.time> '2007-01-01') & (floatsDFAll_9Dtimeorder.time< '2009-01-01')
floatsDFAll_9Dtimeorder[maskid]
floatsDFAll_9Dtimeorder[maskid].plot(x='time', y ='temp')
Out[65]:
<matplotlib.axes._subplots.AxesSubplot at 0x11e1524a8>
In [66]:
# this is a float that explains the need for temperature data
maskid2 = floatsDFAll_9Dtimeorder.id == 10208
floatsDFAll_9Dtimeorder[maskid2].head()
Out[66]:
id
time
var_lat
vn
temp
lon
lat
var_tmp
var_lon
ve
spd
1136
10208
2002-07-04
0.000061
-8.270944
NaN
69.914139
13.599500
1000.0
0.000116
12.942722
17.918639
1137
10208
2002-07-13
0.000060
-5.433167
NaN
70.726278
13.412417
1000.0
0.000113
8.819778
12.927639
1138
10208
2002-07-22
0.000066
-21.372750
NaN
71.035056
12.617778
1000.0
0.000127
-1.535306
25.644722
1139
10208
2002-07-31
0.000069
-31.578528
NaN
70.913944
10.364833
1000.0
0.000135
1.551611
32.618917
1140
10208
2002-08-09
0.000065
-7.338472
NaN
71.453528
9.144639
1000.0
0.000125
12.855500
15.766083
In [67]:
list(floatsDFAll_9Dtimeorder.time)[:5]
Out[67]:
[Timestamp('2002-07-04 00:00:00'),
Timestamp('2002-07-04 00:00:00'),
Timestamp('2002-07-04 00:00:00'),
Timestamp('2002-07-04 00:00:00'),
Timestamp('2002-07-04 00:00:00')]
In [68]:
floatsDFAll_9Dtimeorder.time[:5]
Out[68]:
0 2002-07-04
568 2002-07-04
1136 2002-07-04
1704 2002-07-04
2272 2002-07-04
Name: time, dtype: datetime64[ns]
In [69]:
ds_9day
Out[69]:
<xarray.Dataset>
Dimensions: (eightbitcolor: 256, lat: 276, lon: 360, rgb: 3, time: 589)
Coordinates:
* lat (lat) float64 27.96 27.87 27.79 27.71 27.62 27.54 27.46 ...
* lon (lon) float64 45.04 45.13 45.21 45.29 45.38 45.46 45.54 ...
* rgb (rgb) int64 0 1 2
* eightbitcolor (eightbitcolor) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 ...
* time (time) datetime64[ns] 2002-07-04 2002-07-13 2002-07-22 ...
Data variables:
chlor_a (time, lat, lon) float64 nan nan nan nan nan nan nan nan ...
palette (time, rgb, eightbitcolor) float64 -109.0 0.0 108.0 ...
In [434]:
a = [0,1,2,3,4,5,5,5,5]
a[5] = 4
for i in range(0,len(a)):
if a[i] == 5:
a[i]=4
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-434-4068f1624312> in <module>()
4 if a[i] == 5:
5 a[i]=4
----> 6 a[1,2,3,4]
TypeError: list indices must be integers or slices, not tuple
In [70]:
idx_time = ds_9day.indexes['time']
idx_lat = ds_9day.indexes['lat']
idx_lon = ds_9day.indexes['lon']
### 'time is from small to big number'
print(ds_9day.indexes['time'])
print(type(ds_9day.indexes['time']))
### 'lat is from *big* to small number'
print(ds_9day.indexes['lat'])
print(type(ds_9day.indexes['lat']))
### 'lon is from small to big number'
print(ds_9day.indexes['lon'])
print(type(ds_9day.indexes['lon']))
# validation: to locate the chl-a for this point {time:2002-07-13, lat:27.7916660309, lon:45.2916679382}
DatetimeIndex(['2002-07-04', '2002-07-13', '2002-07-22', '2002-07-31',
'2002-08-09', '2002-08-18', '2002-08-27', '2002-09-05',
'2002-09-14', '2002-09-23',
...
'2016-10-09', '2016-10-18', '2016-10-27', '2016-11-05',
'2016-11-14', '2016-11-23', '2016-12-02', '2016-12-11',
'2016-12-20', '2016-12-29'],
dtype='datetime64[ns]', name='time', length=589, freq='9D')
<class 'pandas.tseries.index.DatetimeIndex'>
Float64Index([27.9583339691, 27.8749980927, 27.7916660309, 27.7083339691,
27.6249980927, 27.5416660309, 27.4583339691, 27.3749980927,
27.2916660309, 27.2083339691,
...
5.79166173935, 5.70833349228, 5.62499761581, 5.54166173935,
5.45833349228, 5.37499761581, 5.29166173935, 5.20833349228,
5.12499761581, 5.04166173935],
dtype='float64', name='lat', length=276)
<class 'pandas.indexes.numeric.Float64Index'>
Float64Index([45.0416679382, 45.1250114441, 45.2083396912, 45.2916679382,
45.3750114441, 45.4583396912, 45.5416679382, 45.6250114441,
45.7083396912, 45.7916679382,
...
74.2083358765, 74.2916641235, 74.3750076294, 74.4583358765,
74.5416641235, 74.6250076294, 74.7083358765, 74.7916641235,
74.8750076294, 74.9583358765],
dtype='float64', name='lon', length=360)
<class 'pandas.indexes.numeric.Float64Index'>
In [ ]:
# do the interpolation for one point above
In [ ]:
#for row in floatsDFAll_9Dtimeorder.itertuples():
#print('row.time = %s, row.id=%d, row.lon=%4.3f, row.lat=%4.3f' % (row.time,row.id,row.lon,row.lat) )
# now just focus on this entry:
# row.time = 2002-07-04 00:00:00, row.id=10206, row.lon=66.534, row.lat=16.219, do interpolation
In [73]:
# simulate the api for one entry in the float data
# generate test points
# test case 1: a single entry (southeast corner for valid values)
row_case1 = pd.DataFrame(data = {'time':'2002-07-13 00:00:00', 'id': 10206, 'lon':74.7083358765, 'lat':5.20833349228},index=[1])
# test case 2: a list of entries, API for DS
row_case2 = pd.DataFrame(data = {'time':['2002-07-13 00:00:00','2002-07-22 00:00:00'] , 'id': [10206, 10206], 'lon':[74.7083358765, 74.6250076294], 'lat':[5.20833349228, 5.29166173935]},index=[2,3])
print(row_case1.time)
print('-----')
print('row_case1', row_case1)
print('row_case2', row_case2)
print('-----')
print(type(row_case1.time))
print('-----')
print(pd.DatetimeIndex(row_case1.time))
#idx_time.get_loc
1 2002-07-13 00:00:00
Name: time, dtype: object
-----
row_case1 id lat lon time
1 10206 5.208333 74.708336 2002-07-13 00:00:00
row_case2 id lat lon time
2 10206 5.208333 74.708336 2002-07-13 00:00:00
3 10206 5.291662 74.625008 2002-07-22 00:00:00
-----
<class 'pandas.core.series.Series'>
-----
DatetimeIndex(['2002-07-13'], dtype='datetime64[ns]', name='time', freq=None)
In [74]:
### np.datetime64 support arithmics on date
### an example
dt64 = np.datetime64('2012-05-01')
dt64
dt65 = np.datetime64('2013-02-02')
dt65
difference = dt65 - dt64
#difference.day
print(type(difference))
print(difference)
# in different units
print('difference in days:', difference / np.timedelta64(1, 'D') )
print('difference in hours:', difference / np.timedelta64(1, 'h') )
print('difference in mintes:', difference / np.timedelta64(1, 'm') )
print('difference in second:', difference / np.timedelta64(1, 's') )
print('difference in millisecond:', difference / np.timedelta64(1, 'ms') )
<class 'numpy.timedelta64'>
277 days
difference in days: 277.0
difference in hours: 6648.0
difference in mintes: 398880.0
difference in second: 23932800.0
difference in millisecond: 23932800000.0
In [75]:
# delete the comment:
# find the nearest idices(values) alaong each direction,
# find the next indices(values) along each direction,
# find the delta along each direction,
# carry out the multipliear interpolations
In [76]:
################
# test case 1: take a single entry (southeast corner for valid values)
row_case1 = pd.DataFrame(data = {'time':'2002-07-13 00:00:00', 'id': 10206, 'lon':74.7083358765, 'lat':5.20833349228},index=[1])
print(row_case1)
id lat lon time
1 10206 5.208333 74.708336 2002-07-13 00:00:00
In [77]:
### interpolation on the time dimension
xtime_test = np.datetime64(row_case1.time.values[0]) # for delta
itime_nearest = idx_time.get_loc(xtime_test, method='nearest')
# itime_nearest # 1
xtime_nearest = ds_9day.time[itime_nearest].values # 2002-07-13T00:00:00.000000000
print('xtime_nearest', xtime_nearest)
print('xtime_nearest', type(xtime_nearest)) # xtime_nearest <class 'numpy.datetime64'>
# the time distance in days
delta_xtime = (xtime_test - xtime_nearest) / np.timedelta64(1, 'D')
print('delta_xtime in days', delta_xtime)
if delta_xtime >=0: # the nearest index is on the left
itime_next = itime_nearest + 1 # increase the location of the index (or coordinates)
else: # the nearest index is on the right
itime_next = itime_nearest -1
# find the next coordinate values
xtime_next = ds_9day.time[itime_next].values
print('xtime_next', xtime_next)
# prepare for the Tri-linear interpolation
base_time = (xtime_next - xtime_nearest) / np.timedelta64(1, 'D')
print('base_time', base_time)
w_time = delta_xtime / base_time
print('w_time', w_time) # 0.0
xtime_nearest 2002-07-13T00:00:00.000000000
xtime_nearest <class 'numpy.datetime64'>
delta_xtime in days 0.0
xtime_next 2002-07-22T00:00:00.000000000
base_time 9.0
w_time 0.0
In [78]:
### interpolation on the lat dimension
xlat_test = row_case1.lat.values[0] + 0.06 # base 5.20833349228 # cell distance around .8, use .2 & .6 as two tests
ilat_nearest = idx_lat.get_loc(xlat_test, method='nearest')
print(ilat_nearest) # 272
xlat_nearest = ds_9day.lat[ilat_nearest].values
print('xlat_nearest', xlat_nearest)
delta_xlat = xlat_test - xlat_nearest
print("delta_xlat",delta_xlat)
if delta_xlat >=0 : # the nearest index is on the right; but order of the latitude is different, it is descending
ilat_next = ilat_nearest-1 # caution about the order, it is an inverse order
else:
ilat_next = ilat_nearest+1
# find the next coordinates value
print(ilat_next) # 273
xlat_next = ds_9day.lat[ilat_next].values
print('xlat_next', xlat_next)
# prepare for the Tri-linear interpolation
w_lat = delta_xlat / (xlat_next - xlat_nearest)
print('w_lat', w_lat) # 0.279956051994
272
xlat_nearest 5.291661739349365
delta_xlat -0.0233282470694
273
xlat_next 5.208333492279053
w_lat 0.279956051994
In [79]:
### interpolation on the lon dimension
xlon_test = row_case1.lon.values[0] +0.06 # base 74.7083358765 # cell distance around .8, use .2 & .6 as two tests
ilon_nearest = idx_lon.get_loc(xlon_test, method = 'nearest')
print(ilon_nearest) # 357
xlon_nearest = ds_9day.lon[ilon_nearest].values
print('xlon_nearest', xlon_nearest)
delta_xlon = xlon_test - xlon_nearest
print("delta_xlat", delta_xlon)
if delta_xlon >=0: # the nearest index is on the left
ilon_next = ilon_nearest +1 # increase the location of the index (or coordinates)
else:
ilon_next = ilon_nearest -1
print(ilon_next) # 356
# find the next coordinate values
xlon_next = ds_9day.lon[ilon_next].values
print("xlon_next", xlon_next)
# prepare for the Tri-linear interpolation
w_lon = delta_xlon / (xlon_next - xlon_nearest)
print("w_lon", w_lon) # 0.279956051583
357
xlon_nearest 74.79166412353516
delta_xlat -0.0233282470352
356
xlon_next 74.70833587646484
w_lon 0.279956051583
In [80]:
'''
### Pseudocode for Trilinear interpolation
# 1D
w = delta_x / (x_next -x_nearest)
f = (1-w)*da.isel(x=ix_nearest).values + w* da.isel(x=ix_next).values
# 2D
wx = delta_x / (x_next - x_nearest)
wy = delta_y / (y_next - y_nearest)
f = (1-wx)*(1-wy)*da.isel(x=ix_nearest, y=iy_nearest).values +
(1-wx)*wy*da.isel(x=ix_nearest,y=iy_next).values +
wx*(1-wy)*da.isel(x=ix_next,y=iy_nearest).values +
wx*wy*da.isel(x=ix_next,y=iy_next).values
# 3D
wx = delta_x / (x_next - x_nearest)
wy = delta_y / (y_next - y_nearest)
wz = delta_z / (z_next - z_nearest)
