In [1]:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib as mpl
import netCDF4 as nc
import datetime as dt
from salishsea_tools import evaltools as et, places, viz_tools, visualisations
import xarray as xr
import pandas as pd
import pickle
import os
import bloomdrivers

%matplotlib inline

In [2]:

start=dt.datetime(2015,1,1)
end=dt.datetime(2015,3,31)
year=str(start.year)
modver='201812'
loc='S3'

savedir='/ocean/aisabell/MEOPAR/extracted_files'

fname=f'JanToMarch_TimeSeries_{year}_{loc}_{modver}.pkl'
savepath=os.path.join(savedir,fname)
recalc=False

In [3]:

loc='S3'

# lat and lon informatin for place:
lon,lat=places.PLACES['S3']['lon lat']
# get place information on SalishSeaCast grid:
ij,ii=places.PLACES['S3']['NEMO grid ji']
# GEM2.5 grid ji is atm forcing grid for ops files
jw,iw=places.PLACES['S3']['GEM2.5 grid ji']

fig, ax = plt.subplots(1,1,figsize = (6,6))
with nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc') as grid:
    viz_tools.plot_coastline(ax, grid, coords ='map', isobath=.1)
ax.plot(lon, lat, '.', markersize=14, color='red')
ax.set_ylim(48,50)
ax.set_xlim(-125,-122)
ax.set_title('Location of Station S3')
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')

Out[3]:

Text(0, 0.5, 'Latitude')

In [ ]:

if recalc==True or not os.path.isfile(savepath):
    basedir='/results/SalishSea/nowcast-green.201812/'
    nam_fmt='nowcast'
    flen=1 # files contain 1 day of data each
    ftype= 'ptrc_T' # load bio files
    tres=24 # 1: hourly resolution; 24: daily resolution  
    flist=et.index_model_files(start,end,basedir,nam_fmt,flen,ftype,tres)
    flist3 = et.index_model_files(start,end,basedir,nam_fmt,flen,"grid_T",tres)
    fliste3t = et.index_model_files(start,end,basedir,nam_fmt,flen,"carp_T",tres)

    ik=0
    with xr.open_mfdataset(flist['paths']) as bio:
        bio_time=np.array(bio.time_centered[:])
        sno3=np.array(bio.nitrate.isel(deptht=ik,y=ij,x=ii))
        sdiat=np.array(bio.diatoms.isel(deptht=ik,y=ij,x=ii))
        sflag=np.array(bio.flagellates.isel(deptht=ik,y=ij,x=ii))
        scili=np.array(bio.ciliates.isel(deptht=ik,y=ij,x=ii))
        no3_alld=np.array(bio.nitrate.isel(y=ij,x=ii)) 
        diat_alld=np.array(bio.diatoms.isel(y=ij,x=ii))
        flag_alld=np.array(bio.flagellates.isel(y=ij,x=ii))
        cili_alld=np.array(bio.ciliates.isel(y=ij,x=ii))
        microzoo=np.array(bio.microzooplankton.isel(y=ij,x=ii))
        mesozoo=np.array(bio.mesozooplankton.isel(y=ij,x=ii))
        no3_past250m=np.array(bio.nitrate.isel(deptht=32,y=ij,x=ii))
        no3_30to90m=np.array(bio.nitrate.isel(deptht=slice(21,26),y=ij,x=ii))
        
