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+# Copyright (c) 2018 MetPy Developers.
+# Distributed under the terms of the BSD 3-Clause License.
+# SPDX-License-Identifier: BSD-3-Clause
+"""
+MetPy Declarative Syntax Tutorial
+=================================
+
+The declarative syntax that is a part of the MetPy packaged is designed to aid in simple
+data exploration and analysis needs by simplifying the plotting context from typical verbose
+Python code. The complexity of data wrangling and plotting are hidden behind the simplified
+syntax to allow a lower barrier to investigating your data.
+"""
+
+#########################################################################
+# Imports
+# -------
+#
+# You'll note that the number of imports is smaller due to using the declarative syntax.
+# There is no need to import Matplotlib or Cartopy to your code as all of that is done
+# behind the scenes.
+from datetime import datetime, timedelta
+
+import xarray as xr
+
+import metpy.calc as mpcalc
+from metpy.cbook import get_test_data
+from metpy.io import metar
+from metpy.plots.declarative import (BarbPlot, ContourPlot, FilledContourPlot, MapPanel,
+                                     PanelContainer, PlotObs)
+from metpy.units import units
+
+#########################################################################
+# Getting Data
+# ------------
+#
+# Depending on what kind of data you are wanting to plot you'll use either Xarray (for gridded
+# data), Pandas (for CSV data), or the MetPy METAR parser (for METAR data).
+#
+# We'll start this tutorial by reading in a gridded dataset using Xarray.
+
+# Open the netCDF file as a xarray Dataset and parse the full dataset
+data = xr.open_dataset(get_test_data('GFS_test.nc', False)).metpy.parse_cf()
+
+# View a summary of the Dataset
+print(data)
+
+#########################################################################
+# Set Datetime
+# ------------
+#
+# Set the date/time of that you desire to plot
+plot_time = datetime(2010, 10, 26, 12)
+
+#########################################################################
+# Subsetting Data
+# ---------------
+#
+# MetPy provides wrappers for the usual xarray indexing and selection routines that can handle
+# quantities with units. For DataArrays, MetPy also allows using the coordinate axis types
+# mentioned above as aliases for the coordinates. And so, if we wanted data to be just over
+# the U.S. for plotting purposes
+ds = data.metpy.sel(lat=slice(70, 10), lon=slice(360 - 150, 360 - 55))
+
+#########################################################################
+# For full details on xarray indexing/selection, see
+# `xarray's documentation <http://xarray.pydata.org/en/stable/indexing.html>`_.
+
+#########################################################################
+# Calculations
+# ------------
+#
+# In MetPy 1.0 and later, calculation functions accept Xarray DataArray's as input and the
+# output a DataArray that can be easily added to an existing Dataset.
+#
+# As an example, we calculate wind speed from the wind components and add it as a new variable
+# to our Dataset.
+ds['wind_speed'] = mpcalc.wind_speed(ds['u-component_of_wind_isobaric'],
+                                     ds['v-component_of_wind_isobaric'])
+
+#########################################################################
+# Plotting
+# --------
+#
+# With that miniaml preparation, we are now ready to use the simplified plotting syntax to be
+# able to plot our data and analyze the meteorological situation.
+#
+# General Structure
+#
+# 1. Set contour attributes
+#
+# 2. Set map characteristics and collect contours
+#
+# 3. Collect panels and plot
+#
+# 4. Show (or save) the results
+#
+# Valid Plotting Types for Gridded Data:
+#
+# - ``ContourPlot()``
+#
+# - ``FilledContourPlot()``
+#
+# - ``ImagePlot()``
+#
+# - ``PlotBarbs()``
+#
+# More complete descrptions of these and other plotting types, as well as the map panel and
+# panel containter classes are at the end of this tutorial.
+#
+# Let's plot a 300-hPa map with color-filled wind speed, which we calculated and added to
+# our Dataset above, and geopotential heights over the CONUS.
+
+#########################################################################
+# We'll start by setting attributes for contours of Geopotential Heights at 300 hPa.
+# We need to set at least the data, field, level, and time attributes. We'll set a few others
+# to have greater control over hour the data is plotted.