f = (1-wx)*(1-wy)*(1-wz)*da.isel(x=ix_nearest, y=iy_nearest, z=iz_nearest).values +
(1-wx)*(1-wy)*wz*da.isel(x=ix_nearest, y=iy_nearest, z=iz_next).values +
(1-wx)*wy*(1-wz)*da.isel(x=ix_nearest, y=iy_next, z=iz_nearest).values +
(1-wx)*wy*wz*da.isel(x=ix_nearest, y=iy_next, z=iz_next).values +
wx*(1-wy)*(1-wz)*da.isel(x=ix_next, y=iy_nearest, z=iz_nearest).values +
wx*(1-wy)*wz*da.isel(x=ix_next, y=iy_nearest, z=iz_next).values +
wx*wy*(1-wz)*da.isel(x=ix_next, y=iy_next, z=iz_nearest).values +
wx*wy*wz*da.isel(x=ix_next, y=iy_next, z=iz_next).values
'''
'''
### interpolate the Trilinear Value
dimensions 3 : time, lat, lon
locations: xtime_test, xlat_test, xlon_test
weights(from the nearest coordinates): w_time, w_lat, w_lon
output: a singel value based on 1D interpolation on each dimension, so use 8-neighbours
'''
# validate this
# try two pints as a list to check the interface
#ds_9day.chlor_a.sel_points(time=list(floatsDFAll_9Dtimeorder.time),lon=list(floatsDFAll_9Dtimeorder.lon), lat=list(floatsDFAll_9Dtimeorder.lat), method='nearest')
# preprocessing for nan values for each dimension
# dim:time
#print(ds_9day.chlor_a.isel_points(time=list([itime_nearest]), lat=list([ilat_nearest]), lon=list([ilon_nearest])).values)
# caution: nan values, store as "list_of_array to 2d_array" first, then sum
tmp = np.array([
ds_9day.chlor_a.isel_points(time=list([itime_nearest]), lat=list([ilat_nearest]), lon=list([ilon_nearest])).values,
ds_9day.chlor_a.isel_points(time=list([itime_nearest]), lat=list([ilat_nearest]), lon=list([ilon_next])).values,
ds_9day.chlor_a.isel_points(time=list([itime_nearest]), lat=list([ilat_next]), lon=list([ilon_nearest])).values,
ds_9day.chlor_a.isel_points(time=list([itime_nearest]), lat=list([ilat_next]), lon=list([ilon_next])).values,
ds_9day.chlor_a.isel_points(time=list([itime_next]), lat=list([ilat_nearest]), lon=list([ilon_nearest])).values,
ds_9day.chlor_a.isel_points(time=list([itime_next]), lat=list([ilat_nearest]), lon=list([ilon_next])).values,
ds_9day.chlor_a.isel_points(time=list([itime_next]), lat=list([ilat_next]), lon=list([ilon_nearest])).values,
ds_9day.chlor_a.isel_points(time=list([itime_next]), lat=list([ilat_next]), lon=list([ilon_next])).values ])
weights = np.array([(1-w_time)*(1-w_lat)*(1-w_lon),
(1-w_time)*(1-w_lat)*w_lon,
(1-w_time)*w_lat*(1-w_lon),
(1-w_time)*w_lat*w_lon,
w_time*(1-w_lat)*(1-w_lon),
w_time*(1-w_lat)*w_lon,
w_time*w_lat*(1-w_lon),
w_time*w_lat*w_lon ])
# how to deal with "nan" values, fill in missing values for the np.array tmpAll
# or fill the mean values to the unweighted array
# http://stackoverflow.com/questions/18689235/numpy-array-replace-nan-values-with-average-of-columns
print('\n neighbouring tensor used', tmp)
# column min: (nan+0.245878 + nan + nan + 0.19680101 + nan + nan + 0.18532801)/8 = 0.20933567333
col_mean = np.nanmean(tmp, axis=0)
print('its mean along axis 0(column) \n', col_mean)
# filling the missing values.
inds = np.where(np.isnan(tmp))
print('\n nan index\n', inds)
tmp[inds]=np.take(col_mean, inds[1])
print('\n values after the fill \n', tmp)
print('\n weighting tensor used \n', weights)
print('\n tensor product\n', np.dot(weights, tmp)) # [ 0.2167019]
# validate by 1D array
# val = np.array([0.20933567, 0.245878, 0.20933567,
# 0.20933567, 0.19680101, 0.20933567,
# 0.20933567,0.18532801])
# np.dot(val, weights) # 0.21670189702309739
neighbouring tensor used [[ nan]
[ 0.245878 ]
[ nan]
[ nan]
[ 0.19680101]
[ nan]
[ nan]
[ 0.18532801]]
its mean along axis 0(column)
[ 0.20933567]
nan index
(array([0, 2, 3, 5, 6]), array([0, 0, 0, 0, 0]))
values after the fill
[[ 0.20933567]
[ 0.245878 ]
[ 0.20933567]
[ 0.20933567]
[ 0.19680101]
[ 0.20933567]
[ 0.20933567]
[ 0.18532801]]
weighting tensor used
[ 0.51846329 0.20158066 0.20158066 0.07837539 0. 0. 0.
0. ]
tensor product
[ 0.2167019]
In [81]:
ilon_nearest
Out[81]:
357
In [263]:
################
# test case 2
# take a {time-list, id-list, lon-list, lat-list}, index-list
# carry out the interpolation
#row_case2 = pd.DataFrame(data = {'time':['2002-07-13 00:00:00','2002-07-22 00:00:00'] , 'id': [10206, 10206], 'lon':[74.7083358765, 74.6250076294], 'lat':[5.20833349228, 5.29166173935]},index=[2,3])
#print(row_case2)
################
# test case 3
row_case2 = pd.DataFrame(data = {'time':['2002-07-13 00:00:00', '2002-07-22 00:00:00', '2002-07-13 00:00:00'] , 'id': [10206, 10206, 10206], 'lon':[74.7083358765, 74.6250076294,74.7083358765], 'lat':[5.20833349228, 5.29166173935, 5.20833349228]},index=[1,2,3])
print(row_case2)
####
#interpolation on the time dimension
time_len = len(row_case2.time.values)
xtime_test = list([ np.datetime64(row_case2.time.values[i]) for i in range(0,time_len) ] ) # for delta
print('\n xtime_test \n', xtime_test)
'''caution: cannot do this inside the function get_loc,
see https://github.com/pandas-dev/pandas/issues/3488
'''
itime_nearest = [idx_time.get_loc(xtime_test[i], method='nearest') for i in range(0, time_len)]
print('\n itime_nearest \n', itime_nearest) # [1,2]
xtime_nearest = ds_9day.time[itime_nearest].values # ['2002-07-13T00:00:00.000000000' '2002-07-22T00:00:00.000000000']
print('\n xtime_nearest\n', xtime_nearest) # ['2002-07-13T00:00:00.000000000' '2002-07-22T00:00:00.000000000']
print('xtime_nearest', type(xtime_nearest)) # xtime_nearest <class 'numpy.ndarray'> # time_nearest <class 'numpy.datetime64'>
# the time distance in days
delta_xtime = (xtime_test - xtime_nearest) / np.timedelta64(1, 'D')
print('\n delta_xtime in days \n', delta_xtime)
print(type(delta_xtime))
itime_next = [itime_nearest[i]+1 if delta_xtime[i] >=0 else itime_nearest[i]-1 for i in range(0, time_len) ]
print('\n itime_next \n',itime_next) # [2, 3]
# find the next coordinate values
xtime_next = ds_9day.time[itime_next].values
print('\n xtime_next \n', xtime_next) # ['2002-07-22T00:00:00.000000000' '2002-07-31T00:00:00.000000000']
# prepare for the Tri-linear interpolation
base_time = (xtime_next - xtime_nearest) / np.timedelta64(1, 'D') # [ 9. 9.]
print('\n base_time \n', base_time)
w_time = delta_xtime / base_time
print('\n w_time \n', w_time) # [ 0. 0.]
####
#interpolation on the lat dimension
xlat_test = row_case2.lat.values + 0.06 # base [ 5.20833349 5.29166174] # cell distance around .8, use .2 & .6 as two tests
print('\n xlat_test \n', xlat_test) # xlat_test [ 5.26833349 5.35166174]
ilat_nearest = [idx_lat.get_loc(xlat_test[i], method='nearest') for i in range(0, time_len)]
print('\n ilat_nearest \n', ilat_nearest) # [272, 271]
xlat_nearest = ds_9day.lat[ilat_nearest].values
print('\n xlat_nearest \n', xlat_nearest) # [ 5.29166174 5.37499762]
delta_xlat = xlat_test - xlat_nearest
print("\n delta_xlat \n",delta_xlat) # [-0.02332825 -0.02333588]
# the nearest index is on the right; but order of the latitude is different, it is descending
ilat_next = [ilat_nearest[i]-1 if delta_xlat[i] >=0 else ilat_nearest[i]+1 for i in range(0, time_len) ]
print('\n ilat_next \n', ilat_next) # [273, 272]
# find the next coordinates value
xlat_next = ds_9day.lat[ilat_next].values
print('\n xlat_next \n', xlat_next) # [ 5.20833349 5.29166174]
# prepare for the Tri-linear interpolation
w_lat = delta_xlat / (xlat_next - xlat_nearest)
print('\n w_lat \n', w_lat) # [ 0.27995605 0.28002197]
####
#interpolation on the lon dimension
xlon_test = row_case2.lon.values +0.06 # base [74.7083358765, 74.6250076294] # cell distance around .8, use .2 & .6 as two tests
print('\n xlon_test \n', xlon_test) # [ 74.76833588 74.68500763]
ilon_nearest = [idx_lon.get_loc(xlon_test[i], method='nearest') for i in range(0, time_len)]
print('\n ilon_nearest \n', ilon_nearest) # [357, 356]
xlon_nearest = ds_9day.lon[ilon_nearest].values
print('\n xlon_nearest \n', xlon_nearest) # [ 74.79166412 74.70833588]
delta_xlon = xlon_test - xlon_nearest
print("\n delta_xlon \n", delta_xlon) # [-0.02332825 -0.02332825]
ilon_next = [ilon_nearest[i]+1 if delta_xlon[i] >=0 else ilon_nearest[i]-1 for i in range(0, time_len) ]
print('\n ilon_next \n',ilon_next) # [356, 355]
# find the next coordinate values
xlon_next = ds_9day.lon[ilon_next].values
print("\n xlon_next \n", xlon_next) # [ 74.70833588 74.62500763]
# prepare for the Tri-linear interpolation
w_lon = delta_xlon / (xlon_next - xlon_nearest)
print("\n w_lon \n", w_lon) # [ 0.27995605 0.27995605]
####
# local Tensor product for Trilinear interpolation
# caution: nan values, store as "list_of_array to 2d_array" first, then sum
# no casting to list needed here, inputs are already lists
tmp = np.array([
ds_9day.chlor_a.isel_points(time=itime_nearest, lat=ilat_nearest, lon=ilon_nearest).values,
ds_9day.chlor_a.isel_points(time=itime_nearest, lat=ilat_nearest, lon=ilon_next).values,
ds_9day.chlor_a.isel_points(time=itime_nearest, lat=ilat_next, lon=ilon_nearest).values,
ds_9day.chlor_a.isel_points(time=itime_nearest, lat=ilat_next, lon=ilon_next).values,
ds_9day.chlor_a.isel_points(time=itime_next, lat=ilat_nearest, lon=ilon_nearest).values,
ds_9day.chlor_a.isel_points(time=itime_next, lat=ilat_nearest, lon=ilon_next).values,
ds_9day.chlor_a.isel_points(time=itime_next, lat=ilat_next, lon=ilon_nearest).values,
ds_9day.chlor_a.isel_points(time=itime_next, lat=ilat_next, lon=ilon_next).values ])
weights = np.array([(1-w_time)*(1-w_lat)*(1-w_lon),
(1-w_time)*(1-w_lat)*w_lon,
(1-w_time)*w_lat*(1-w_lon),
(1-w_time)*w_lat*w_lon,
w_time*(1-w_lat)*(1-w_lon),
w_time*(1-w_lat)*w_lon,
w_time*w_lat*(1-w_lon),
w_time*w_lat*w_lon ])
# how to deal with "nan" values, fill in missing values for the np.array tmpAll
# or fill the mean values to the unweighted array
# http://stackoverflow.com/questions/18689235/numpy-array-replace-nan-values-with-average-of-columns
print('\n neighbouring tensor used \n', tmp)
'''
neighbouring tensor used
[[ nan 0.181841 ]
[ 0.245878 nan]
[ nan nan]
[ nan nan]
[ 0.19680101 nan]
[ nan nan]
[ nan nan]
[ 0.18532801 nan]]
'''
# column min: (nan+0.245878 + nan + nan + 0.19680101 + nan + nan + 0.18532801)/8 = 0.20933567333
col_mean = np.nanmean(tmp, axis=0)
print('\n its mean along axis 0(column) \n', col_mean) # [ 0.20933567 0.181841 ]
# filling the missing values.
inds = np.where(np.isnan(tmp))
print('\n nan index\n', inds)
tmp[inds]=np.take(col_mean, inds[1])
print('\n values after the fill \n', tmp)
print('\n weighting tensor used \n', weights)
print("weights.shape", weights.shape) # (8, 3)
print("tmp.shape", tmp.shape) # (8, 3)
nrow_w, ncol_w = weights.shape
nrow_t, ncol_t = tmp.shape
assert nrow_w == nrow_t, "the row count of weights and values are not the same!"
