# -*- coding: utf-8 -*-

"""

Created on Fri Aug 25 22:14:26 2017

@author: Unidata/MetPy

#Changes and modifications made by Noah Brauer

"""

# Copyright (c) 2008-2016 MetPy Developers.

# Distributed under the terms of the BSD 3-Clause License.

# SPDX-License-Identifier: BSD-3-Clause

"""

===========================

Upper Air Sounding Tutorial

===========================

Upper air analysis is a staple of many synoptic and mesoscale analysis

problems. In this tutorial we will gather weather balloon data, plot it,

perform a series of thermodynamic calculations, and summarize the results.

To learn more about the Skew-T diagram and its use in weather analysis and

forecasting, checkout `this <http://homes.comet.ucar.edu/~alanbol/aws-tr-79-006.pdf>`_

air weather service guide.

"""

from datetime import datetime

import matplotlib.pyplot as plt

from mpl_toolkits.axes_grid1.inset_locator import inset_axes

import numpy as np

import metpy.calc as mpcalc

from metpy.io import get_upper_air_data

from metpy.plots import Hodograph, SkewT

from os import open

#########################################################################

# Getting Data

# ------------

#

# We will download data from the

# `University of Wyoming sounding data page <http://weather.uwyo.edu/upperair/sounding.html>`_

# , which has an extensive archive of data available, as well as current data.

#

# In this case, we will download the sounding data from the 1999 May 3

# tornado by passing a ``datetime`` object and station name to the

# ``get_upper_air_data`` function.

#site = input("Enter Upper-Air Site")

#year = input("Enter Year (YYYY)")

#month = input("Enter Month (M/MM)")

#day = input("Enter Day (D/DD)")

#hour = input("Enter Hour (H/HH)")

#dataset = get_upper_air_data(datetime(year, month, day, hour), DNR)

dataset = get_upper_air_data(datetime(2017, 8, 27, 12), 'CRP')

##########################################################################

# We can view the fields available in the dataset. We will create some simple

# variables to make the rest of the code more concise.

print(dataset.variables.keys())

p = dataset.variables['pressure'][:]

T = dataset.variables['temperature'][:]

Td = dataset.variables['dewpoint'][:]

u = dataset.variables['u_wind'][:]

v = dataset.variables['v_wind'][:]

##########################################################################

# Thermodynamic Calculations

# --------------------------

#

# Often times we will want to calculate some thermodynamic parameters of a

# sounding. The MetPy calc module has many such calculations already implemented!

#

# * **Lifting Condensation Level (LCL)** - The level at which an air parcel's

# relative humidity becomes 100% when lifted along a dry adiabatic path.

# * **Parcel Path** - Path followed by a hypothetical parcel of air, beginning

# at the surface temperature/pressure and rising dry adiabatically until

# reaching the LCL, then rising moist adiabatially.

# Calculate the LCL

lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])

print(lcl_pressure, lcl_temperature)

# Calculate the parcel profile.

parcel_prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')

##########################################################################

# Basic Skew-T Plotting

# ---------------------

#

# The Skew-T (log-P) diagram is the standard way to view rawinsonde data. The

# y-axis is height in pressure coordinates and the x-axis is temperature. The

# y coordinates are plotted on a logarithmic scale and the x coordinate system

# is skewed. An explanation of skew-T interpretation is beyond the scope of this

# tutorial, but here we will plot one that can be used for analysis or

# publication.

#

# The most basic skew-T can be plotted with only five lines of Python.

# These lines perform the following tasks:

#

# 1. Create a ``Figure`` object and set the size of the figure.

#

# 2. Create a ``SkewT`` object

#

# 3. Plot the pressure and temperature (note that the pressure,

# the independent variable, is first even though it is plotted on the y-axis).

#

# 4. Plot the pressure and dewpoint temperature.

#

# 5. Plot the wind barbs at the appropriate pressure using the u and v wind

# components.

# Create a new figure. The dimensions here give a good aspect ratio

fig = plt.figure(figsize=(9, 9))

skew = SkewT(fig)