        with xr.open_mfdataset(fliste3t['paths']) as carp:
            intdiat=np.array(np.sum(bio.diatoms.isel(y=ij,x=ii)*carp.e3t.isel(y=ij,x=ii),1)) # depth integrated diatom
            intphyto=np.array(np.sum((bio.diatoms.isel(y=ij,x=ii)+bio.flagellates.isel(y=ij,x=ii)\
                            +bio.ciliates.isel(y=ij,x=ii))*carp.e3t.isel(y=ij,x=ii),1))
            spar=np.array(carp.PAR.isel(deptht=ik,y=ij,x=ii))
            intmesoz=np.array(np.sum(bio.mesozooplankton.isel(y=ij,x=ii)*carp.e3t.isel(y=ij,x=ii),1))
            intmicroz=np.array(np.sum(bio.microzooplankton.isel(y=ij,x=ii)*carp.e3t.isel(y=ij,x=ii),1))
            intzoop=np.array(np.sum((bio.mesozooplankton.isel(y=ij,x=ii)+bio.microzooplankton.isel(y=ij,x=ii))*carp.e3t.isel(y=ij,x=ii),1))
    fracdiat=intdiat/intphyto # depth integrated fraction of diatoms

    zoop=microzoo+mesozoo
    sphyto=sdiat+sflag+scili
    phyto_alld=diat_alld+flag_alld+cili_alld
    percdiat=sdiat/sphyto # percent diatoms
    
    with xr.open_mfdataset(flist3['paths']) as grid:
        grid_time=np.array(grid.time_centered[:])
        temp=np.array(grid.votemper.isel(deptht=ik,y=ij,x=ii)) #surface temperature
        salinity=np.array(grid.vosaline.isel(deptht=ik,y=ij,x=ii)) #surface salinity

    opsdir='/results/forcing/atmospheric/GEM2.5/operational'

    flist2=et.index_model_files(start,end,opsdir,nam_fmt='ops',flen=1,ftype='None',tres=24)
    with xr.open_mfdataset(flist2['paths']) as winds:
        u_wind=np.array(winds.u_wind.isel(y=jw,x=iw))
        v_wind=np.array(winds.v_wind.isel(y=jw,x=iw))
        twind=np.array(winds.time_counter)
        solar=np.array(winds.solar.isel(y=jw,x=iw))
    # wind speed:
    wspeed=np.sqrt(u_wind**2 + v_wind**2)
    # wind direction in degrees from east
    d = np.arctan2(v_wind, u_wind)
    winddirec=np.rad2deg(d + (d < 0)*2*np.pi)

    # reading Fraser river flow files
    dfFra=pd.read_csv('/ocean/eolson/MEOPAR/obs/ECRivers/Flow/FraserHopeDaily__Dec-2-2020_10_31_05PM.csv',
                      skiprows=1)
    # the original file contains both flow and water level information in the same field (Value)
    # keep only the flow data, where PARAM=1 (drop PARAM=2 values, water level data)
    # flow units are m3/s
    # DD is YD, year day (ie. 1 is jan 1)
    dfFra.drop(dfFra.loc[dfFra.PARAM==2].index,inplace=True)  

    # rename 'Value' column to 'Flow' now that we have removed all the water level rows
    dfFra.rename(columns={'Value':'Flow'}, inplace=True) 
        # inplace=True does this function on the orginal dataframe

    # no time information so use dt.date
    dfFra['Date']=[dt.date(iyr,1,1)+dt.timedelta(days=idd-1) for iyr, idd in zip(dfFra['YEAR'],dfFra['DD'])]
    # taking the value from the yr column, jan1st date, and making jan1 column to be 1 not 0
    dfFra.head(2)

    # select portion of dataframe in desired date range
    dfFra2=dfFra.loc[(dfFra.Date>=start.date())&(dfFra.Date<=end.date())]
    riv_time=dfFra2['Date'].values
    rivFlow=dfFra2['Flow'].values
    # could also write dfFra['Date'], sometimes this is required
    # newstart is a datetime object, so we convert it to just a date with .date
    pickle.dump((bio_time,sno3,sdiat,sflag,scili,diat_alld,no3_alld,flag_alld,cili_alld,phyto_alld,intdiat,intphyto,zoop,microzoo,mesozoo,spar,fracdiat,sphyto,percdiat,
            grid_time,salinity,temp,u_wind,v_wind,twind,solar,wspeed,winddirec,riv_time,rivFlow),no3_past250m,no3_30to90m,intmesoz,intmicroz,intzoop,open(savepath,'wb'))
else:
    bio_time,sno3,sdiat,sflag,scili,diat_alld,no3_alld,flag_alld,cili_alld,phyto_alld,intdiat,intphyto,zoop,microzoo,mesozoo,spar,fracdiat,sphyto,percdiat,\
            grid_time,salinity,temp,u_wind,v_wind,twind,solar,wspeed,winddirec,riv_time,rivFlow,no3_past250m,no3_30to90m,intmesoz,intmicroz,intzoop=pickle.load(open(savepath,'rb'))