assert ncol_w == ncol_t, "the row count of weights and values are not the same!"
print('\n tensor product\n', np.dot(weights[:,0], tmp[:,0]) ) # 0.216701896135 should be [ 0.2167019]
# new interpolation process of the Chl_a
chl_new = np.empty(ncol_w)
for i in range(0, ncol_w, 1):
chl_new[i] = np.dot(weights[:,i], tmp[:,i])
print('chl_newInt', chl_new) # [ 0.2167019 0.181841 0.2167019]
# validate by 1D array
# val = np.array([0.20933567, 0.245878, 0.20933567,
# 0.20933567, 0.19680101, 0.20933567,
# 0.20933567,0.18532801])
# np.dot(val, weights) # 0.21670189702309739
## output xarray.dataarray of points, see examples below
# this is the way how xarray.Dataset works
# if you want a xarray.DataArray, first generate a xarray.Dataset, then select DataArray from there.
chl_newInt = xr.Dataset({'chl': (['id'], np.random.randn(3))},
coords={
'time':(['points'],['2002-07-13 00:00:00', '2002-07-22 00:00:00', '2002-07-13 00:00:00']) , \
'id': (['points'], [10206, 10206, 10206]),
'lon': (['points'], [74.7083358765, 74.6250076294,74.7083358765]),
'lat':(['points'], [5.20833349228, 5.29166173935, 5.20833349228]),
'points': (['points'], [0,1,2])}) # this way the dims is set to point
print('\n',chl_newInt.chl)
'''
### Task: output xarray.dataarray as points
## example 1
arr = xr.DataArray(np.random.rand(4,3), [('time', pd.date_range('2000-01-01', periods=4)), ('space', ['IA', 'IL', 'IN'])] )
print("first example",arr)
print("\n \n")
## example2 -- concrete xr.DataArray
data = np.random.rand(4, 3)
locs = ['IA', 'IL', 'IN']
times = pd.date_range('2000-01-01', periods=4)
brr = xr.DataArray(data, coords={'time': times, 'space': locs, 'const': 42, \
'ranking': ('space', [1, 2, 3])}, \
dims=['time', 'space'])
print("second example",brr)
'''
'''
### the target output
#the output generated by the xarray.DataSet => xarray.DataArray
<xarray.DataArray 'chlor_a' (points: 147112)>
array([ nan, nan, nan, ..., nan, nan, nan])
Coordinates:
time (points) datetime64[ns] 2002-07-04 2002-07-04 2002-07-04 ...
lon (points) float64 74.96 66.54 69.88 65.04 69.88 74.96 69.46 ...
lat (points) float64 27.96 16.21 13.62 16.04 13.62 27.96 20.04 ...
* points (points) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 ...
### the solution approach we tried to is: first, generate a xarray.DataSet
second, generte a xarray.DataArray from the xarray DataSet if needed.
# 1. looking at this example, DataArray seems to be working.
# works here
da_test1 = xr.DataArray(np.random.rand(3), dims=['x'],
coords={'x': np.array([10206, 10206, 10206]), 'y': 2} )
print('\n da_test1', da_test1)
##########
# but not works here
da_test1 = xr.DataArray(np.random.rand(3), dims=['x'],
coords={'x': np.array([10206, 10206, 10206]), 'y': np.array([10206, 10206, 10206])} )
print('\n da_test1', da_test1)
# err's
##########
##########
# works here
da_test1 = xr.DataArray(np.random.rand(3), dims=['x'],
coords={'x': np.array([10206, 10206, 10206]), 'y': 2} )
print('\n da_test1', da_test1)
# da_test1 <xarray.DataArray (x: 3)>
# array([ 0.90386212, 0.21516239, 0.44707272])
# Coordinates:
# * x (x) int64 10206 10206 10206
# y int64 2
##########
##########
# so we do this
# this is the way how xarray.Dataset works
# if you want a xarray.DataArray, first generate a xarray.Dataset, then select DataArray from there.
chl_newInt = xr.Dataset({'chl': (['id'], np.random.randn(3))},
coords={
'id': (['id'], np.array([10206, 10206, 10206])),
'lon': (['id'], np.array([74.7083358765, 74.6250076294,74.7083358765])),
'lat': (['id'], np.array([5.20833349228, 5.29166173935, 5.20833349228]))})
print('chl_newInt', chl_newInt) # generate the xarray.DataSet
print('\n \n')
print('chl_newInt.chl', chl_newInt.chl) # xarray.DataSet contains many xarray.DataArray
#chl_newInt <xarray.Dataset>
#Dimensions: (id: 3)
#Coordinates:
# lon (id) float64 74.71 74.63 74.71
# * id (id) int64 10206 10206 10206
# lat (id) float64 5.208 5.292 5.208
#Data variables:
# chl (id) float64 0.783 -0.9714 -0.3206
#chl_newInt.chl <xarray.DataArray 'chl' (id: 3)>
#array([ 0.78301614, -0.97144208, -0.3206447 ])
#Coordinates:
# lon (id) float64 74.71 74.63 74.71
# * id (id) int64 10206 10206 10206
# lat (id) float64 5.208 5.292 5.208
'''
print()
id lat lon time
1 10206 5.208333 74.708336 2002-07-13 00:00:00
2 10206 5.291662 74.625008 2002-07-22 00:00:00
3 10206 5.208333 74.708336 2002-07-13 00:00:00
xtime_test
[numpy.datetime64('2002-07-13T00:00:00'), numpy.datetime64('2002-07-22T00:00:00'), numpy.datetime64('2002-07-13T00:00:00')]
itime_nearest
[1, 2, 1]
xtime_nearest
['2002-07-13T00:00:00.000000000' '2002-07-22T00:00:00.000000000'
'2002-07-13T00:00:00.000000000']
xtime_nearest <class 'numpy.ndarray'>
delta_xtime in days
[ 0. 0. 0.]
<class 'numpy.ndarray'>
itime_next
[2, 3, 2]
xtime_next
['2002-07-22T00:00:00.000000000' '2002-07-31T00:00:00.000000000'
'2002-07-22T00:00:00.000000000']
base_time
[ 9. 9. 9.]
w_time
[ 0. 0. 0.]
xlat_test
[ 5.26833349 5.35166174 5.26833349]
ilat_nearest
[272, 271, 272]
xlat_nearest
[ 5.29166174 5.37499762 5.29166174]
delta_xlat
[-0.02332825 -0.02333588 -0.02332825]
ilat_next
[273, 272, 273]
xlat_next
[ 5.20833349 5.29166174 5.20833349]
w_lat
[ 0.27995605 0.28002197 0.27995605]
xlon_test
[ 74.76833588 74.68500763 74.76833588]
ilon_nearest
[357, 356, 357]
xlon_nearest
[ 74.79166412 74.70833588 74.79166412]
delta_xlon
[-0.02332825 -0.02332825 -0.02332825]
ilon_next
[356, 355, 356]
xlon_next
[ 74.70833588 74.62500763 74.70833588]
w_lon
[ 0.27995605 0.27995605 0.27995605]
neighbouring tensor used
[[ nan 0.181841 nan]
[ 0.245878 nan 0.245878 ]
[ nan nan nan]
[ nan nan nan]
[ 0.19680101 nan 0.19680101]
[ nan nan nan]
[ nan nan nan]
[ 0.18532801 nan 0.18532801]]
its mean along axis 0(column)
[ 0.20933567 0.181841 0.20933567]
nan index
(array([0, 0, 1, 2, 2, 2, 3, 3, 3, 4, 5, 5, 5, 6, 6, 6, 7]), array([0, 2, 1, 0, 1, 2, 0, 1, 2, 1, 0, 1, 2, 0, 1, 2, 1]))
values after the fill
[[ 0.20933567 0.181841 0.20933567]
[ 0.245878 0.181841 0.245878 ]
[ 0.20933567 0.181841 0.20933567]
[ 0.20933567 0.181841 0.20933567]
[ 0.19680101 0.181841 0.19680101]
[ 0.20933567 0.181841 0.20933567]
[ 0.20933567 0.181841 0.20933567]
[ 0.18532801 0.181841 0.18532801]]
weighting tensor used
[[ 0.51846329 0.51841582 0.51846329]
[ 0.20158066 0.20156221 0.20158066]
[ 0.20158066 0.20162813 0.20158066]
[ 0.07837539 0.07839385 0.07837539]
[ 0. 0. 0. ]
[ 0. 0. 0. ]
[ 0. 0. 0. ]
[ 0. 0. 0. ]]
weights.shape (8, 3)
tmp.shape (8, 3)
tensor product
0.216701896135
chl_newInt [ 0.2167019 0.181841 0.2167019]
<xarray.DataArray 'chl' (id: 3)>
array([-0.05494149, 1.75896415, -0.70221769])
In [256]:
#########
# an concrete example of how to generate xarray dataset and dataarray
# this is the way how xarray.Dataset works
# if you want a xarray.DataArray, first generate a xarray.Dataset, then select DataArray from there.
chl_newInt = xr.Dataset({'chl': (['id'], np.random.randn(3))},
coords={
'id': (['id'], np.array([10206, 10206, 10206])),
'lon': (['id'], np.array([74.7083358765, 74.6250076294,74.7083358765])),
'lat': (['id'], np.array([5.20833349228, 5.29166173935, 5.20833349228]))})
print('chl_newInt', chl_newInt)
print('\n \n')
print('chl_newInt.chl', chl_newInt.chl
chl_newInt <xarray.Dataset>
Dimensions: (id: 3)
Coordinates:
lon (id) float64 74.71 74.63 74.71
* id (id) int64 10206 10206 10206
lat (id) float64 5.208 5.292 5.208
Data variables:
chl (id) float64 0.783 -0.9714 -0.3206
chl_newInt.chl <xarray.DataArray 'chl' (id: 3)>
array([ 0.78301614, -0.97144208, -0.3206447 ])
Coordinates:
lon (id) float64 74.71 74.63 74.71
* id (id) int64 10206 10206 10206
lat (id) float64 5.208 5.292 5.208
In [258]:
