import numpy as np
import netCDF4 as nc
import matplotlib.pyplot as plt
from salishsea_tools import (tidetools, geo_tools, viz_tools)
import numpy.ma as ma
import pandas as pd
import datetime
import pytz
import os
%matplotlib inline
from IPython.display import HTML
HTML('''<script>
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$('div.input').hide();
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<form action="javascript:code_toggle()"><input type="submit" value="Click here to toggle on/off the raw code."></form>''')
grid = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')
bathy, X, Y = tidetools.get_bathy_data(grid)
mesh = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/mesh_mask201702.nc')
HINDCAST_PATH= '/results/SalishSea/nowcast-green/'
f = pd.read_csv('/ocean/eolson/MEOPAR/obs/PSFCitSci/2016ChlorophyllChlData.csv')
g = pd.read_csv('/ocean/eolson/MEOPAR/obs/PSFCitSci/2016ChlorophyllStationData.csv')
g['DateCollected'] = g['DateCollected '].values
w = pd.merge(f, g, on = ['Station', 'DateCollected', 'Depth_m'])
cs_table = w[w.quality_flag != 4]
cs_table = cs_table.drop_duplicates()
local = pytz.timezone ("America/Los_Angeles")
cs_table.head()
DateCollected | ShipBoat | Station | Depth_m | Chla_ugL | MeanChla_ugL | CV | quality_flag | Phaeophytin_ugL | MeanPhaeophytin_ugL | DateCollected | TimeCollected | Latitude | Longitude | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 02/18/2016 | NaN | PR-8 | 5 | 0.63 | 0.59 | 11.3 | 0 | 0.47 | 0.43 | 02/18/2016 | 9:59:00 AM | 49.92167 | -124.92000 |
2 | 02/18/2016 | JAS | LND-3 | 5 | 0.62 | 0.65 | 6.7 | 0 | 0.36 | 0.37 | 02/18/2016 | 10:51:00 AM | 50.02000 | -124.88167 |
3 | 02/22/2016 | MRN | IS-2 | 5 | 0.55 | 0.65 | 22.3 | 3 | 0.50 | 0.62 | 02/22/2016 | NaN | 49.63667 | -124.08333 |
5 | 02/26/2016 | STB | NQ-1 | 5 | 0.90 | 0.87 | 4.2 | 0 | 0.41 | 0.42 | 02/26/2016 | 2:27:00 PM | 49.38833 | -124.35833 |
6 | 03-03-2016 | --- | GO-5 | 5 | 0.79 | 0.73 | 10.7 | 0 | 0.36 | 0.34 | 03-03-2016 | 4:16:00 PM | 48.94500 | -123.32000 |
list_of_lons = np.array([])
list_of_lats = np.array([])
list_of_datetimes = np.array([])
list_of_cs_chl = np.array([])
list_of_model_chl = np.array([])
for n in cs_table.index:
Yind, Xind = geo_tools.find_closest_model_point(cs_table.Longitude[n],
cs_table.Latitude[n],
X, Y, land_mask = bathy.mask)
if mesh.variables['tmask'][0,4,Yind, Xind] == 1:
if type(cs_table.TimeCollected[n]) != float:
try:
local_datetime = (datetime.datetime.combine(datetime.datetime.strptime(
cs_table.DateCollected[n], '%m/%d/%Y'),
datetime.datetime.strptime(
cs_table.TimeCollected[n],
'%I:%M:%S %p').time()))
except (ValueError):
local_datetime = (datetime.datetime.combine(datetime.datetime.strptime(
cs_table.DateCollected[n], '%m-%d-%Y'),
datetime.datetime.strptime(
cs_table.TimeCollected[n],
'%I:%M:%S %p').time()))
date = local.localize(local_datetime, is_dst=True).astimezone(pytz.utc)
sub_dir = date.strftime('%d%b%y').lower()
datestr = date.strftime('%Y%m%d')
fname = 'SalishSea_1h_{}_{}_ptrc_T.nc'.format(datestr, datestr)
nuts = nc.Dataset(os.path.join(HINDCAST_PATH, sub_dir, fname))
if date.minute < 30:
before = datetime.datetime(year = date.year, month = date.month, day = date.day,
hour = (date.hour), minute = 30) - datetime.timedelta(hours=1)
after = before + datetime.timedelta(hours=1)
sub_dir2 = after.strftime('%d%b%y').lower()
datestr2 = after.strftime('%Y%m%d')
fname2 = 'SalishSea_1h_{}_{}_ptrc_T.nc'.format(datestr2, datestr2)
nuts2 = nc.Dataset(os.path.join(HINDCAST_PATH, sub_dir2, fname2))
delta = (date.minute + 30) / 60
chl_val = 1.6*((1-delta)*(nuts.variables['diatoms'][before.hour, 4, Yind, Xind]
