import xray
ds = xray.open_dataset('https://motherlode.ucar.edu/repository/opendap/41f2b38a-4e70-4135-8ff8-dbf3d1dcbfc1/entry.das', 
                       decode_times=False)
print(ds)

"""
Explanation: netCDF File Visualization Case Study
I was asked by a colleague to visualize data contained within this netCDF file (OPeNDAP link) with Python. What follows is an exploration of how I achieved that objective. Because this exercise touches upon many technologies related to Unidata, it makes for an interesting case study. We will be meandering through,

netCDF
WMO GRIB metadata
Map projections
xray data analysis library
cartopy visualization library

Crack Open the File
To get our bearings let's see what there is inside our netCDF file. We will be using the xray library to dig inside our netCDF data. xray is similar to pandas, but for the Common Data Model. We could have just used the netcdf4-python library but xray has output that is more nicely formatted. Let's first import xray and open the dataset.
End of explanation
"""


print(ds['th'])

"""
Explanation: Dimensions, Coordinates, Data Variables
As far as the dimensions and coordinates go, the most relevant and important coordinates variables are x and y. We can see the data variables, such as, temperature (t), mixing ratio (mr), and potential temperature (th), are mostly on a 1901 x 1801 grid. There is also the mysterious nav dimension and associated data variables which we will be examining later.
Let's set a goal of visualizing potential temperature with the Cartopy plotting package.
The first step is to get more information concerning the variables we are interested in. For example, let's look at potential temperature or th.
End of explanation
"""


th = ds['th'].values[0][0]
print(th)

"""
Explanation: potential temperature (th)
Let's grab the data array for potential temperature (th).
End of explanation
"""


print(ds['grid_type_code'])

"""
Explanation: To Visualize the Data, We have to Decrypt the Projection
In order, to visualize the data that are contained within a two-dimensional array onto a map that represents a three-dimensional globe, we need to understand the projection of the data.
We can make an educated guess these are contained in the data variables with the nav cooridinate variable.
Specifically,

grid_type
grid_type_code
x_dim
y_dim
Nx
Ny
La1
Lo1
LoV
Latin1
Latin2
Dx
Dy

But what are these??
For Grins, Let's Scrutinize the grid_type_code
End of explanation
"""


print(ds['grid_type_code'].values[0])

"""
Explanation: Google to the Rescue
A simple Google search of GRIB-1 GDS data representation type takes us to
A GUIDE TO THE CODE FORM FM 92-IX Ext. GRIB Edition 1 from 1994 document. Therein one can find an explanation of the variables needed to understand the map projection. Let's review these variables.
End of explanation
"""


grid_type = ds['grid_type'].values
print('The grid type is ', grid_type[0])

"""
Explanation: What is grid_type_code of 5?
Let's look at Table 6 . A grid_type_code of 5 corresponds to a projection of Polar Stereographic.
Next up grid_type
End of explanation
"""


nx, ny = ds['Nx'].values[0], ds['Ny'].values[0]
print(nx, ny)


"""
Explanation: Uh oh! Polar Stereographic or Lambert Conformal??
Note that this newest piece of information relating to a Lambert Conformal projection disagrees with the  earlier projection information about a Polar Stereographic projection. There is a bug in the metadata description of the projection.
Moving on Anyway, next Nx and Ny
According to the grib documentation Nx and Ny represent the number grid points along the x and y axes. Let's grab those.
End of explanation
"""


la1, lo1 = ds['La1'].values[0], ds['Lo1'].values[0]
print(la1, lo1)


"""
Explanation: La1 and Lo1
Next let's get La1 and Lo1 which are defined as the "first grid points" These are probably the latitude and longitude for one of the corners of the grid.
End of explanation
"""


latin1, latin2 = ds['Latin1'].values[0], ds['Latin2'].values[0]
print(latin1, latin2)


"""
Explanation: Latin1 and Latin2
Next up are the rather mysteriously named Latin1 and Latin2 variables. When I first saw these identifiers, I thought they referred to a Unicode block, but in fact they relate to the secants of the projection cone. I do not know why they are called "Latin" and this name is confusing. At any rate, we can feel comfortable that we are dealing with Lambert Conformal rather than Polar Stereographic.