### a few small examples.
da_test1 = xr.DataArray(np.random.rand(3), dims=['x'],
coords={'x': np.array([10206, 10206, 10206]), 'y': 2} )
print('\n da_test1', da_test1)
# ----
da_test2 = xr.DataArray(np.random.rand(3,1), dims=['x','y'],
coords={'x': np.arange(3), 'y': np.arange(1)})
print('\n da_test2', da_test2)
# ---
da_test3 = xr.DataArray(np.random.rand(3,4), dims=['y','x'],
coords={'y': np.arange(3), 'x': np.arange(4)})
da_test3
print("\n da_test3", da_test3)
da_test1 <xarray.DataArray (x: 3)>
array([ 0.90386212, 0.21516239, 0.44707272])
Coordinates:
* x (x) int64 10206 10206 10206
y int64 2
da_test2 <xarray.DataArray (x: 3, y: 1)>
array([[ 0.22799785],
[ 0.98791972],
[ 0.49444939]])
Coordinates:
* x (x) int64 0 1 2
* y (y) int64 0
da_test3 <xarray.DataArray (y: 3, x: 4)>
array([[ 0.3810371 , 0.74344288, 0.18500166, 0.32106083],
[ 0.53067774, 0.29293885, 0.5059269 , 0.921197 ],
[ 0.52148026, 0.37649667, 0.80721335, 0.32436297]])
Coordinates:
* x (x) int64 0 1 2 3
* y (y) int64 0 1 2
In [419]:
### for debugging purpose
# output the dataset ds_9day and output the dataframe
ds_9day.to_netcdf("ds_9day.nc")
row_case4 = pd.DataFrame(data={'time':list(floatsDFAll_9Dtimeorder.time), 'lon':list(floatsDFAll_9Dtimeorder.lon), 'lat':list(floatsDFAll_9Dtimeorder.lat), 'id':list(floatsDFAll_9Dtimeorder.id) } )
##print('\n before dropping nan \n', row_case4)
# process to drop nan in any of the columns [id], [lat], [lon], [time]
row_case4 = row_case4.dropna(subset=['id', 'lat', 'lon', 'time'], how = 'any') # these four fields are critical
row_case4.to_csv("row_case4.csv")
In [417]:
#### Interface to be sperated
# input: ds_9day
# indexers, {a:a1, b:b1...}
# dim, denote the main dimension
# output: chl_newInt
#
# Keyword Arguments
# approach 1 depreciate
# because: each of the indexers component might be ordered differently
#############
# def sel_points_multilinear(dset, dims='points', out = 'str', **indexers)
## test case
# sel_points_multilinear(ds_9day, dims = 'points', out ='chlor_a', time = list(['2002-07-13 00:00:00']), lat = list([5]), lon = list([70]) )
############
# e.g. time-ascending, lat-descending, need to tell 'time' from 'lat'
# so use different parameters for inputs
## approach 2
## from dataframe to dataset
## input:
## dframe: list of time, lan, lon, id
## dset: carry out the interpolation use dset data structure and its component
## output:
## a list or dataframe with chl_newInt.chl
### Todos:
# within the interface, try different test cases
# within the interface, try to use the test cases from the float data
# within the itnerface, try to seperate the code from the note book, and make the code generic
# remember to take log_e instead of log_10
#
# clean up this notebook, seperate, clean, and take notes
def sel_points_multilinear(dset, dframe, dims='points', col_name = 'str'): # col_name should match the satellite dataset
# sel_points_multilinear(ds_9day, row_case1, dims = 'points', col_name ='chlor_a')
## get the indices of time, lat, lon
idx_time = dset.indexes['time']
idx_lat = dset.indexes['lat']
idx_lon = dset.indexes['lon']
'''
################### printings
### 'time is from small to big number'
print(dset.indexes['time'])
print(type(dset.indexes['time']))
### 'lat is from *big* to small number'
print(dset.indexes['lat'])
print(type(dset.indexes['lat']))
### 'lon is from small to big number'
print(dset.indexes['lon'])
print(type(dset.indexes['lon']))
# validation: to locate the chl-a for this point {time:2002-07-13, lat:27.7916660309, lon:45.2916679382}
'''
####
#interpolation on the time dimension
time_len = len(dframe.time.values)
xtime_test = list([ np.datetime64(dframe.time.values[i]) for i in range(0,time_len) ] ) # for delta
##print('\n xtime_test \n', xtime_test)
'''caution: cannot do this inside the function get_loc,
see https://github.com/pandas-dev/pandas/issues/3488
'''
itime_nearest = [idx_time.get_loc(xtime_test[i], method='nearest') for i in range(0, time_len)]
##print('\n itime_nearest \n', itime_nearest) # [1,2]
xtime_nearest = dset.time[itime_nearest].values # ['2002-07-13T00:00:00.000000000' '2002-07-22T00:00:00.000000000']
##print('\n xtime_nearest\n', xtime_nearest) # ['2002-07-13T00:00:00.000000000' '2002-07-22T00:00:00.000000000']
##print('xtime_nearest', type(xtime_nearest)) # xtime_nearest <class 'numpy.ndarray'> # time_nearest <class 'numpy.datetime64'>
# the time distance in days
delta_xtime = (xtime_test - xtime_nearest) / np.timedelta64(1, 'D')
##print('\n delta_xtime in days \n', delta_xtime)
##print(type(delta_xtime))
itime_next = [itime_nearest[i]+1 if delta_xtime[i] >=0 else itime_nearest[i]-1 for i in range(0, time_len) ]
##print('\n itime_next \n',itime_next) # [2, 3]
# find the next coordinate values
xtime_next = dset.time[itime_next].values
##print('\n xtime_next \n', xtime_next) # ['2002-07-22T00:00:00.000000000' '2002-07-31T00:00:00.000000000']
# prepare for the Tri-linear interpolation
base_time = (xtime_next - xtime_nearest) / np.timedelta64(1, 'D') # [ 9. 9.]
##print('\n base_time \n', base_time)
w_time = delta_xtime / base_time
##print('\n w_time \n', w_time) # [ 0. 0.]
####
#interpolation on the lat dimension
#xlat_test = dframe.lat.values + 0.06 # base [ 5.20833349 5.29166174] # cell distance around .8, use .2 & .6 as two tests
xlat_test = dframe.lat.values
##print('\n xlat_test \n', xlat_test) # xlat_test [ 5.26833349 5.35166174]
ilat_nearest = [idx_lat.get_loc(xlat_test[i], method='nearest') for i in range(0, time_len)]
##print('\n ilat_nearest \n', ilat_nearest) # [272, 271]
xlat_nearest = dset.lat[ilat_nearest].values
##print('\n xlat_nearest \n', xlat_nearest) # [ 5.29166174 5.37499762]
delta_xlat = xlat_test - xlat_nearest
##print("\n delta_xlat \n",delta_xlat) # [-0.02332825 -0.02333588]
# the nearest index is on the right; but order of the latitude is different, it is descending
ilat_next = [ilat_nearest[i]-1 if delta_xlat[i] >=0 else ilat_nearest[i]+1 for i in range(0, time_len) ]
##print('\n ilat_next \n', ilat_next) # [273, 272]
# find the next coordinates value
xlat_next = dset.lat[ilat_next].values
##print('\n xlat_next \n', xlat_next) # [ 5.20833349 5.29166174]
# prepare for the Tri-linear interpolation
w_lat = delta_xlat / (xlat_next - xlat_nearest)
##print('\n w_lat \n', w_lat) # [ 0.27995605 0.28002197]
####
#interpolation on the lon dimension
#xlon_test = dframe.lon.values +0.06 # base [74.7083358765, 74.6250076294] # cell distance around .8, use .2 & .6 as two tests
xlon_test = dframe.lon.values
##print('\n xlon_test \n', xlon_test) # [ 74.76833588 74.68500763]
ilon_nearest = [idx_lon.get_loc(xlon_test[i], method='nearest') for i in range(0, time_len)]
##print('\n ilon_nearest \n', ilon_nearest) # [357, 356]
xlon_nearest = dset.lon[ilon_nearest].values
##print('\n xlon_nearest \n', xlon_nearest) # [ 74.79166412 74.70833588]
delta_xlon = xlon_test - xlon_nearest
##print("\n delta_xlon \n", delta_xlon) # [-0.02332825 -0.02332825]
ilon_next = [ilon_nearest[i]+1 if delta_xlon[i] >=0 else ilon_nearest[i]-1 for i in range(0, time_len) ]
##print('\n ilon_next \n',ilon_next) # [356, 355]
# find the next coordinate values
xlon_next = dset.lon[ilon_next].values
##print("\n xlon_next \n", xlon_next) # [ 74.70833588 74.62500763]
# prepare for the Tri-linear interpolation
w_lon = delta_xlon / (xlon_next - xlon_nearest)
##print("\n w_lon \n", w_lon) # [ 0.27995605 0.27995605]
####
# local Tensor product for Trilinear interpolation
# caution: nan values, store as "list_of_array to 2d_array" first, then sum
# no casting to list needed here, inputs are already lists
tmp = np.array([
dset[col_name].isel_points(time=itime_nearest, lat=ilat_nearest, lon=ilon_nearest).values,
dset[col_name].isel_points(time=itime_nearest, lat=ilat_nearest, lon=ilon_next).values,
dset[col_name].isel_points(time=itime_nearest, lat=ilat_next, lon=ilon_nearest).values,
dset[col_name].isel_points(time=itime_nearest, lat=ilat_next, lon=ilon_next).values,
dset[col_name].isel_points(time=itime_next, lat=ilat_nearest, lon=ilon_nearest).values,
dset[col_name].isel_points(time=itime_next, lat=ilat_nearest, lon=ilon_next).values,
dset[col_name].isel_points(time=itime_next, lat=ilat_next, lon=ilon_nearest).values,
dset[col_name].isel_points(time=itime_next, lat=ilat_next, lon=ilon_next).values ])
weights = np.array([(1-w_time)*(1-w_lat)*(1-w_lon),
(1-w_time)*(1-w_lat)*w_lon,
(1-w_time)*w_lat*(1-w_lon),
(1-w_time)*w_lat*w_lon,
w_time*(1-w_lat)*(1-w_lon),
w_time*(1-w_lat)*w_lon,
w_time*w_lat*(1-w_lon),
w_time*w_lat*w_lon ])
# how to deal with "nan" values, fill in missing values for the np.array tmpAll
# or fill the mean values to the unweighted array
# http://stackoverflow.com/questions/18689235/numpy-array-replace-nan-values-with-average-of-columns
print('\n neighbouring tensor used \n', tmp)
'''
neighbouring tensor used
[[ nan 0.181841 ]
[ 0.245878 nan]
[ nan nan]
[ nan nan]
[ 0.19680101 nan]
[ nan nan]
[ nan nan]
[ 0.18532801 nan]]
'''
# column min: (nan+0.245878 + nan + nan + 0.19680101 + nan + nan + 0.18532801)/8 = 0.20933567333
col_mean = np.nanmean(tmp, axis=0)
print('\n its mean along axis 0(column) \n', col_mean) # [ 0.20933567 0.181841 ]
# filling the missing values.
inds = np.where(np.isnan(tmp))
print('\n nan index\n', inds)
tmp[inds]=np.take(col_mean, inds[1])
print('\n values after the fill \n', tmp)
print('\n weighting tensor used \n', weights)
print("weights.shape", weights.shape) # (8, 3)
print("tmp.shape", tmp.shape) # (8, 3)
nrow_w, ncol_w = weights.shape
nrow_t, ncol_t = tmp.shape
assert nrow_w == nrow_t, "the row count of weights and values are not the same!"
assert ncol_w == ncol_t, "the row count of weights and values are not the same!"