basedir='/results/SalishSea/nowcast-green.201812/' nam_fmt='nowcast' flen=1 # files contain 1 day of data each ftype= 'ptrc_T' # load bio files tres=24 # 1: hourly resolution; 24: daily resolution
flist=et.index_model_files(start,end,basedir,nam_fmt,flen,ftype,tres) with xr.open_mfdataset(flist['paths']) as bio2: no3_past_250m=np.array(bio2.nitrate.sel(deptht=slice('250'),y=ij,x=ii)) no3_30m_90m=np.array(bio2.nitrate.sel(deptht=slice('30','90'),y=ij,x=ii)) no3_3m=np.array(bio2.nitrate.sel(deptht=slice('0','3'),y=ij,x=ii)) diat_3m=np.array(bio2.diatoms.sel(deptht=slice('0','3'),y=ij,x=ii)) flag_3m=np.array(bio2.flagellates.sel(deptht=slice('0','3'),y=ij,x=ii)) cili_3m=np.array(bio2.ciliates.sel(deptht=slice('0','3'),y=ij,x=ii))

In [8]:

# loop through years
years=list()
bloomtime1=list()
bloomtime2=list()
bloomtime3=list()
for year in range(2015,2020):
    fname=f'springTimeSeries_{str(year)}_{loc}_{modver}.pkl'
    savepath=os.path.join(savedir,fname)
    bio_time,sno3,sdiat,sflag,scili,diat_alld,no3_alld,flag_alld,cili_alld,phyto_alld,intdiat,intphyto,spar,fracdiat,sphyto,percdiat,\
            u_wind,v_wind,twind,solar,wspeed,winddirec,riv_time,rivFlow=pickle.load(open(savepath,'rb'))
    # put code that calculates bloom timing here
    bt1=bloomdrivers.metric1_bloomtime(phyto_alld,no3_alld,bio_time)
    bt2=bloomdrivers.metric2_bloomtime(sphyto,sno3,bio_time)
    bt3=bloomdrivers.metric3_bloomtime(sphyto,sno3,bio_time)
    years.append(year)
    bloomtime1.append(bt1)
    bloomtime2.append(bt2)
    bloomtime3.append(bt3)   
years=np.array(years)
bloomtime1=np.array(bloomtime1)
bloomtime2=np.array(bloomtime2)
bloomtime3=np.array(bloomtime3)

Combine separate year files into arrays:¶

In [9]:

# loop through years

years=list()
windjan=list()
windfeb=list()
windmar=list()
solarjan=list()
solarfeb=list()
solarmar=list()
parjan=list()
parfeb=list()
parmar=list()
tempjan=list()
tempfeb=list()
tempmar=list()
saljan=list()
salfeb=list()
salmar=list()
fraserjan=list()
fraserfeb=list()
frasermar=list()
zoojan=list()
zoofeb=list()
zoomar=list()