+
+# Set attributes for contours of Geopotential Heights at 300 hPa
+cntr2 = ContourPlot()
+cntr2.data = ds
+cntr2.field = 'Geopotential_height_isobaric'
+cntr2.level = 300 * units.hPa
+cntr2.time = plot_time
+cntr2.contours = list(range(0, 10000, 120))
+cntr2.linecolor = 'black'
+cntr2.linestyle = 'solid'
+cntr2.clabels = True
+
+#########################################################################
+# Now we'll set the attributes for plotting color-filled contours of wind speed at 300 hPa.
+# Again, the attributes that must be set include data, field, level, and time. We'll also set
+# a colormap and colorbar to be purposeful for wind speed. Additionally, we'll set the
+# attribute to change the units from m/s to knots, which is the common plotting units for
+# wind speed.
+
+# Set attributes for plotting color-filled contours of wind speed at 300 hPa
+cfill = FilledContourPlot()
+cfill.data = ds
+cfill.field = 'wind_speed'
+cfill.level = 300 * units.hPa
+cfill.time = plot_time
+cfill.contours = list(range(10, 201, 20))
+cfill.colormap = 'BuPu'
+cfill.colorbar = 'horizontal'
+cfill.plot_units = 'knot'
+
+#########################################################################
+# Once we have our contours (and any colorfill plots) set up, we will want to define the map
+# panel that we'll plot the data on. This is the place where we can set the view extent,
+# projection of our plot, add map lines like coastlines and states, set a plot title.
+# One of the key elements is to add the data to the map panel as a list with the plots
+# attribute.
+
+# Set the attributes for the map and add our data to the map
+panel = MapPanel()
+panel.area = [-125, -74, 20, 55]
+panel.projection = 'lcc'
+panel.layers = ['states', 'coastline', 'borders']
+panel.title = f'{cfill.level.m}-hPa Heights and Wind Speed at {plot_time}'
+panel.plots = [cfill, cntr2]
+
+#########################################################################
+# Finally we'll collect all of the panels to plot on the figure, set the size of the figure,
+# and ultimately show or save the figure.
+
+# Set the attributes for the panel and put the panel in the figure
+pc = PanelContainer()
+pc.size = (15, 15)
+pc.panels = [panel]
+
+#########################################################################
+# All of our setting now produce the following map!
+
+# Show the image
+pc.show()
+
+#########################################################################
+# That's it! What a nice looking map, with relatively simple set of code.
+
+
+#########################################################################
+# Adding Wind Barbs
+# -----------------
+#
+# We can easily add wind barbs to the plot we generated above by adding another plot type
+# and adding it to the panel. The plot type for wind barbs is ``PlotBarbs()`` and has its own
+# set of attributes to control plotting a vector quantity.
+
+#########################################################################
+# We start with setting the attributes that we had before for our 300 hPa plot inlcuding,
+# Geopotential Height contours, and color-filled wind speed.
+
+# Set attributes for contours of Geopotential Heights at 300 hPa
+cntr2 = ContourPlot()
+cntr2.data = ds
+cntr2.field = 'Geopotential_height_isobaric'
+cntr2.level = 300 * units.hPa
+cntr2.time = plot_time
+cntr2.contours = list(range(0, 10000, 120))
+cntr2.linecolor = 'black'
+cntr2.linestyle = 'solid'
+cntr2.clabels = True
+
+# Set attributes for plotting color-filled contours of wind speed at 300 hPa
+cfill = FilledContourPlot()
+cfill.data = ds
+cfill.field = 'wind_speed'
+cfill.level = 300 * units.hPa
+cfill.time = plot_time
+cfill.contours = list(range(10, 201, 20))
+cfill.colormap = 'BuPu'
+cfill.colorbar = 'horizontal'
+cfill.plot_units = 'knot'
+
+#########################################################################
+# Now we'll set the attributes for plotting wind barbs, with the required attributes of data,
+# time, field, and level. The skip attribute is particularly useful for thining the number of
+# wind barbs that are plotted on the map and again we'll convert to units of knots.