Credit: http://www.geo.hunter.cuny.edu/~jochen
End of explanation
"""


lov = ds['LoV'].values[0]
print(lov)


"""
Explanation: The Central Meridian for the Lambert Conformal Projection, LoV
If we are defining a Lambert Conformal projection, we will require the central meridian that the GRIB documentation refers to as LoV.
End of explanation
"""


print(ds['Dx'])
print(ds['Dy'])


"""
Explanation: Dx and Dy
Finally, let's look at the grid increments. In particular, we need to find the units.
End of explanation
"""


dx,dy = ds['Dx'].values[0],ds['Dy'].values[0]
print(dx,dy)

"""
Explanation: Units for Dx and Dy
The units for the deltas are in meters.
End of explanation
"""


%matplotlib inline

import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import matplotlib as mpl

"""
Explanation: Let's Review What We Have
We now have all the information we need to understand the Lambert projection:

The secants of the Lambert Conformal projection (Latin1, Latin2)
The central meridian of the projection (LoV)

Moreover, we have additional information that shows how the data grid relates to the projection:

The number of grid points in x and y (Nx, Ny)
The delta in meters between grid point (Dx, Dy)
The first latitude and longitude of the data (first latitude, first longitude).

We are Ready for Visualization (almost)!
Let's import cartopy and matplotlib.
End of explanation
"""


proj = ccrs.LambertConformal(central_longitude=lov,standard_parallels=(latin1,latin2))

"""
Explanation: Define the Lambert Conformal Projection with Cartopy
End of explanation
"""



pc = ccrs.PlateCarree()


"""
Explanation: Lambert Conformal Grid Extents

To plot the data we need the left,right,bottom,top extents of the grid expressed in Lambert Conformal
coordinates.
Key point: The projection coordinate systems have flat topology and Euclidean distance.

Calculating the Extents
Remember, we have:

The number of grid points in x and y (Nx, Ny)
The delta in meters between grid point (Dx, Dy)
The first latitude and longitude of the data (first latitude, first longitude).

We have one of the corners in latitude and longitude, but we need to convert it LC coordinates and derive the other corner.
Platte Carrée Projection
The Platte Carrée Projection is a very simple X,Y/Cartesian projection. It is used a lot in Cartopy because it allows you to express coordinates in familiar Latitude and Longitudes. Remember: The projection coordinate systems have flat topology and Euclidean distance.
Platte Carrée

Source: Wikipedia Source
Create the PlatteCarre Cartopy Projection
End of explanation
"""


left,bottom = proj.transform_point(lo1,la1,pc)
print(left,bottom)

"""
Explanation: Convert Corner from Lat/Lon PlatteCarre to LC
The transform_point method translates coordinates from one projection coordinate system to the other.
End of explanation
"""


right,top = left + nx*dx,bottom + ny*dy
print(right,top)

"""
Explanation: Derive Opposite Corner
Derive the opposite corner from the number of points and the delta. Again, we can do this because the projection coordinate systems have flat topology and Euclidean distance.
End of explanation
"""


#Define the figure
fig = plt.figure(figsize=(12, 12))

# Define the extents and add the data
ax = plt.axes(projection=proj)
extents = (left, right, bottom, top)
ax.contourf(th, origin='lower', extent=extents, transform=proj)

# Add bells and whistles
ax.coastlines(resolution='50m', color='black', linewidth=2)
ax.add_feature(ccrs.cartopy.feature.NaturalEarthFeature(category='cultural', name='admin_1_states_provinces_lines', scale='50m',facecolor='none'))
ax.add_feature(ccrs.cartopy.feature.BORDERS, linewidth='1', edgecolor='black')
ax.gridlines()

plt.show()

"""
Explanation: Plot It Up!
We now have the extents, we are ready to plot.
End of explanation
"""


th.shape

th[0,0]

"""
Explanation: Exercises for the Reader

The extents are actually not perfect and snip the image. Why? Fix.
Add a colorbar and jazz up the plot.
Trick question: Can you label the axes with latitude and longitudes?
Try a different projection which should be fairly easy with Cartopy.
End of explanation
"""


