# Import NumPy, Pandas, xarray, Matplotlib, and datetime at the top of your code
import numpy as np
import pandas as pd
import xarray as xr
import matplotlib.pyplot as plt
from datetime import datetime, timedelta

# Download netCDF4 library
!pip install netcdf4  # You can comment this out once it has run

# Give Colab access to Google Drive
from google.colab import drive
drive.mount('/content/drive')

# Filepath for World Ocean Atlas 2018 (WOA18) temperature netCDF file

# Note: you may need to change this to match your own filepath,
# which you can get by opening the left sidebar (folder icon),
# navigating to the file, clicking the "..." on the file, and
# selecting "Copy path"
filepath = '/content/drive/My Drive/OCEAN 215 - Autumn \'20/OCEAN 215 - Autumn \'20 - Course documents/Zoom class slides and notebooks/2020-11-10 - class #11 - data/woa18_temp.nc'

# Load the data file from Google Drive as an xarray Dataset
temp = xr.open_dataset(filepath)

# View data and stats
display(temp)

# Answers
# a. 2 variables
# b. Average temperature and number of observations per grid cell
# c. 1960-2011
# d. 1°
# e. 5500 m

# Write code here:
# -35°N, 110°E
temp.sel(depth=0,lat=-35,lon=110,time=datetime(2011,1,1),method='nearest')['t_an'].item()

time_series = temp.sel(depth=0,lat=46,lon=-128,method='nearest')['t_an']
display(time_series)

depth_profile = temp.sel(lat=46,lon=-128,time=datetime(2011,1,1),method='nearest')['t_an']
display(depth_profile)

fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize=(14,5))

ax1.plot(time_series['time'].values,time_series,c='k')
ax1.set_xlabel('Time')
ax1.set_ylabel('Temperature (°C)')
ax1.set_title('Time series')
ax1.grid()

ax2.plot(depth_profile.values,depth_profile['depth'],c='k')
ax2.invert_yaxis()
ax2.set_xlabel('Temperature (°C)')
ax2.set_ylabel('Depth (m)')
ax2.set_title('Profile')
ax2.grid()

temperature = temp['t_an'].values.flatten()
print(temperature.shape)

fig = plt.figure(figsize=(10,10))
p = plt.hist(temperature,bins=50, rwidth=0.85)

temp_ave = np.nanmean(temperature)
temp_std = np.nanstd(temperature)


plt.plot([temp_ave,temp_ave],[0,2.7e6],c='k',lw=2)
plt.text(temp_ave,1.5e6,'Average='+str(round(temp_ave,2))+'˚C')
plt.plot([temp_ave+temp_std,temp_ave+temp_std],[0,2.7e6],c ='k',ls='--',lw=2)
plt.plot([temp_ave-temp_std,temp_ave-temp_std],[0,2.7e6],c ='k',ls='--',lw=2)

plt.grid()
plt.ylim((0,2.7e6))
plt.xlabel('Temperatures ˚C',fontsize=14)
plt.ylabel('Counts',fontsize=14)
plt.title('WOA temperatures',fontsize=14)

plt.savefig('drive/My Drive/WOA_hist.png')

# Import statements and drive mount
import numpy as np
import matplotlib.pyplot as pl
from google.drive import drive
drive.mount('/content/drive')

# Where the file is and its name
folderpath= '/content/drive/My Drive/Data_folder/'
filename = 'Dissolved Inorganic Nutrients.csv

# Read the data
data = pd.read_csv(folderpath + filname, parse_dates=[1], na_values=-999)
data.describe

# Clean the data and take averages for each day of data
nitrate = data['Nitrite and Nitrate (µmol/L)']
data['Nitrite and Nitrate (µmol/L)'] = nitrate.where(nitrate>0)

cleandata = data.groupby('Datetime GMT').mean

# Assign variable names and change to np arrays
P = cleandata['Phosphate (µmol/L)'.values
S = clandata['Silicate (µmol/L)'].values
N = cleandata["Nitrite and Nitrate (µmol/L)'].values
dates = cleandata.index.values

# Make a figure and plot the data
fig = plt.figure(figsize=(10,10))
ax.plot(data,S,'s-',c='mediumblue',label='S')
ax.plot(dates,N,'*-',c='darkgreen',label='N')
ax.plot(datas,P,'o-',c='firebrick',label='P')

# Format the figure
plt.set_xlabel('Dates')
plt.set_ylabel('Concentration (µmol/L)")
plt.title('Inorganic nutrients')
plt.grid()
plt.yscale('log')
plt.gca.legend()

# Save the figure
plt.savefig(folderpath 'lineplots.png')