print('\n tensor product\n', np.dot(weights[:,0], tmp[:,0]) ) # 0.216701896135 should be [ 0.2167019]
# new interpolation process of the Chl_a
chl_new = np.empty(ncol_w)
for i in range(0, ncol_w, 1):
chl_new[i] = np.dot(weights[:,i], tmp[:,i])
print('chl_newInt', chl_new) # [ 0.2167019 0.181841 0.2167019]
# validate by 1D array
# val = np.array([0.20933567, 0.245878, 0.20933567,
# 0.20933567, 0.19680101, 0.20933567,
# 0.20933567,0.18532801])
# np.dot(val, weights) # 0.21670189702309739
## output xarray.dataarray of points, see examples below
# this is the way how xarray.Dataset works
# if you want a xarray.DataArray, first generate a xarray.Dataset, then select DataArray from there.
dframe_out = xr.Dataset({col_name: (['id'],chl_new)},
coords={
#'time':(['points'],['2002-07-13 00:00:00', '2002-07-22 00:00:00', '2002-07-13 00:00:00']) ,
'time':(['points'], dframe.time) ,
'id': (['points'], dframe.id),
'lon': (['points'], dframe.lon),
'lat':(['points'], dframe.lat),
'points': (['points'], range(0,len(dframe))) } ) # this way the dims is set to point
print('\n',dframe_out[col_name])
## summation
print('\n dset \n', dset)
print('\n dims \n', dims)
#print('\n time is \n', time)
#print('\n lat is \n', lat )
#print('\n lon is \n', lon )
return dframe_out
#newtmpAll = sel_points_multilinear(ds_9day, dims = 'points', out ='chlor_a', time = list(['2002-07-13 00:00:00']), lat = list([5]), lon = list([70]) )
################
##test case 1-3
## xlat_test = dframe.lat.values + 0.06
## xlon_test = dframe.lon.values +0.06
# test case 1: take a single entry (southeast corner for valid values) (passed)
#row_case1 = pd.DataFrame(data = {'time':'2002-07-13 00:00:00', 'id': 10206, 'lon':74.7083358765, 'lat':5.20833349228},index=[1])
#print(row_case1)
#result_out1 = sel_points_multilinear(ds_9day, row_case1, dims = 'points', col_name ='chlor_a') # + 0.06 for both
#print(result_out1)
# recheck the [id], [index], [] & there length in the output
# test case 2: take 3 entries (passed):
#row_case2 = pd.DataFrame(data = {'time':['2002-07-13 00:00:00', '2002-07-22 00:00:00', '2002-07-13 00:00:00'] , 'id': [10206, 10206, 10206], 'lon':[74.7083358765, 74.6250076294,74.7083358765], 'lat':[5.20833349228, 5.29166173935, 5.20833349228]}, index=[1,2,3])
#print(row_case2)
#result_out2 = sel_points_multilinear(ds_9day, row_case2, dims = 'points', col_name ='chlor_a')
#print(result_out2)
# test case 3: use the partial real data
#row_case3 = pd.DataFrame(data={'time':list(floatsDFAll_9Dtimeorder.time[:15]), 'lon':list(floatsDFAll_9Dtimeorder.lon[:15]), 'lat':list(floatsDFAll_9Dtimeorder.lat[:15]), 'id':list(floatsDFAll_9Dtimeorder.id[:15]) } )
#print('\n before dropping nan \n', row_case3)
## process to drop nan in any of the columns [id], [lat], [lon], [time]
#row_case3 = row_case3.dropna(subset=['id', 'lat', 'lon', 'time'], how = 'any') # these four fields are critical
#print('\n after dropping nan \n', row_case3)
#result_out3 = sel_points_multilinear(ds_9day, row_case3, dims = 'points', col_name ='chlor_a')
#print('\n after the preprocessing \n', result_out3)
#print('\n this two length should be equal %d == %d?' %(len(row_case3.index), len(result_out3.points) ) )
# test case 4: use the full real data
row_case4 = pd.DataFrame(data={'time':list(floatsDFAll_9Dtimeorder.time), 'lon':list(floatsDFAll_9Dtimeorder.lon), 'lat':list(floatsDFAll_9Dtimeorder.lat), 'id':list(floatsDFAll_9Dtimeorder.id) } )
print('\n before dropping nan \n', row_case4)
# process to drop nan in any of the columns [id], [lat], [lon], [time]
row_case4 = row_case4.dropna(subset=['id', 'lat', 'lon', 'time'], how = 'any') # these four fields are critical
print('\n after dropping nan \n', row_case4)
result_out4 = sel_points_multilinear(ds_9day, row_case4, dims = 'points', col_name ='chlor_a')
print('\n after the preprocessing \n', result_out4)
print('\n this two length should be equal %d == %d?' %(len(row_case4.index), len(result_out4.points) ) )
# c. check the interface and may need to take the id and index from the float data, decide where to dropna,
# perhaps before the functions calls
#
# d. need to remove or comment out the print outs
# task
# a. to simulate & seperate this interface, call it as a function
# b. a better approach is to simulate the input and output stream to write the test case and functions
# tmpAll = ds_9day.chlor_a.sel_points(time=list(floatsDFAll_9Dtimeorder.time),lon=list(floatsDFAll_9Dtimeorder.lon), lat=list(floatsDFAll_9Dtimeorder.lat), method='nearest')
File "<ipython-input-417-cc44b3152088>", line 41
dset = ds_9day
^
IndentationError: unexpected indent
In [437]:
row_case4.lat.describe()
Out[437]:
count 3466.000000
mean 11.835987
std 4.562076
min 5.001000
25% 7.796354
50% 11.730181
75% 15.130285
max 26.863000
Name: lat, dtype: float64
In [442]:
ds_9day.lat.to_series().describe()
Out[442]:
count 276.000000
mean 16.499998
std 6.651546
min 5.041662
25% 10.770829
50% 16.499998
75% 22.229166
max 27.958334
Name: lat, dtype: float64
In [445]:
ds_9day.lat
Out[445]:
<xarray.DataArray 'lat' (lat: 276)>
array([ 27.95833397, 27.87499809, 27.79166603, 27.70833397,
27.62499809, 27.54166603, 27.45833397, 27.37499809,
27.29166603, 27.20833397, 27.12499809, 27.04166603,
26.95833397, 26.87499809, 26.79166603, 26.70833397,
26.62499809, 26.54166603, 26.45833397, 26.37499809,
26.29166603, 26.20833397, 26.12499809, 26.04166603,
25.95833397, 25.87499809, 25.79166222, 25.70833397,
25.62499809, 25.54166222, 25.45833397, 25.37499809,
25.29166222, 25.20833397, 25.12499809, 25.04166222,
24.95833397, 24.87499809, 24.79166222, 24.70833397,
24.62499809, 24.54166222, 24.45833397, 24.37499809,
24.29166222, 24.20833397, 24.12499809, 24.04166222,
23.95833397, 23.87499809, 23.79166222, 23.70833397,
23.62499809, 23.54166222, 23.45833397, 23.37499809,
23.29166222, 23.20833397, 23.12499809, 23.04166222,
22.95833397, 22.87499809, 22.79166222, 22.70833397,
22.62499809, 22.54166222, 22.45833397, 22.37499809,
22.29166222, 22.20833397, 22.12499809, 22.04166222,
21.95833397, 21.87499809, 21.79166222, 21.70833397,
21.62499809, 21.54166222, 21.45833397, 21.37499809,
21.29166222, 21.20833397, 21.12499809, 21.04166222,
20.95833397, 20.87499809, 20.79166222, 20.70833397,
20.62499809, 20.54166222, 20.45833397, 20.37499809,
20.29166222, 20.20833397, 20.12499809, 20.04166222,
19.95833397, 19.87499809, 19.79166222, 19.70833397,
19.62499809, 19.54166222, 19.45833397, 19.37499809,
19.29166222, 19.20833397, 19.12499809, 19.04166222,
18.95833397, 18.87499809, 18.79166222, 18.70833397,
18.62499809, 18.54166222, 18.45833397, 18.37499809,
18.29166222, 18.20833397, 18.12499809, 18.04166222,
17.95833397, 17.87499809, 17.79166222, 17.70833397,
17.62499809, 17.54166222, 17.45833397, 17.37499809,
17.29166222, 17.20833397, 17.12499809, 17.04166222,
16.95833397, 16.87499809, 16.79166222, 16.70833397,
16.62499809, 16.54166222, 16.45833397, 16.37499809,
16.29166222, 16.20833397, 16.12499809, 16.04166222,
15.95833302, 15.87499714, 15.79166126, 15.70833302,
15.62499714, 15.54166126, 15.45833302, 15.37499714,
15.29166126, 15.20833302, 15.12499714, 15.04166126,
14.95833302, 14.87499714, 14.79166126, 14.70833302,
14.62499714, 14.54166126, 14.45833302, 14.37499714,
14.29166126, 14.20833302, 14.12499714, 14.04166126,
13.95833302, 13.87499714, 13.79166126, 13.70833302,
13.62499714, 13.54166126, 13.45833302, 13.37499714,
13.29166126, 13.20833302, 13.12499714, 13.04166126,
12.95833302, 12.87499714, 12.79166126, 12.70833302,
12.62499714, 12.54166126, 12.45833302, 12.37499714,
12.29166126, 12.20833302, 12.12499714, 12.04166126,
11.95833302, 11.87499714, 11.79166126, 11.70833302,
11.62499714, 11.54166126, 11.45833302, 11.37499714,
11.29166126, 11.20833302, 11.12499714, 11.04166126,
10.95833302, 10.87499714, 10.79166126, 10.70833302,
10.62499714, 10.54166126, 10.45833302, 10.37499714,
10.29166126, 10.20833302, 10.12499714, 10.04166126,
9.95833302, 9.87499714, 9.79166126, 9.70833302,
9.62499714, 9.54166126, 9.45833302, 9.37499714,
9.29166126, 9.20833302, 9.12499714, 9.04166126,
8.95833302, 8.87499714, 8.79166126, 8.70833302,
8.62499714, 8.54166126, 8.45833302, 8.37499714,
8.29166126, 8.20833302, 8.12499714, 8.04166126,
7.95833349, 7.87499762, 7.79166174, 7.70833349,
7.62499762, 7.54166174, 7.45833349, 7.37499762,
7.29166174, 7.20833349, 7.12499762, 7.04166174,
6.95833349, 6.87499762, 6.79166174, 6.70833349,
6.62499762, 6.54166174, 6.45833349, 6.37499762,
6.29166174, 6.20833349, 6.12499762, 6.04166174,
5.95833349, 5.87499762, 5.79166174, 5.70833349,
5.62499762, 5.54166174, 5.45833349, 5.37499762,
5.29166174, 5.20833349, 5.12499762, 5.04166174])
Coordinates:
* lat (lat) float64 27.96 27.87 27.79 27.71 27.62 27.54 27.46 27.37 ...
Attributes:
long_name: Latitude
units: degree_north
valid_min: -90.0
valid_max: 90.0
In [444]:
(27.958334 - 5.041662) / 0.08
Out[444]:
286.45840000000004
In [438]:
row_case4.lon.describe()
Out[438]:
count 3466.000000
mean 63.972772
std 6.663474
min 45.177323
25% 59.226904
50% 65.254475
75% 69.165937
max 74.996000
Name: lon, dtype: float64
In [443]:
ds_9day.lon.to_series().describe()
Out[443]:
count 360.000000
mean 60.000005
std 8.672272
min 45.041668
25% 52.520836
50% 60.000004
75% 67.479168
max 74.958336
Name: lon, dtype: float64
In [461]:
row_case4 # 3466 rows × 4 columns
Out[461]:
id
lat
lon
time
1
10206
16.219000
66.533639
2002-07-04
2
10208
13.599500
69.914139
2002-07-04
3
11089
16.068778
65.036972
2002-07-04
4
15703
13.637028
69.915889
2002-07-04
6
27069
20.077750
69.445889
2002-07-04
8
28842
18.624861
60.830556
2002-07-04
9
34159
12.894139
59.736889
2002-07-04
11
34210
6.022361
56.763194
2002-07-04
12
34211
8.210750
68.565389
2002-07-04
13
34212
6.679556
65.946111
2002-07-04
21
34708
10.225111
60.658278
2002-07-04
22
34709
5.027000
52.974000
2002-07-04
23
34710
13.057111
50.333750
2002-07-04
24
34714
13.741167
64.694000
2002-07-04
25
34716
7.716750
66.285250
2002-07-04
26
34718
15.458417
72.973500
2002-07-04
27
34719
17.217028
71.378167
2002-07-04
28
34720
14.142056
69.482472
2002-07-04
29
34721
16.928722
65.552333
2002-07-04
30
34722
11.437306
70.746722
2002-07-04
31
34723
16.602917
66.431333
2002-07-04
224
2134712
9.076889
63.827222
2002-07-04
260
10206
16.377000
66.940500
2002-07-13
261
10208
13.412417
70.726278
2002-07-13
262
11089
14.950806
65.671444
2002-07-13
263
15703
13.542222
70.630806
2002-07-13
265
27069
19.667861
71.304944
2002-07-13
267
28842
17.796611
61.256583
2002-07-13
268
34159
14.227972
62.072750
2002-07-13
270
34210
5.166000
56.681857
2002-07-13
...
...
...
...
...