+
+# Set attributes for plotting wind barbs
+barbs = BarbPlot()
+barbs.data = ds
+barbs.time = plot_time
+barbs.field = ['u-component_of_wind_isobaric', 'v-component_of_wind_isobaric']
+barbs.level = 300 * units.hPa
+barbs.skip = (3, 3)
+barbs.plot_units = 'knot'
+
+#########################################################################
+# Add all of our plot types to the panel, don't forget to add in the new wind barbs to our plot
+# list!
+
+# Set the attributes for the map and add our data to the map
+panel = MapPanel()
+panel.area = [-125, -74, 20, 55]
+panel.projection = 'lcc'
+panel.layers = ['states', 'coastline', 'borders']
+panel.title = f'{cfill.level.m}-hPa Heights and Wind Speed at {plot_time}'
+panel.plots = [cfill, cntr2, barbs]
+
+# Set the attributes for the panel and put the panel in the figure
+pc = PanelContainer()
+pc.size = (15, 15)
+pc.panels = [panel]
+
+# Show the figure
+pc.show()
+
+
+#########################################################################
+# Plot Surface Obs
+# ----------------
+#
+# We can also plot surface (or upper-air) observations at point locations using the simplified
+# syntax. Whether it is surface or upper-air data, the ``PlotObs()`` class is what you would
+# want to use. Then you would add those observations to a map panel and then collect the panels
+# to plot the figure; similar to what you would do for a gridded plot.
+df = metar.parse_metar_file(get_test_data('metar_20190701_1200.txt', False), year=2019,
+                            month=7)
+
+# Let's take a look at the variables that we could plot coming from our METAR observations.
+print(df.keys())
+
+# Set the observation time
+obs_time = datetime(2019, 7, 1, 12)
+
+#########################################################################
+# Setting of our attributes for plotting observations is pretty straignforward and just needs
+# to be lists for the variables, and a comparable number of items for plot characteristics that
+# are specific to the individual fields. For example, the locations around a station plot, the
+# plot units, and any plotting formats would all meed to have the same number of items as the
+# fields attribute.
+#
+# Plotting wind bards is done through the vector_field attribute and you can reduce the number
+# of points plotted (especially important for surface observations) with the reduce points
+# attribute.
+#
+# For a very basic plot of one field, the minimum required attributes are the data, time,
+# fields, and location attributes.
+
+# Plot desired data
+obs = PlotObs()
+obs.data = df
+obs.time = obs_time
+obs.time_window = timedelta(minutes=15)
+obs.level = None
+obs.fields = ['cloud_coverage', 'air_temperature', 'dew_point_temperature',
+              'air_pressure_at_sea_level', 'present_weather']
+obs.plot_units = [None, 'degF', 'degF', None, None]
+obs.locations = ['C', 'NW', 'SW', 'NE', 'W']
+obs.formats = ['sky_cover', None, None, lambda v: format(v * 10, '.0f')[-3:],
+               'current_weather']
+obs.reduce_points = 0.75
+obs.vector_field = ['eastward_wind', 'northward_wind']
+
+#########################################################################
+# We use the same Classes for plotting our data on a map panel and collecting all of the
+# panels on the figure. In this case we'll focus in on the state of Indiana for plotting.
+
+# Panel for plot with Map features
+panel = MapPanel()
+panel.layout = (1, 1, 1)
+panel.projection = 'lcc'
+panel.area = 'in'
+panel.layers = ['states']
+panel.title = f'Surface plot for {obs_time}'
+panel.plots = [obs]
+
+# Bringing it all together
+pc = PanelContainer()
+pc.size = (10, 10)
+pc.panels = [panel]
+
+pc.show()
+
+#########################################################################
+# Detailed Attribute Descriptions
+# -------------------------------
+#
+# This final section contains verbose descriptions of the attributes that can be set by the
+# plot types used in this tutorial.
+
+#########################################################################
+# ContourPlot()
+# -------------
+#
+# This class is designed to plot contours of gridded data, most commonly model output from the
+# GFS, NAM, RAP, or other gridded dataset (e.g., NARR).
+#
+# Attributes:
+#
+# ``data``
+#
+# This attribute must be set with the variable name that contains the xarray dataset.