+ nuts.variables['ciliates'][before.hour,4,Yind, Xind]
+ nuts.variables['flagellates'][before.hour,4,Yind,Xind]) +
(delta)*(nuts2.variables['diatoms'][after.hour, 4, Yind, Xind]
+ nuts2.variables['ciliates'][after.hour,4,Yind, Xind]
+ nuts2.variables['flagellates'][after.hour,4,Yind,Xind]))
if date.minute >= 30:
before = datetime.datetime(year = date.year, month = date.month, day = date.day,
hour = (date.hour), minute = 30)
after = before + datetime.timedelta(hours=1)
sub_dir2 = after.strftime('%d%b%y').lower()
datestr2 = after.strftime('%Y%m%d')
fname2 = 'SalishSea_1h_{}_{}_ptrc_T.nc'.format(datestr2, datestr2)
nuts2 = nc.Dataset(os.path.join(HINDCAST_PATH, sub_dir2, fname2))
delta = (date.minute) / 60
chl_val = 1.6*(delta*(nuts.variables['diatoms'][before.hour, 4, Yind, Xind]
+ nuts.variables['ciliates'][before.hour,4,Yind, Xind]
+ nuts.variables['flagellates'][before.hour,4,Yind,Xind]) +
(1- delta)*(nuts2.variables['diatoms'][after.hour, 4, Yind, Xind]
+ nuts2.variables['ciliates'][after.hour,4,Yind, Xind]
+ nuts2.variables['flagellates'][after.hour,4,Yind,Xind]))
list_of_lons = np.append(list_of_lons, cs_table.Longitude[n])
list_of_lats = np.append(list_of_lats, cs_table.Latitude[n])
list_of_datetimes = np.append(list_of_datetimes, date)
list_of_cs_chl = np.append(list_of_cs_chl, cs_table.MeanChla_ugL[n])
list_of_model_chl = np.append(list_of_model_chl, chl_val)
import matplotlib as mpl
mpl.rcParams['font.size'] = 12
mpl.rcParams['axes.titlesize'] = 12
fig, ax = plt.subplots(figsize = (8,8))
viz_tools.set_aspect(ax, coords = 'map')
ax.plot(list_of_lons, list_of_lats, 'ro')
viz_tools.plot_coastline(ax, grid, coords = 'map')
ax.set_ylim(48.5, 50.5)
ax.set_xlim(-125.5, -122.5);
list_of_cs_chl.shape
(99,)
fig, ax = plt.subplots(figsize = (8,8))
ax.plot(list_of_cs_chl, list_of_model_chl, 'b.', alpha = 0.5)
ax.plot(np.arange(0,25), color = 'grey')
ax.grid('on')
ax.set_title('Citizen Science Chl 2016, depth = 5m')
ax.set_xlabel('Citizen Science')
ax.set_ylabel('Nowcast-green');
print('bias = ' + str(-np.mean(list_of_cs_chl) + np.mean(list_of_model_chl)))
print('RMSE = ' + str(np.sqrt(np.sum((list_of_model_chl - list_of_cs_chl)**2) /
len(list_of_cs_chl))))
xbar = np.mean(list_of_cs_chl)
print('Willmott = ' + str(1-(np.sum((list_of_model_chl - list_of_cs_chl)**2) /
np.sum((np.abs(list_of_model_chl - xbar)
+ np.abs(list_of_cs_chl - xbar))**2))))
bias = 0.0512555971049 RMSE = 5.74398844744 Willmott = 0.523401348247
fig, ax = plt.subplots(figsize = (20,8))
ax.plot(list_of_lats, list_of_model_chl - list_of_cs_chl, 'ro', alpha =0.5)
ax.grid('on')
ax.set_xlabel('lat', fontsize = 15)
ax.set_ylabel('Model - Observed',fontsize = 15)
ax.set_title('Chl', fontsize = 20)
<matplotlib.text.Text at 0x7f717e0f2b00>
fig, ax = plt.subplots(figsize = (20,8))
ax.plot(list_of_lons, list_of_model_chl - list_of_cs_chl, 'ro', alpha =0.5)
ax.grid('on')
ax.set_xlabel('lon', fontsize = 15)
ax.set_ylabel('Model - Observed',fontsize = 15)
ax.set_title('Chl', fontsize = 20)
<matplotlib.text.Text at 0x7f717dfbf710>
fig, ax = plt.subplots(figsize = (20,8))
ax.plot(list_of_datetimes, list_of_model_chl - list_of_cs_chl, 'ro', alpha =0.5)
ax.grid('on')
ax.set_xlabel('Date', fontsize = 15)
ax.set_ylabel('Model - Observed',fontsize = 15)
ax.set_title('Chl', fontsize = 20)
<matplotlib.text.Text at 0x7f7179b54a20>
cs_table2 = cs_table[cs_table.quality_flag != 3]
list_of_lons2 = np.array([])
list_of_lats2 = np.array([])
list_of_datetimes2 = np.array([])
list_of_cs_chl2 = np.array([])
list_of_model_chl2 = np.array([])
for n in cs_table2.index:
Yind, Xind = geo_tools.find_closest_model_point(cs_table2.Longitude[n],
cs_table2.Latitude[n],
X, Y, land_mask = bathy.mask)
if mesh.variables['tmask'][0,4,Yind, Xind] == 1:
if type(cs_table2.TimeCollected[n]) != float:
try:
local_datetime = (datetime.datetime.combine(datetime.datetime.strptime(
cs_table2.DateCollected[n], '%m/%d/%Y'),
datetime.datetime.strptime(
cs_table2.TimeCollected[n],
'%I:%M:%S %p').time()))
except (ValueError):
local_datetime = (datetime.datetime.combine(datetime.datetime.strptime(
cs_table2.DateCollected[n], '%m-%d-%Y'),
datetime.datetime.strptime(
cs_table2.TimeCollected[n],
'%I:%M:%S %p').time()))
date = local.localize(local_datetime, is_dst=True).astimezone(pytz.utc)
sub_dir = date.strftime('%d%b%y').lower()
datestr = date.strftime('%Y%m%d')
fname = 'SalishSea_1h_{}_{}_ptrc_T.nc'.format(datestr, datestr)
nuts = nc.Dataset(os.path.join(HINDCAST_PATH, sub_dir, fname))
if date.minute < 30:
before = datetime.datetime(year = date.year, month = date.month, day = date.day,
hour = (date.hour), minute = 30) - datetime.timedelta(hours=1)
after = before + datetime.timedelta(hours=1)
sub_dir2 = after.strftime('%d%b%y').lower()
datestr2 = after.strftime('%Y%m%d')
fname2 = 'SalishSea_1h_{}_{}_ptrc_T.nc'.format(datestr2, datestr2)
nuts2 = nc.Dataset(os.path.join(HINDCAST_PATH, sub_dir2, fname2))
delta = (date.minute + 30) / 60
chl_val = 1.6*((1-delta)*(nuts.variables['diatoms'][before.hour, 4, Yind, Xind]
+ nuts.variables['ciliates'][before.hour,4,Yind, Xind]
+ nuts.variables['flagellates'][before.hour,4,Yind,Xind]) +
(delta)*(nuts2.variables['diatoms'][after.hour, 4, Yind, Xind]
+ nuts2.variables['ciliates'][after.hour,4,Yind, Xind]
+ nuts2.variables['flagellates'][after.hour,4,Yind,Xind]))
if date.minute >= 30:
before = datetime.datetime(year = date.year, month = date.month, day = date.day,
hour = (date.hour), minute = 30)
after = before + datetime.timedelta(hours=1)
sub_dir2 = after.strftime('%d%b%y').lower()
datestr2 = after.strftime('%Y%m%d')
fname2 = 'SalishSea_1h_{}_{}_ptrc_T.nc'.format(datestr2, datestr2)
nuts2 = nc.Dataset(os.path.join(HINDCAST_PATH, sub_dir2, fname2))
delta = (date.minute) / 60
chl_val = 1.6*(delta*(nuts.variables['diatoms'][before.hour, 4, Yind, Xind]
+ nuts.variables['ciliates'][before.hour,4,Yind, Xind]
+ nuts.variables['flagellates'][before.hour,4,Yind,Xind]) +
(1- delta)*(nuts.variables['diatoms'][after.hour, 4, Yind, Xind]
+ nuts.variables['ciliates'][after.hour,4,Yind, Xind]
+ nuts.variables['flagellates'][after.hour,4,Yind,Xind]))
list_of_lons2 = np.append(list_of_lons2, cs_table2.Longitude[n])
list_of_lats2 = np.append(list_of_lats2, cs_table2.Latitude[n])
list_of_datetimes2 = np.append(list_of_datetimes2, date)
list_of_cs_chl2 = np.append(list_of_cs_chl2, cs_table2.MeanChla_ugL[n])
list_of_model_chl2 = np.append(list_of_model_chl2, chl_val)
list_of_cs_chl2.shape
(73,)
fig, ax = plt.subplots(figsize = (5,5))
ax.plot(list_of_cs_chl2, list_of_model_chl2, 'b.', alpha = 0.5)
ax.plot(np.arange(0,25), color = 'grey')
ax.grid('on')
ax.set_title('Citizen Science Chl 2016, depth = 5m, quality flag != 3')
ax.set_xlabel('Citizen Science')
ax.set_ylabel('Nowcast-green');
print('bias = ' + str(-np.mean(list_of_cs_chl2) + np.mean(list_of_model_chl2)))
print('RMSE = ' + str(np.sqrt(np.sum((list_of_model_chl2 - list_of_cs_chl2)**2) /
len(list_of_cs_chl2))))
xbar = np.mean(list_of_cs_chl2)
print('Willmott = ' + str(1-(np.sum((list_of_model_chl2 - list_of_cs_chl2)**2) /
np.sum((np.abs(list_of_model_chl2 - xbar)
+ np.abs(list_of_cs_chl2 - xbar))**2))))
bias = 0.210582890445 RMSE = 5.93745094389 Willmott = 0.46304980325