145765
127406
6.122556
74.268694
2016-05-09
145781
147127
14.255222
63.940944
2016-05-09
145791
60150420
6.582000
56.257750
2016-05-09
145816
62321420
5.877071
50.923714
2016-05-09
146002
114873
6.767000
53.584111
2016-05-18
146006
114917
12.170583
71.688611
2016-05-18
146007
114945
12.602944
61.497639
2016-05-18
146024
127406
5.092400
74.408400
2016-05-18
146040
147127
15.968694
63.791167
2016-05-18
146050
60150420
6.247778
57.581639
2016-05-18
146075
62321420
5.439722
50.685500
2016-05-18
146261
114873
7.209389
52.048528
2016-05-27
146265
114917
12.177167
72.052556
2016-05-27
146266
114945
12.663167
62.420472
2016-05-27
146299
147127
15.647833
65.088111
2016-05-27
146309
60150420
5.405667
59.078556
2016-05-27
146334
62321420
5.699074
50.822889
2016-05-27
146520
114873
8.219972
53.932500
2016-06-05
146524
114917
11.990111
72.433333
2016-06-05
146525
114945
12.374472
64.366056
2016-06-05
146558
147127
14.264861
65.293000
2016-06-05
146593
62321420
5.035143
52.675000
2016-06-05
146779
114873
5.619294
52.882000
2016-06-14
146783
114917
11.269667
72.658000
2016-06-14
146784
114945
11.410694
65.215833
2016-06-14
146817
147127
13.010806
66.670889
2016-06-14
147038
114873
7.734613
52.556935
2016-06-23
147042
114917
10.514129
73.125452
2016-06-23
147043
114945
10.182875
65.232563
2016-06-23
147076
147127
11.982875
68.082437
2016-06-23
3466 rows × 4 columns
In [471]:
# testcase 4: using the real data
#row_case4 = pd.read_csv("./row_case4.csv")
#ds_9day = xr.open_dataset("./ds_9day.nc")
# bounds
# row_case.lat > ds_9day.lat.min # descending
# row_case.lat < ds_9day.lat.max # descending
# row_case.lon > ds_9day.lat.min # ascending
# row_case.lat > ds_9day.lat.min # ascending
ds_9day_latmin = ds_9day.lat.to_series().min()
ds_9day_latmax = ds_9day.lat.to_series().max()
ds_9day_lonmin = ds_9day.lon.to_series().min()
ds_9day_lonmax = ds_9day.lon.to_series().max()
mask = (row_case4.lat > ds_9day_latmin) & (row_case4.lat < ds_9day_latmax) \
& (row_case4.lon > ds_9day_lonmin) & (row_case4.lon < ds_9day_lonmax)
row_case4 = row_case4[mask]
print(row_case4) # [3440 rows x 4 columns]
id lat lon time
1 10206 16.219000 66.533639 2002-07-04
2 10208 13.599500 69.914139 2002-07-04
3 11089 16.068778 65.036972 2002-07-04
4 15703 13.637028 69.915889 2002-07-04
6 27069 20.077750 69.445889 2002-07-04
8 28842 18.624861 60.830556 2002-07-04
9 34159 12.894139 59.736889 2002-07-04
11 34210 6.022361 56.763194 2002-07-04
12 34211 8.210750 68.565389 2002-07-04
13 34212 6.679556 65.946111 2002-07-04
21 34708 10.225111 60.658278 2002-07-04
23 34710 13.057111 50.333750 2002-07-04
24 34714 13.741167 64.694000 2002-07-04
25 34716 7.716750 66.285250 2002-07-04
26 34718 15.458417 72.973500 2002-07-04
27 34719 17.217028 71.378167 2002-07-04
28 34720 14.142056 69.482472 2002-07-04
29 34721 16.928722 65.552333 2002-07-04
30 34722 11.437306 70.746722 2002-07-04
31 34723 16.602917 66.431333 2002-07-04
224 2134712 9.076889 63.827222 2002-07-04
260 10206 16.377000 66.940500 2002-07-13
261 10208 13.412417 70.726278 2002-07-13
262 11089 14.950806 65.671444 2002-07-13
263 15703 13.542222 70.630806 2002-07-13
265 27069 19.667861 71.304944 2002-07-13
267 28842 17.796611 61.256583 2002-07-13
268 34159 14.227972 62.072750 2002-07-13
270 34210 5.166000 56.681857 2002-07-13
271 34211 7.079694 69.984639 2002-07-13
... ... ... ... ...
145748 114945 12.211833 61.181694 2016-05-09
145765 127406 6.122556 74.268694 2016-05-09
145781 147127 14.255222 63.940944 2016-05-09
145791 60150420 6.582000 56.257750 2016-05-09
145816 62321420 5.877071 50.923714 2016-05-09
146002 114873 6.767000 53.584111 2016-05-18
146006 114917 12.170583 71.688611 2016-05-18
146007 114945 12.602944 61.497639 2016-05-18
146024 127406 5.092400 74.408400 2016-05-18
146040 147127 15.968694 63.791167 2016-05-18
146050 60150420 6.247778 57.581639 2016-05-18
146075 62321420 5.439722 50.685500 2016-05-18
146261 114873 7.209389 52.048528 2016-05-27
146265 114917 12.177167 72.052556 2016-05-27
146266 114945 12.663167 62.420472 2016-05-27
146299 147127 15.647833 65.088111 2016-05-27
146309 60150420 5.405667 59.078556 2016-05-27
146334 62321420 5.699074 50.822889 2016-05-27
146520 114873 8.219972 53.932500 2016-06-05
146524 114917 11.990111 72.433333 2016-06-05
146525 114945 12.374472 64.366056 2016-06-05
146558 147127 14.264861 65.293000 2016-06-05
146779 114873 5.619294 52.882000 2016-06-14
146783 114917 11.269667 72.658000 2016-06-14
146784 114945 11.410694 65.215833 2016-06-14
146817 147127 13.010806 66.670889 2016-06-14
147038 114873 7.734613 52.556935 2016-06-23
147042 114917 10.514129 73.125452 2016-06-23
147043 114945 10.182875 65.232563 2016-06-23
147076 147127 11.982875 68.082437 2016-06-23
[3440 rows x 4 columns]
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
#### Interface to be seperated from this notebook and into a different routine
def sel_points_multilinear(da, dim='yanyan', **indexers):
# input xarray.dataset
# list of coordinates, time[], lon[], lat[],
# out put a list of
return chl_newInt
pass
#ds_9day.chlor_a.sel_points(time=list([xtime_test]),lon=list([xlon_test]), lat=list([xlat_test]), method='nearest')
# tmpAll = ds_9day.chlor_a.sel_points(time=list(floatsDFAll_9Dtimeorder.time),lon=list(floatsDFAll_9Dtimeorder.lon), lat=list(floatsDFAll_9Dtimeorder.lat), method='nearest')
In [ ]:
### think about an interface to sperate the test case as a seperate piece.
### should be easy to do
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
#print(type(tmpAll))
#print(size(tmpAll))
#tmpAll[0].shape
#ds_9day.chlor_a.sel_points(time=list([xtime_test]),lon=list([xlon_test]), lat=list([xlat_test]), method='nearest')
In [186]:
Out[186]:
0.21670189702309739
In [417]:
time=list([itime_nearest])
time
Out[417]:
[326]
In [418]:
lat=list([ilat_nearest])
lat
Out[418]:
[2]
In [300]:
lon=list([ilon_nearest])
lon
Out[300]:
[3]
In [323]:
print((1-w_time)*(1-w_lat)*w_lon*ds_9day.chlor_a.isel_points(time=list([itime_nearest]), lat=list([ilat_nearest]), lon=list([ilon_next])) )
<xarray.DataArray 'chlor_a' (points: 1)>
array([ nan])
Coordinates:
lon (points) float64 45.21
time (points) datetime64[ns] 2002-07-13
lat (points) float64 27.79
* points (points) int64 0
In [326]:
a = [np.nan, 5, 6]
In [335]:
ds_9day.chlor_a.isel_points(time=list([itime_nearest]), lat=list([ilat_nearest]), lon=list([ilon_nearest])).values
Out[335]:
array([ nan])
In [362]:
xtime_test
Out[362]:
numpy.datetime64('2002-07-13T00:00:00')
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [128]:
#### the api for the interpolation operator
def sel_points_multilinear(da, dim='points', **indexers):
"""Return a new DataArray whose dataset is given by bilinear
interpoation of index labels along the specified dimension(s).
"""
pass
#sel_points_multilinear(data, lon=[25,45], lat=[10,20], time=[])
In [11]:
#### an 2D unit test example
def test_sel_points_multilinear():
x = np.arange(10)
data = 10*x
da = xr.DataArray(data, dims='x', coords={'x': x})
x0 = 1.5
data_expected = 15
data_interp = sel_points_multilinear(da, x=x0)
assert data_interp.values[0] == data_expected
x0 = [1.5, 2.5]
data_expected = [15, 25]
data_interp = sel_points_multilinear(da, x=x0)
assert data_interp.values == data_expected
test_sel_points_multilinear()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-11-fb5bce06b808> in <module>()
18
19
---> 20 test_sel_points_multilinear()
<ipython-input-11-fb5bce06b808> in test_sel_points_multilinear()
1 #### an 2D unit test example
2 def test_sel_points_multilinear():
----> 3 x = np.arange(10)
4 data = 10*x
5 da = xr.DataArray(data, dims='x', coords={'x': x})
NameError: name 'np' is not defined
In [118]:
da = xr.DataArray(np.random.rand(10), dims='x')
da
Out[118]:
<xarray.DataArray (x: 10)>
array([ 0.52458177, 0.88233255, 0.68879645, 0.04002446, 0.44534634,
0.89089184, 0.20623118, 0.23330272, 0.7336189 , 0.46581596])
Coordinates:
* x (x) int64 0 1 2 3 4 5 6 7 8 9
In [125]:
da.sel_points(x=[5,6], dim='float_number') # dim is just the name of the coordinates
Out[125]:
<xarray.DataArray (float_number: 2)>
array([ 0.89089184, 0.20623118])
Coordinates:
x (float_number) int64 5 6
* float_number (float_number) int64 0 1
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In [113]:
Out[113]:
id
lat
lon
time
1
10206
16.219
66.534
2002-07-04 00:00:00
In [96]:
np.random.randn(1,4)
Out[96]:
array([[ 0.6732195 , -2.05477633, 1.71085993, -0.48701498]])
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# a little test for the api in loops for the dataframe
# check df.itertuples? it is faster and preserves the data format
'''
chl_ocx=[]
for row in floats_timeorder.itertuples():
#print(row)
#print('row.time = %s, row.id=%d, row.lon=%4.3f, row.lat=%4.3f' % (row.time,row.id,row.lon,row.lat) )
tmp=ds_2day.chl_ocx.sel_points(time=[row.time],lon=[row.lon], lat=[row.lat], method='nearest') # interpolation
chl_ocx.append(tmp)
floats_timeorder['chl_ocx'] = pd.Series(chl_ocx, index=floats_timeorder.index)
chl_ocx[0].to_series
'''
In [ ]:
for row in floatsDFAll_9Dtimeorder.itertuples():
print('row.time = %s, row.id=%d, row.lon=%4.3f, row.lat=%4.3f' % (row.time,row.id,row.lon,row.lat) )
# now just focus on this singel point:
# row.time = 2002-07-04 00:00:00, row.id=10206, row.lon=66.534, row.lat=16.219, do interpolation
row.time = 2002-07-04 00:00:00, row.id=7574, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=10206, row.lon=66.534, row.lat=16.219
row.time = 2002-07-04 00:00:00, row.id=10208, row.lon=69.914, row.lat=13.599
row.time = 2002-07-04 00:00:00, row.id=11089, row.lon=65.037, row.lat=16.069
row.time = 2002-07-04 00:00:00, row.id=15703, row.lon=69.916, row.lat=13.637
row.time = 2002-07-04 00:00:00, row.id=15707, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=27069, row.lon=69.446, row.lat=20.078
row.time = 2002-07-04 00:00:00, row.id=27139, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=28842, row.lon=60.831, row.lat=18.625
row.time = 2002-07-04 00:00:00, row.id=34159, row.lon=59.737, row.lat=12.894
row.time = 2002-07-04 00:00:00, row.id=34173, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=34210, row.lon=56.763, row.lat=6.022
row.time = 2002-07-04 00:00:00, row.id=34211, row.lon=68.565, row.lat=8.211
row.time = 2002-07-04 00:00:00, row.id=34212, row.lon=65.946, row.lat=6.680
row.time = 2002-07-04 00:00:00, row.id=34223, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=34310, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=34311, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=34312, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=34314, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=34315, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=34374, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=34708, row.lon=60.658, row.lat=10.225
row.time = 2002-07-04 00:00:00, row.id=34709, row.lon=52.974, row.lat=5.027
row.time = 2002-07-04 00:00:00, row.id=34710, row.lon=50.334, row.lat=13.057
row.time = 2002-07-04 00:00:00, row.id=34714, row.lon=64.694, row.lat=13.741
row.time = 2002-07-04 00:00:00, row.id=34716, row.lon=66.285, row.lat=7.717
row.time = 2002-07-04 00:00:00, row.id=34718, row.lon=72.974, row.lat=15.458
row.time = 2002-07-04 00:00:00, row.id=34719, row.lon=71.378, row.lat=17.217
row.time = 2002-07-04 00:00:00, row.id=34720, row.lon=69.482, row.lat=14.142
row.time = 2002-07-04 00:00:00, row.id=34721, row.lon=65.552, row.lat=16.929
row.time = 2002-07-04 00:00:00, row.id=34722, row.lon=70.747, row.lat=11.437
row.time = 2002-07-04 00:00:00, row.id=34723, row.lon=66.431, row.lat=16.603
row.time = 2002-07-04 00:00:00, row.id=36530, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=36537, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=37192, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=37194, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=37200, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=37213, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=37214, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=37641, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=39204, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=40084, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=40273, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=40552, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=41317, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=42519, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=43598, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=43740, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=43743, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=43744, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=43746, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=43748, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=43995, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=45748, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=46470, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=46471, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=46472, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=53352, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=53358, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=53363, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=53364, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=53365, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=53366, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=53367, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=53404, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=54017, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=54038, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=54371, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=57939, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=57940, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=59213, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=59215, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=59362, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=59363, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=59365, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=59366, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=59367, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=59368, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=59369, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=59371, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=59372, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=59373, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=59394, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=60492, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=62193, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=62195, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=62196, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=62197, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=62198, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=62199, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=62200, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=62201, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=62202, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=62207, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=62552, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=62553, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=62554, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=62555, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=62556, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=62557, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=62558, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=63036, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=63067, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=63068, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=63069, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=63070, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=63071, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=63072, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=63073, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=63074, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=63075, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=63076, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=63926, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=63928, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=63929, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=63930, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=63934, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=63935, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=70695, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=70696, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=70697, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=70699, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=70952, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=71138, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=71139, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=71140, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=71141, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=71142, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=71158, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=71159, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=71160, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=71161, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=71162, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=72633, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=72634, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=72638, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=73076, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=73077, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=73079, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=75137, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=75138, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=75140, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=75141, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=75142, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=79184, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=79185, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=79188, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=79322, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=81824, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=81828, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=81851, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=81938, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=81996, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=81997, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=82625, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=83499, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=88651, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=88652, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=88659, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=88671, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=90485, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=90513, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=92626, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=92627, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=92630, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=92631, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=98672, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=98673, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=98674, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=98675, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=98676, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=98679, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=101609, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=101833, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=109290, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=109377, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=109382, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=109402, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=109404, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=109544, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=109551, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=114553, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=114556, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=114559, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=114575, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=114873, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=114874, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=114875, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=114876, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=114917, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=114945, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=114948, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=116006, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=116184, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=116187, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=116212, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=116345, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=116463, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=116464, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=116465, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=116466, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=116467, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=116468, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=126933, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=126935, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=126950, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=127055, row.lon= nan, row.lat= nan