+# (Typically this is the variable ds)
+#
+# ``field``
+#
+# This attribute must be set with the name of the variable that you want to contour.
+# For example, to plot the heights of pressure surfaces from the GFS you would use the name
+# ``‘Geopotential_height_isobaric’``
+#
+# ``level``
+#
+# This attribute sets the level of the data you wish to plot. If it is a pressure level,
+# then it must be set to a unit bearing value (e.g., 500*units.hPa). If the variable does
+# not have any vertical levels (e.g., mean sea-level pressure), then the level attribute must
+# be set to None.
+#
+# ``time``
+#
+# This attribute must be set with a datetime object, just as with the ``PlotObs()`` class.
+# To get a forecast hour, you can use the timedelta function from datetime to add the number of
+# hours into the future you wish to plot. For example, if you wanted the six hour forecast from
+# the 00 UTC 2 February 2020 model run, then you would set the attribute with:
+#
+# ``datetime(2020, 2, 2, 0) + timedelta(hours=6)``
+#
+# ``contours``
+#
+# This attribute sets the contour values to be plotted with a list. This can be set manually
+# with a list of integers in square brackets (e.g., ``[5400, 5460, 5520, 5580, 5640, 5700]``)
+# or programmatically (e.g., ``list(range(0, 10000, 60))``). The second method is a way to
+# easily set a contour interval (in this case 60).
+#
+# ``clabel``
+#
+# This attribute can be set to ``True`` if you desire to have your contours labeled.
+#
+# ``linestyle``
+#
+# This attribute can be set to make the contours ``‘solid’``, ``‘dashed’``, ``‘dotted’``,
+# or ``‘dashdot’``. Other linestyles are can be used and are found at:
+# https://matplotlib.org/3.1.0/gallery/lines_bars_and_markers/linestyles.html
+#
+# Default is ``‘solid’``.
+#
+# ``linewidth``
+#
+# This attribute alters the width of the contours (defaults to 1). Setting the value greater
+# than 1 will yield a thicker contour line.
+#
+# ``linecolor``
+#
+# This attribute sets the color of the contour lines. Default is ``‘black’``. All colors from
+# matplotlib are valid: https://matplotlib.org/3.1.0/_images/sphx_glr_named_colors_003.png
+#
+# ``plot_units``
+#
+# If you want to change the units for plotting purposes, add the string value of the units
+# desired. For example, if you want to plot temperature in Celsius, then set this attribute
+# to ``‘degC’``.
+
+#########################################################################
+# FilledContourPlot()
+# -------------------
+#
+# Works very similarly to ``ContourPlot()``, except that contours are filled using a colormap
+# between contour values. All attributes for ``ContourPlot()`` work for color-filled plots,
+# except for linestyle, linecolor, and linewidth. Additionally, there are the following
+# attributes that work for color-filling:
+#
+# Attributes:
+#
+# ``colormap``
+#
+# This attribute is used to set a valid colormap from either Matplotlib or MetPy:
+# Matplotlib Colormaps: https://matplotlib.org/3.1.1/gallery/color/colormap_reference.html
+# MetPy Colormaps: https://unidata.github.io/MetPy/v1.0/api/generated/metpy.plots.ctables.html
+#
+# ``colorbar``
+#
+# This attribute can be set to ``‘vertical’`` or ``‘horizontal’``, which is the location the
+# colorbar will be plotted on the panel.
+#
+# ``image_range``
+#
+# A set of values indicating the minimum and maximum for the data being plotted. This
+# attribute should be set as ``(min_value, max_value)``, where min_value and max_value are
+# numeric values.
+
+#########################################################################
+# PanelContainer()
+# ----------------
+#
+# Attributes:
+#
+# ``size``
+#
+# The size of the figure in inches (e.g., (10, 8))
+#
+# ``panels``
+#
+# A list collecting the panels to be plotted in the figure.