for year in range(2015,2020):
    fname=f'JanToMarch_TimeSeries_{year}_{loc}_{modver}.pkl'
    savepath=os.path.join(savedir,fname)
    bio_time,sno3,sdiat,sflag,scili,diat_alld,no3_alld,flag_alld,cili_alld,phyto_alld,intdiat,intphyto,zoop,microzoo,mesozoo,spar,fracdiat,sphyto,percdiat,\
            grid_time,salinity,temp,u_wind,v_wind,twind,solar,wspeed,winddirec,riv_time,rivFlow=pickle.load(open(savepath,'rb'))
    # put code that calculates drivers here
    wind=bloomdrivers.janfebmar_wspeed3(twind,wspeed)
    solar=bloomdrivers.janfebmar_irradiance(twind,solar)
    par=bloomdrivers.janfebmar_spar(bio_time,spar)
    temp=bloomdrivers.janfebmar_temp(grid_time,temp)
    sal=bloomdrivers.janfebmar_temp(grid_time,salinity)
    if year != 2019:
        fraser=bloomdrivers.janfebmar_fraserflow(riv_time,rivFlow)
        fraserjan.append(fraser[0])
        fraserfeb.append(fraser[1])
        frasermar.append(fraser[2])
    else:
        pass
    zoo=bloomdrivers.janfebmar_zooplankton(bio_time,zoop)
    years.append(year)
    windjan.append(wind[0])
    windfeb.append(wind[1])
    windmar.append(wind[2])   
    solarjan.append(solar[0])
    solarfeb.append(solar[1])
    solarmar.append(solar[2])
    parjan.append(par[0])
    parfeb.append(par[1])
    parmar.append(par[2])
    tempjan.append(temp[0])
    tempfeb.append(temp[1])
    tempmar.append(temp[2])
    saljan.append(sal[0])
    salfeb.append(sal[1])
    salmar.append(sal[2])
    
    zoojan.append(zoo[0])
    zoofeb.append(zoo[1])
    zoomar.append(zoo[2])

years=np.array(years)
windjan=np.array(windjan)
windfeb=np.array(windfeb)
windmar=np.array(windmar)
solarjan=np.array(solarjan)
solarfeb=np.array(solarfeb)
solarmar=np.array(solarmar)
parjan=np.array(parjan)
parfeb=np.array(parfeb)
parmar=np.array(parmar)
tempjan=np.array(tempjan)
tempfeb=np.array(tempfeb)
tempmar=np.array(tempmar)
saljan=np.array(saljan)
salfeb=np.array(salfeb)
salmar=np.array(salmar)
fraserjan=np.array(fraserjan)
fraserfeb=np.array(fraserfeb)
frasermar=np.array(frasermar)
zoojan=np.array(zoojan)
zoofeb=np.array(zoofeb)
zoomar=np.array(zoomar)

# get year day
yearday1=et.datetimeToYD(bloomtime1)  # convert to year day tool
yearday2=et.datetimeToYD(bloomtime2)
yearday3=et.datetimeToYD(bloomtime3)

Bloom date¶

In [12]:

# plot bloomtime for each year: 
fig,ax=plt.subplots(1,1,figsize=(7,5))
p1=ax.plot(years,yearday1, 'o',color='firebrick',label='Metric1')
p2=ax.plot(years,yearday2, 'o',color='b',label='Metric2')
p3=ax.plot(years,yearday3, 'o',color='orange',label='Metric3')
ax.set_ylabel('Year Day')
ax.set_xlabel('Year')
ax.set_title('Spring Phytoplankton Bloom Date by Year')
ax.set_xticks([2015,2016,2017,2018,2019])
ax.legend(handles=[p1[0],p2[0],p3[0]],loc='upper left')

Out[12]:

<matplotlib.legend.Legend at 0x7f2e24582cd0>

Monthly average wind speed cubed (January-March)¶

In [15]:

fig,ax=plt.subplots(1,1,figsize=(12,5))
p1=ax.plot(years,windjan, '-',color='green',label='January Average')
p2=ax.plot(years,windfeb, '-',color='c',label='February Average')
p3=ax.plot(years,windmar, '-',color='orchid',label='March Average')
ax.set_ylabel('Wind Speed Cubed ($\mathregular{m^3}$/$\mathregular{s^3}$)')
ax.set_xlabel('Year')
ax.set_title('Wind Speed Cubed by Year')
ax.set_xticks([2015,2016,2017,2018,2019])
ax.legend(handles=[p1[0],p2[0],p3[0]],loc='upper left')

ax1=ax.twinx()
p4=ax1.plot(years,yearday1, 'o',color='firebrick',label='Metric1')
p5=ax1.plot(years,yearday2, 'o',color='b',label='Metric2')
p6=ax1.plot(years,yearday3, 'o',color='orange',label='Metric3')
ax1.set_ylabel('Year Day')
ax1.legend(handles=[p4[0],p5[0],p6[0]],loc='upper right')

Out[15]:

<matplotlib.legend.Legend at 0x7f5c336b27f0>

Monthly average solar radiation (January-March)¶

In [28]:

fig,ax=plt.subplots(1,1,figsize=(12,5))
p1=ax.plot(years,solarjan, '-',color='green',label='January Average')
p2=ax.plot(years,solarfeb, '-',color='c',label='February Average')
p3=ax.plot(years,solarmar, '-',color='orchid',label='March Average')
ax.set_ylabel('Solar Irradiance (W/$\mathregular{m^2}$)')
ax.set_xlabel('Year')
ax.set_title('Solar Irradiance by Year')
ax.set_xticks([2015,2016,2017,2018,2019])
ax.legend(handles=[p1[0],p2[0],p3[0]],loc='upper left')

ax1=ax.twinx()
p4=ax1.plot(years,yearday1, 'o',color='firebrick',label='Metric1')
p5=ax1.plot(years,yearday2, 'o',color='b',label='Metric2')
p6=ax1.plot(years,yearday3, 'o',color='orange',label='Metric3')
ax1.set_ylabel('Year Day')
ax1.legend(handles=[p4[0],p5[0],p6[0]],loc='upper right')

Out[28]:

<matplotlib.legend.Legend at 0x7f2e1dc28f10>

Monthly average photosynthetically available radiation (PAR) (January-March)¶

In [12]:

fig,ax=plt.subplots(1,1,figsize=(12,5))
p1=ax.plot(years,parjan, '-',color='green',label='January Average')
p2=ax.plot(years,parfeb, '-',color='c',label='February Average')
p3=ax.plot(years,parmar, '-',color='orchid',label='March Average')
ax.set_ylabel('PAR (W/$\mathregular{m^2}$)')
ax.set_xlabel('Year')
ax.set_title('Model PAR by Year')
ax.set_xticks([2015,2016,2017,2018,2019])
ax.legend(handles=[p1[0],p2[0],p3[0]],loc='upper left')

ax1=ax.twinx()
p4=ax1.plot(years,yearday1, 'o',color='firebrick',label='Metric1')
p5=ax1.plot(years,yearday2, 'o',color='b',label='Metric2')
p6=ax1.plot(years,yearday3, 'o',color='orange',label='Metric3')
ax1.set_ylabel('Year Day')
ax1.legend(handles=[p4[0],p5[0],p6[0]],loc='center right')

Out[12]:

<matplotlib.legend.Legend at 0x7f5c380b5cd0>

Monthly average surface temperature (January-March)¶

In [13]:

fig,ax=plt.subplots(1,1,figsize=(12,5))
p1=ax.plot(years,tempjan, '-',color='green',label='January Average')
p2=ax.plot(years,tempfeb, '-',color='c',label='February Average')
p3=ax.plot(years,tempmar, '-',color='orchid',label='March Average')
ax.set_ylabel('Surface Temperature ($^\circ$C)')
ax.set_xlabel('Year')
ax.set_title('Surface Conservative Temperature by Year')
ax.set_xticks([2015,2016,2017,2018,2019])
ax.legend(handles=[p1[0],p2[0],p3[0]],loc='upper right')

ax1=ax.twinx()
p4=ax1.plot(years,yearday1, 'o',color='firebrick',label='Metric1')
p5=ax1.plot(years,yearday2, 'o',color='b',label='Metric2')
p6=ax1.plot(years,yearday3, 'o',color='orange',label='Metric3')
ax1.set_ylabel('Year Day')
ax1.legend(handles=[p4[0],p5[0],p6[0]],loc='upper left')