row.time = 2002-07-04 00:00:00, row.id=127406, row.lon= nan, row.lat= nan
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row.time = 2015-01-09 00:00:00, row.id=37194, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=37200, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=37213, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=37214, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=37641, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=39204, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=40084, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=40273, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=40552, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=41317, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=42519, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=43598, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=43740, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=43743, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=43744, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=43746, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=43748, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=43995, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=45748, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=46470, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=46471, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=46472, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=53352, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=53358, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=53363, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=53364, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=53365, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=53366, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=53367, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=53404, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=54017, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=54038, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=54371, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=57939, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=57940, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=59213, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=59215, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=59362, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=59363, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=59365, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=59366, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=59367, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=59368, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=59369, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=59371, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=59372, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=59373, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=59394, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=60492, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=62193, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=62195, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=62196, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=62197, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=62198, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=62199, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=62200, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=62201, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=62202, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=62207, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=62552, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=62553, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=62554, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=62555, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=62556, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=62557, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=62558, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=63036, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=63067, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=63068, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=63069, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=63070, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=63071, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=63072, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=63073, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=63074, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=63075, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=63076, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=63926, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=63928, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=63929, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=63930, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=63934, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=63935, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=70695, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=70696, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=70697, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=70699, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=70952, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=71138, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=71139, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=71140, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=71141, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=71142, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=71158, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=71159, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=71160, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=71161, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=71162, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=72633, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=72634, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=72638, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=73076, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=73077, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=73079, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=75137, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=75138, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=75140, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=75141, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=75142, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=79184, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=79185, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=79188, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=79322, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=81824, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=81828, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=81851, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=81938, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=81996, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=81997, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=82625, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=83499, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=88651, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=88652, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=88659, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=88671, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=90485, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=90513, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=92626, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=92627, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=92630, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=92631, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=98672, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=98673, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=98674, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=98675, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=98676, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=98679, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=101609, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=101833, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=109290, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=109377, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=109382, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=109402, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=109404, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=109544, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=109551, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=114553, row.lon=67.030, row.lat=15.754
row.time = 2015-01-09 00:00:00, row.id=114556, row.lon=66.506, row.lat=15.641
row.time = 2015-01-09 00:00:00, row.id=114559, row.lon=65.111, row.lat=23.379
row.time = 2015-01-09 00:00:00, row.id=114575, row.lon=67.037, row.lat=19.022
row.time = 2015-01-09 00:00:00, row.id=114873, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=114874, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=114875, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=114876, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=114917, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=114945, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=114948, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=116006, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=116184, row.lon=50.666, row.lat=8.025
row.time = 2015-01-09 00:00:00, row.id=116187, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=116212, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=116345, row.lon=74.404, row.lat=5.146
row.time = 2015-01-09 00:00:00, row.id=116463, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=116464, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=116465, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=116466, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=116467, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=116468, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=126933, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=126935, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=126950, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=127055, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=127406, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=127429, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=133654, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=133659, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=135776, row.lon=69.398, row.lat=15.190
row.time = 2015-01-09 00:00:00, row.id=135780, row.lon=69.946, row.lat=17.245
row.time = 2015-01-09 00:00:00, row.id=135781, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=135782, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=135784, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=135785, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=135786, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=135787, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=135788, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=135789, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=135790, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=145074, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=147127, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=2134712, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=2343739, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=2444350, row.lon= nan, row.lat= nan
row.time = 2015-01-09 00:00:00, row.id=3098671, row.lon= nan, row.lat= nan
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# a little test for the api in loops for the dataframe
# check df.itertuples? it is faster and preserves the data format
'''
chl_ocx=[]
for row in floats_timeorder.itertuples():
#print(row) # this line should not be executed!!!! takes a long time!!!!!
#print('row.time = %s, row.id=%d, row.lon=%4.3f, row.lat=%4.3f' % (row.time,row.id,row.lon,row.lat) )
tmp=ds_2day.chl_ocx.sel_points(time=[row.time],lon=[row.lon], lat=[row.lat], method='nearest') # interpolation
chl_ocx.append(tmp)
floats_timeorder['chl_ocx'] = pd.Series(chl_ocx, index=floats_timeorder.index)
chl_ocx[0].to_series
'''
In [372]:
# this one line avoid the list above
# it took a really long time for 2D interpolation, it takes an hour
tmpAll = ds_9day.chlor_a.sel_points(time=list(floatsDFAll_9Dtimeorder.time),lon=list(floatsDFAll_9Dtimeorder.lon), lat=list(floatsDFAll_9Dtimeorder.lat), method='nearest')
#print('the count of nan vaues in tmpAll is',tmpAll.to_series().isnull().sum())
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/pandas/indexes/base.py:2352: RuntimeWarning: invalid value encountered in less
indexer = np.where(op(left_distances, right_distances) |
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/pandas/indexes/base.py:2352: RuntimeWarning: invalid value encountered in less_equal
indexer = np.where(op(left_distances, right_distances) |
In [373]:
tmpAll # an array of xarray.DataArray
Out[373]:
<xarray.DataArray 'chlor_a' (points: 147112)>
array([ nan, nan, nan, ..., nan, nan, nan])
Coordinates:
time (points) datetime64[ns] 2002-07-04 2002-07-04 2002-07-04 ...
lon (points) float64 74.96 66.54 69.88 65.04 69.88 74.96 69.46 ...
lat (points) float64 27.96 16.21 13.62 16.04 13.62 27.96 20.04 ...
* points (points) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 ...
In [374]:
#print(tmpAll.dropna().shape)
tmpAll.to_series().dropna().shape # (1631,) good values
Out[374]:
(1631,)
In [375]:
# tmp.to_series() to transfer it from xarray dataset to series
floatsDFAll_9Dtimeorder['chlor_a'] = pd.Series(np.array(tmpAll.to_series()), index=floatsDFAll_9Dtimeorder.index)
print("after editing the dataframe the nan values in 'chlor_a' is", floatsDFAll_9Dtimeorder.chlor_a.isnull().sum() ) # they should be the same values as above
# take a look at the data
floatsDFAll_9Dtimeorder
# visualize the float around the arabian sea region
fig, ax = plt.subplots(figsize=(12,10))
floatsDFAll_9Dtimeorder.plot(kind='scatter', x='lon', y='lat', c='chlor_a', cmap='RdBu_r', edgecolor='none', ax=ax)
def scale(x):
logged = np.log10(x)
return logged
#print(floatsAll_timeorder['chlor_a'].apply(scale))
floatsDFAll_9Dtimeorder['chlor_a_log10'] = floatsDFAll_9Dtimeorder['chlor_a'].apply(scale)
floatsDFAll_9Dtimeorder
#print("after the transformation the nan values in 'chlor_a_log10' is", floatsAll_timeorder.chlor_a_log10.isnull().sum() )
# visualize the float around the arabian sea region
fig, ax = plt.subplots(figsize=(12,10))
floatsDFAll_9Dtimeorder.plot(kind='scatter', x='lon', y='lat', c='chlor_a_log10', cmap='RdBu_r', edgecolor='none', ax=ax)
floatsDFAll_9Dtimeorder.chlor_a.dropna().shape # (1631,)
#floatsDFAll_9Dtimeorder.chlor_a_log10.dropna().shape # (1631,)
after editing the dataframe the nan values in 'chlor_a' is 145481
Out[375]:
(1631,)
In [376]:
# take the diff of the chlor_a, and this has to be done in xarray
# transfer the dataframe into xarry dataset again
# take the difference
floatsDFAll_9Dtimeorder
Out[376]:
id
time
var_lat
vn
temp
lon
lat
var_tmp
var_lon
ve
spd
chlor_a
chlor_a_log10
0
7574
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
568
10206
2002-07-04
0.001326
0.027056
NaN
66.533639
16.219000
1000.000000
0.005002
7.056056
8.462583
NaN
NaN
1136
10208
2002-07-04
0.000061
-8.270944
NaN
69.914139
13.599500
1000.000000
0.000116
12.942722
17.918639
NaN
NaN
1704
11089
2002-07-04
0.000065
-14.363611
27.796889
65.036972
16.068778
0.003620
0.000123
11.143806
20.217250
NaN
NaN
2272
15703
2002-07-04
0.000054
-7.037222
28.572750
69.915889
13.637028
0.088844
0.000099
11.706389
16.841889
NaN
NaN
2840
15707
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
3408
27069
2002-07-04
0.000056
-1.797889
28.981389
69.445889
20.077750
0.001708
0.000102
24.130583
25.498500
NaN
NaN
3976
27139
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
4544
28842
2002-07-04
0.000099
-8.122111
27.620472
60.830556
18.624861
0.003344
0.000208
5.026861
18.067944
NaN
NaN
5112
34159
2002-07-04
0.000059
16.704556
NaN
59.736889
12.894139
1000.000000
0.000112
31.753083
37.039111
NaN
NaN
5680
34173
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
6248
34210
2002-07-04
0.000063
-17.727861
26.452806
56.763194
6.022361
0.003696
0.000124
-3.750167
26.752611
NaN
NaN
6816
34211
2002-07-04
0.000055
-14.241500
28.380222
68.565389
8.210750
0.003512
0.000102
23.260944
28.413083
0.118290
-0.927052
7384
34212
2002-07-04
0.000055
7.009000
28.577889
65.946111
6.679556
0.003549
0.000100
42.864500
48.849444
NaN
NaN
7952
34223
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
8520
34310
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
9088
34311
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
9656
34312
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
10224
34314
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
10792
34315
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
11360
34374
2002-07-04
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
11928
34708
2002-07-04
0.000057
2.088833
27.291500
60.658278
10.225111
0.001784
0.000109
30.589861
30.958611
NaN
NaN
12496
34709
2002-07-04
0.000094
47.817000
26.934000
52.974000
5.027000
0.002233
0.000189
-84.267000
96.889000
0.288937
-0.539196
13064
34710
2002-07-04
0.000051
-8.140111
31.149000
50.333750
13.057111
0.001854
0.000097
0.763722
47.400444
NaN
NaN
13632
34714
2002-07-04
0.000059
4.682833
27.765694
64.694000
13.741167
0.001799
0.000111
35.870556
37.025528
NaN
NaN
14200
34716
2002-07-04
0.000057
2.213806
28.780944
66.285250
7.716750
0.001785
0.000106
34.058694
37.381000
NaN
NaN
14768
34718
2002-07-04
0.000055
-33.327528
29.063444
72.973500
15.458417
0.001695
0.000103
20.389944
39.619167
NaN
NaN
15336
34719
2002-07-04
0.000057
-20.858056
28.959417
71.378167
17.217028
0.001652
0.000107
13.919778
27.380083
NaN
NaN
15904
34720
2002-07-04
0.000060
-19.437778
28.664167
69.482472
14.142056
0.001771
0.000111
11.141528
24.609778
NaN
NaN
16472
34721
2002-07-04
0.000060
-10.328972
27.908083
65.552333
16.928722
0.001733
0.000113
5.753667
13.113972
NaN
NaN
...