# Plot the data using normal plotting functions, in this case using

# log scaling in Y, as dictated by the typical meteorological plot

skew.plot(p, T, 'r', linewidth=2)

skew.plot(p, Td, 'g', linewidth=2)

#skew.plot_barbs(p, u, v)

#Increase the number in these arrays to increase the declutter rate

skew.plot_barbs(p[0::4], u[0::4], v[0::4])

# Show the plot

#plt.show()

##########################################################################

# Advanced Skew-T Plotting

# ------------------------

#

# Fiducial lines indicating dry adiabats, moist adiabats, and mixing ratio are

# useful when performing further analysis on the Skew-T diagram. Often the

# 0C isotherm is emphasized and areas of CAPE and CIN are shaded.

# Create a new figure. The dimensions here give a good aspect ratio

#fig = plt.figure(figsize=(9, 9))

skew = SkewT(fig, rotation=30)

# Plot the data using normal plotting functions, in this case using

# log scaling in Y, as dictated by the typical meteorological plot

skew.plot(p, T, 'r')

skew.plot(p, Td, 'g')

#skew.plot_barbs(p, u, v,)

#Increase the number in these arrays to increase the declutter rate

skew.plot_barbs(p[0::4], u[0::4], v[0::4])

skew.ax.set_ylim(1000, 100)

skew.ax.set_xlim(-40, 60)

# Plot LCL temperature as black dot

skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')

# Plot the parcel profile as a black line

skew.plot(p, parcel_prof, 'k', linewidth=2)

# Shade areas of CAPE and CIN

skew.shade_cin(p, T, parcel_prof)

skew.shade_cape(p, T, parcel_prof)

# Plot a zero degree isotherm

l = skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)

# Add the relevant special lines

skew.plot_dry_adiabats()

skew.plot_moist_adiabats()

skew.plot_mixing_lines()

# Show the plot

#plt.show()

##########################################################################

# Adding a Hodograph

# ------------------

#

# A hodograph is a polar representation of the wind profile measured by the rawinsonde.

# Winds at different levels are plotted as vectors with their tails at the origin, the angle

# from the vertical axes representing the direction, and the length representing the speed.

# The line plotted on the hodograph is a line connecting the tips of these vectors,

# which are not drawn.

# Create a new figure. The dimensions here give a good aspect ratio

#fig = plt.figure(figsize=(9, 9))

skew = SkewT(fig, rotation=30)

# Plot the data using normal plotting functions, in this case using

# log scaling in Y, as dictated by the typical meteorological plot

skew.plot(p, T, 'r')

skew.plot(p, Td, 'g')

#comment out barb color for black barbs

#skew.plot_barbs(p, u, v,)

#Increase the number in these arrays to increase the declutter rate

skew.plot_barbs(p[0::4], u[0::4], v[0::4])

skew.ax.set_ylim(1000, 100)

skew.ax.set_xlim(-40, 60)

skew.ax.set_ylabel = ('Pressure')

# Plot LCL as black dot

skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')

# Plot the parcel profile as a black line

skew.plot(p, parcel_prof, 'k', linewidth=2)

# Shade areas of CAPE and CIN

skew.shade_cin(p, T, parcel_prof)

skew.shade_cape(p, T, parcel_prof)

# Plot a zero degree isotherm

skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)

# Add the relevant special lines

skew.plot_dry_adiabats()

skew.plot_moist_adiabats()

skew.plot_mixing_lines()

# Create a hodograph

# Create an inset axes object that is 40% width and height of the

# figure and put it in the upper right hand corner.

#comment next 4 lines back in if you want to plot a hodograph

#ax_hod = inset_axes(skew.ax, '40%', '40%', loc=1)

#h = Hodograph(ax_hod, component_range=80.)

#h.add_grid(increment=20)

#h.plot_colormapped(u, v, np.hypot(u, v)) # Plot a line colored by wind speed

#bg_color = 'black'

#fig = plt.figure(facecolor=bg_color)

#plot station/date

#plot a title

plt.title('CRP 201708270000', color = 'black')

# Show the plot

plt.show()