Out[13]:

<matplotlib.legend.Legend at 0x7f5c39401640>

Monthly average surface salinity (January-March)¶

In [14]:

fig,ax=plt.subplots(1,1,figsize=(12,5))
p1=ax.plot(years,saljan, '-',color='green',label='January Average')
p2=ax.plot(years,salfeb, '-',color='c',label='February Average')
p3=ax.plot(years,salmar, '-',color='orchid',label='March Average')
ax.set_ylabel('Salinity (g/kg)')
ax.set_xlabel('Year')
ax.set_title('Absolute Surface Salinity by Year')
ax.set_xticks([2015,2016,2017,2018,2019])
ax.legend(handles=[p1[0],p2[0],p3[0]],loc='upper left')

ax1=ax.twinx()
p4=ax1.plot(years,yearday1, 'o',color='firebrick',label='Metric1')
p5=ax1.plot(years,yearday2, 'o',color='b',label='Metric2')
p6=ax1.plot(years,yearday3, 'o',color='orange',label='Metric3')
ax1.set_ylabel('Year Day')
ax1.legend(handles=[p4[0],p5[0],p6[0]],loc='upper right')

Out[14]:

<matplotlib.legend.Legend at 0x7f5c3371cf40>

Monthly average Fraser river flow (January-March)¶

In [10]:

yearnot19=years[0:4]
fig,ax=plt.subplots(1,1,figsize=(12,5))
p1=ax.plot(yearnot19,fraserjan, '-',color='green',label='January Average')
p2=ax.plot(yearnot19,fraserfeb, '-',color='c',label='February Average')
p3=ax.plot(yearnot19,frasermar, '-',color='orchid',label='March Average')
ax.set_ylabel('Flow (m$^3$s$^{-1}$)')
ax.set_xlabel('Year')
ax.set_title('Fraser River Flow by Year')
ax.set_xticks([2015,2016,2017,2018,2019])
ax.legend(handles=[p1[0],p2[0],p3[0]],loc='upper right')

ax1=ax.twinx()
p4=ax1.plot(years,yearday1, 'o',color='firebrick',label='Metric1')
p5=ax1.plot(years,yearday2, 'o',color='b',label='Metric2')
p6=ax1.plot(years,yearday3, 'o',color='orange',label='Metric3')
ax1.set_ylabel('Year Day')
ax1.legend(handles=[p4[0],p5[0],p6[0]],loc='upper left')

Out[10]:

<matplotlib.legend.Legend at 0x7f5c38221520>

Monthly average surface zooplankton concentration (January-March)¶

In [11]:

fig,ax=plt.subplots(1,1,figsize=(12,5))
p1=ax.plot(years,zoojan, '-',color='green',label='January Average')
p2=ax.plot(years,zoofeb, '-',color='c',label='February Average')
p3=ax.plot(years,zoomar, '-',color='orchid',label='March Average')
ax.set_ylabel('Zooplankon Concentration ($\mu$M N)')
ax.set_xlabel('Year')
ax.set_title('Surface Zooplankon Concentration by Year')
ax.set_xticks([2015,2016,2017,2018,2019])
ax.legend(handles=[p1[0],p2[0],p3[0]],loc='center right')

ax1=ax.twinx()
p4=ax1.plot(years,yearday1, 'o',color='firebrick',label='Metric1')
p5=ax1.plot(years,yearday2, 'o',color='b',label='Metric2')
p6=ax1.plot(years,yearday3, 'o',color='orange',label='Metric3')
ax1.set_ylabel('Year Day')
ax1.legend(handles=[p4[0],p5[0],p6[0]],loc='upper left')