...
...
...
...
...
...
...
...
...
...
...
...
...
130639
3098682
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
131207
60073460
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
131775
60074440
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
132343
60077450
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
132911
60150420
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
133479
60454500
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
134047
60656200
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
134615
60657200
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
135183
60658190
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
135751
60659110
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
136319
60659120
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
136887
60659190
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
137455
60659200
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
138023
60940960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
138591
60940970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
139159
60941960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
139727
60941970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
140295
60942960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
140863
60942970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
141431
60943960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
141999
60943970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
142567
60944960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
143135
60944970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
143703
60945970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
144271
60946960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
144839
60947960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
145407
60947970
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
145975
60948960
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
146543
60950430
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
147111
62321420
2016-06-23
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
147112 rows × 13 columns
In [377]:
# unstack() will provide a 2d dataframe
# reset_index() will reset all the index as columns
In [378]:
# prepare the data in dataset and about to take the diff
tmp = xr.Dataset.from_dataframe(floatsDFAll_9Dtimeorder.set_index(['time','id']) ) # set time & id as the index); use reset_index to revert this operation
# take the diff on the chlor_a
chlor_a_rate = tmp.diff(dim='time',n=1).chlor_a.to_series().reset_index()
# make the column to a proper name
chlor_a_rate.rename(columns={'chlor_a':'chl_rate'}, inplace='True')
chlor_a_rate
# merge the two dataframes {floatsDFAll_XDtimeorder; chlor_a_rate} into one dataframe based on the index {id, time} and use the left method
floatsDFAllRate_9Dtimeorder=pd.merge(floatsDFAll_9Dtimeorder,chlor_a_rate, on=['time','id'], how = 'left')
floatsDFAllRate_9Dtimeorder
# check
print('check the sum of the chlor_a before the merge', chlor_a_rate.chl_rate.sum())
print('check the sum of the chlor_a after the merge',floatsDFAllRate_9Dtimeorder.chl_rate.sum())
# visualize the chlorophyll rate, it is *better* to visualize at this scale
fig, ax = plt.subplots(figsize=(12,10))
floatsDFAllRate_9Dtimeorder.plot(kind='scatter', x='lon', y='lat', c='chl_rate', cmap='RdBu_r', vmin=-0.8, vmax=0.8, edgecolor='none', ax=ax)
# visualize the chlorophyll rate on the log scale
floatsDFAllRate_9Dtimeorder['chl_rate_log10'] = floatsDFAllRate_9Dtimeorder['chl_rate'].apply(scale)
floatsDFAllRate_9Dtimeorder
fig, ax = plt.subplots(figsize=(12,10))
floatsDFAllRate_9Dtimeorder.plot(kind='scatter', x='lon', y='lat', c='chl_rate_log10', cmap='RdBu_r', edgecolor='none', ax=ax)
#floatsDFAllRate_9Dtimeorder.chl_rate.dropna().shape # (1008,) data points
floatsDFAllRate_9Dtimeorder.chl_rate_log10.dropna().shape # (417,)data points..... notice, chl_rate can be negative, so do not take log10
check the sum of the chlor_a before the merge -64.3533472159612
check the sum of the chlor_a after the merge -64.3533472159612
Out[378]:
(417,)
In [49]:
pd.to_datetime(floatsDFAllRate_9Dtimeorder.time)
type(pd.to_datetime(floatsDFAllRate_9Dtimeorder.time))
ts = pd.Series(0, index=pd.to_datetime(floatsDFAllRate_9Dtimeorder.time) ) # creat a target time series for masking purpose
# take the month out
month = ts.index.month
# month.shape # a check on the shape of the month.
selector = ((11==month) | (12==month) | (1==month) | (2==month) | (3==month) )
selector
print('shape of the selector', selector.shape)
print('all the data count in [11-01, 03-31] is', floatsDFAllRate_9Dtimeorder[selector].chl_rate.dropna().shape) # total (672,)
print('all the data count is', floatsDFAllRate_9Dtimeorder.chl_rate.dropna().shape ) # total (1008,)
shape of the selector (147112,)
all the data count in [11-01, 03-31] is (672,)
all the data count is (1008,)
In [50]:
# histogram for non standarized data
axfloat = floatsDFAllRate_9Dtimeorder[selector].chl_rate.dropna().hist(bins=100,range=[-0.3,0.3])
axfloat.set_title('9-Day chl_rate')
Out[50]:
<matplotlib.text.Text at 0x447f5fe48>
In [51]:
# standarized series
ts = floatsDFAllRate_9Dtimeorder[selector].chl_rate.dropna()
ts_standardized = (ts - ts.mean())/ts.std()
axts = ts_standardized.hist(bins=100,range=[-0.3,0.3])
axts.set_title('9-Day standardized chl_rate')
Out[51]:
<matplotlib.text.Text at 0x11a8bf048>
In [52]:
# all the data
fig, axes = plt.subplots(nrows=8, ncols=2, figsize=(12, 10))
fig.subplots_adjust(hspace=0.05, wspace=0.05)
for i, ax in zip(range(2002,2017), axes.flat) :
tmpyear = floatsDFAllRate_9Dtimeorder[ (floatsDFAllRate_9Dtimeorder.time > str(i)) & (floatsDFAllRate_9Dtimeorder.time < str(i+1)) ] # if year i
#fig, ax = plt.subplots(figsize=(12,10))
print(tmpyear.chl_rate.dropna().shape) # total is 1001
tmpyear.plot(kind='scatter', x='lon', y='lat', c='chl_rate', cmap='RdBu_r',vmin=-0.6, vmax=0.6, edgecolor='none', ax=ax)
ax.set_title('year %g' % i)
# remove the extra figure
ax = plt.subplot(8,2,16)
fig.delaxes(ax)
(47,)
(45,)
(7,)
(38,)
(105,)
(92,)
(140,)
(31,)
(62,)
(16,)
(32,)
(28,)
(198,)
(105,)
(55,)
In [53]:
fig, axes = plt.subplots(nrows=7, ncols=2, figsize=(12, 10))
fig.subplots_adjust(hspace=0.05, wspace=0.05)
for i, ax in zip(range(2002,2016), axes.flat) :
tmpyear = floatsDFAllRate_9Dtimeorder[ (floatsDFAllRate_9Dtimeorder.time >= (str(i)+ '-11-01') ) & (floatsDFAllRate_9Dtimeorder.time <= (str(i+1)+'-03-31') ) ] # if year i
# select only particular month, Nov 1 to March 31
#fig, ax = plt.subplots(figsize=(12,10))
print(tmpyear.chl_rate.dropna().shape) # the total is 672
tmpyear.plot(kind='scatter', x='lon', y='lat', c='chl_rate', cmap='RdBu_r', vmin=-0.6, vmax=0.6, edgecolor='none', ax=ax)
ax.set_title('year %g' % i)
(58,)
(1,)
(9,)
(66,)
(44,)
(129,)
(28,)
(50,)
(5,)
(32,)
(0,)
(106,)
(91,)
(53,)
In [ ]:
In [ ]:
In [104]:
# let's output the data as a csv or hdf file to disk to save the experiment time
df_list = []
for i in range(2002,2017) :
tmpyear = floatsDFAllRate_9Dtimeorder[ (floatsDFAllRate_9Dtimeorder.time >= (str(i)+ '-11-01') ) & (floatsDFAllRate_9Dtimeorder.time <= (str(i+1)+'-03-31') ) ] # if year i
# select only particular month, Nov 1 to March 31
df_list.append(tmpyear)
df_tmp = pd.concat(df_list)
print('all the data count in [11-01, 03-31] is ', df_tmp.chl_rate.dropna().shape) # again, the total is (692,)
df_chl_out_9D_modisa = df_tmp[~df_tmp.chl_rate.isnull()] # only keep the non-nan values
#list(df_chl_out_XD.groupby(['id'])) # can see the continuity pattern of the Lagarangian difference for each float id
# output to a csv or hdf file
df_chl_out_9D_modisa.head()
all the data count in [11-01, 03-31] is (672,)
Out[104]:
id
time
temp
var_lat
var_tmp
ve
var_lon
vn
lon
spd
lat
chlor_a
chlor_a_log10
chl_rate
chl_rate_log10
3627
10206
2002-11-07
NaN
0.000494
1000.000000
-2.217083
0.001535
2.990778
67.132000
5.446583
11.126222
0.130267
-0.885166
-0.004264
NaN
3629
11089
2002-11-07
28.829472
0.000064
0.003812
-16.412472
0.000123
-3.991722
64.391056
17.995028
14.279667
0.197237
-0.705012
0.074821
-1.125976
3631
15707
2002-11-07
NaN
0.000074
1000.000000
-12.316611
0.000147
-18.253056
67.155306
24.656417
13.142667
0.152200
-0.817584
-0.004472
NaN
3649
34710
2002-11-07
28.448167
0.000069
0.001857
-2.827667
0.000135
19.539861
63.041861
20.774778
17.717111
0.372568
-0.428795
0.018603
-1.730417
3886
10206
2002-11-16
NaN
0.001033
1000.000000
-1.089083
0.003872
0.501111
67.029167
4.028889
11.179833
0.145233
-0.837935
0.014966
-1.824894
In [105]:
df_chl_out_9D_modisa.index.name = 'index' # make it specific for the index name
# CSV CSV CSV CSV with specfic index
df_chl_out_9D_modisa.to_csv('df_chl_out_9D_modisa.csv', sep=',', index_label = 'index')
# load CSV output
test = pd.read_csv('df_chl_out_9D_modisa.csv', index_col='index')
test.head()
Out[105]:
id
time
temp
var_lat
var_tmp
ve
var_lon
vn
lon
spd
lat
chlor_a
chlor_a_log10
chl_rate
chl_rate_log10
index
3627
10206
2002-11-07
NaN
0.000494
1000.000000
-2.217083
0.001535
2.990778
67.132000
5.446583
11.126222
0.130267
-0.885166
-0.004264
NaN
3629
11089
2002-11-07
28.829472
0.000064
0.003812
-16.412472
0.000123
-3.991722
64.391056
17.995028
14.279667
0.197237
-0.705012
0.074821
-1.125976
3631
15707
2002-11-07
NaN
0.000074
1000.000000
-12.316611
0.000147
-18.253056
67.155306
24.656417
13.142667
0.152200
-0.817584
-0.004472
NaN
3649
34710
2002-11-07
28.448167
0.000069
0.001857
-2.827667
0.000135
19.539861
63.041861
20.774778
17.717111
0.372568
-0.428795
0.018603
-1.730417
3886
10206
2002-11-16
NaN
0.001033
1000.000000
-1.089083
0.003872
0.501111
67.029167
4.028889
11.179833
0.145233
-0.837935
0.014966
-1.824894
In [ ]:
Content source: vyan2000/ocml-public
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