Out[11]:

<matplotlib.legend.Legend at 0x7f5c38211640>

Nitrate¶

mid-depth nitrate, say 30-90m (try for each location)

January nitrate concentration in deep SoG (below 250 m)

In [13]:

no3_alld

Out[13]:

array([[23.515175  , 23.554594  , 23.603527  , ...,  0.        ,
         0.        ,  0.        ],
       [23.571758  , 23.57355   , 23.580078  , ...,  0.        ,
         0.        ,  0.        ],
       [24.198307  , 24.19861   , 24.19911   , ...,  0.        ,
         0.        ,  0.        ],
       ...,
       [ 0.08288033,  0.08332632,  0.09127697, ...,  0.        ,
         0.        ,  0.        ],
       [ 0.0371336 ,  0.05146486,  0.09865781, ...,  0.        ,
         0.        ,  0.        ],
       [ 0.03236723,  0.04070939,  0.07257345, ...,  0.        ,
         0.        ,  0.        ]], dtype=float32)

In [14]:

# b) get average no3 in upper 3m
no3_alld_df=pd.DataFrame(no3_alld)
no3_alld_df
#upper_3m_no3=no3_alld_df[250:]
#upper_3m_no3.columns=['upper_3m_no3']
#upper_3m_no3

Out[14]:

	0	1	2	3	4	5	6	7	8	9	...	30	31	32	33	34	35	36	37	38	39
0	23.515175	23.554594	23.603527	23.658932	23.707211	23.756565	23.807390	23.850113	23.888948	23.920668	...	28.655493	28.705704	28.729893	28.730452	0.0	0.0	0.0	0.0	0.0	0.0
1	23.571758	23.573549	23.580078	23.618185	23.680647	23.745729	23.813982	23.873266	23.940020	24.006979	...	28.643595	28.714565	28.755180	28.758980	0.0	0.0	0.0	0.0	0.0	0.0
2	24.198307	24.198610	24.199110	24.199781	24.200630	24.201689	24.203051	24.204964	24.208244	24.215879	...	28.698906	28.769850	28.798454	28.796621	0.0	0.0	0.0	0.0	0.0	0.0
3	23.992994	23.993858	23.995405	23.997711	24.000687	24.005335	24.012848	24.026379	24.054794	24.113146	...	28.746355	28.841621	28.862658	28.863733	0.0	0.0	0.0	0.0	0.0	0.0
4	23.666676	23.671400	23.684618	23.702757	23.719288	23.748533	23.772276	23.797523	23.830030	23.879538	...	28.781496	28.848213	28.865246	28.884823	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
85	0.249139	0.250048	0.272372	0.296945	0.357909	0.512107	0.829846	1.325657	2.913785	7.108970	...	27.519426	28.470781	28.744110	28.939680	0.0	0.0	0.0	0.0	0.0	0.0
86	0.178986	0.179665	0.184106	0.206280	0.309262	0.544323	1.075272	2.377133	5.026740	7.730538	...	27.412582	28.532671	28.886221	29.026134	0.0	0.0	0.0	0.0	0.0	0.0
87	0.082880	0.083326	0.091277	0.121230	0.205495	0.476379	0.855857	1.934850	4.332655	7.406206	...	27.412470	28.485342	28.976994	29.122942	0.0	0.0	0.0	0.0	0.0	0.0
88	0.037134	0.051465	0.098658	0.148937	0.247649	0.422142	0.922184	2.178910	4.146522	6.751084	...	27.533159	28.511713	28.937258	29.117840	0.0	0.0	0.0	0.0	0.0	0.0
89	0.032367	0.040709	0.072573	0.182095	0.308702	0.472839	1.113250	2.759563	5.734659	8.726698	...	27.520538	28.699280	29.013016	29.156851	0.0	0.0	0.0	0.0	0.0	0.0

90 rows × 40 columns

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