+#
+# ``show``
+#
+# Show the plot
+#
+# ``save``
+#
+# Save the figure using the Matplotlib arguments/keyword arguments
+
+#########################################################################
+# MapPanel()
+# ----------
+#
+# Attributes:
+#
+# ``layout``
+#
+# The Matplotlib layout of the figure. For a single panel figure the setting should be
+# ``(1, 1, 1)``
+#
+# ``projection``
+#
+# The projection can be set with the name of a default projection (``‘lcc’``, ``‘mer’``, or
+# ``‘ps’``) or it can be set to a Cartopy projection.
+#
+# ``layers``
+#
+# This attribute will add map layers to identify boundaries or features to plot on the map.
+# Valid layers are ``'borders'``, ``'coastline'``, ``'states'``, ``'lakes'``, ``'land'``,
+# ``'ocean'``, ``'rivers'``, ``'counties'``.
+#
+# ``area``
+#
+# This attribute sets the geographical area of the panel. This can be set with a predefined
+# name of an area including all US state postal abbreviations (e.g., ``‘us’``, ``‘natl’``,
+# ``‘in’``, ``‘il’``, ``‘wi’``, ``‘mi’``, etc.) or a tuple value that corresponds to
+# longitude/latitude box based on the projection of the map with the format
+# ``(west-most longitude, east-most longitude, south-most latitude, north-most latitude)``.
+# This tuple defines a box from the lower-left to the upper-right corner.
+#
+# ``title``
+#
+# This attribute sets a title for the panel.
+#
+# ``plots``
+#
+# A list collecting the observations to be plotted in the panel.
+
+#########################################################################
+# BarbPlot()
+# ----------
+#
+# This plot class is used to add wind barbs to the plot with the following
+#
+# Attributes:
+#
+# ``data``
+#
+# This attribute must be set to the variable that contains the vector components to be plotted.
+#
+# ``field``
+#
+# This attribute is a list of the vector components to be plotted. For the typical
+# meteorological case it would be the ``[‘u-compopnent’, ‘v-component’]``.
+#
+# ``time``
+#
+# This attribute should be set to a datetime object, the same as for all other declarative
+# classes.
+#
+# ``barblength``
+#
+# This attribute sets the length of the wind barbs. The default value is based on the
+# font size.
+#
+# ``color``
+#
+# This attribute sets the color of the wind barbs, which can be any Matplotlib color.
+# Default color is ``‘black’``.
+#
+# ``earth_relative``
+#
+# This attribute can be set to False if the vector components are grid relative (e.g., for NAM
+# or NARR output)
+#
+# ``pivot``
+#
+# This attribute can be set to a string value about where the wind barb will pivot relative to
+# the grid point. Possible values include ``‘tip’`` or ``‘middle’``. Default is ``‘middle’``.
+
+########################################################################
+# PlotObs()
+# ---------
+#
+# This class is used to plot point observations from the surface or upper-air.
+#
+# Attributes:
+#
+# ``data``
+#
+# This attribute needs to be set to the DataFrame variable containing the fields that you
+# desire to plot.
+#
+# ``fields``
+#
+# This attribute is a list of variable names from your DataFrame that you desire to plot at the
+# given locations around the station model.
+#
+# ``level``
+#
+# For a surface plot this needs to be set to None.
+#
+# ``time``
+#
+# This attribute needs to be set to subset your data attribute for the time of the observations
+# to be plotted. This needs to be a datetime object.
+#
+# ``locations``
+#
+# This attribute sets the location of the fields to be plotted around the surface station
+# model. The default location is center ``(‘C’)``
+#
+# ``time_range``
+#
+# This attribute allows you to define a window for valid observations (e.g., 15 minutes on
+# either side of the datetime object setting. This is important for surface data since actual
+# observed times are not all exactly on the hour. If multiple observations exist in the defined
+# window, the most recent observations is retained for plotting purposes.
+#
+# ``formats``
+#
+# This attribute sets a formatter for text or plotting symbols around the station model. For
+# example, plotting mean sea-level pressure is done in a three-digit code and a formatter can
+# be used to achieve that on the station plot.
+#
+# MSLP Formatter: ``lambda v: format(10 * v, '.0f')[-3:]``
+#
+# For plotting symbols use the available MetPy options through their name. Valid symbol formats
+# are ``'current_weather'``, ``'sky_cover'``, ``'low_clouds'``, ``'mid_clouds'``,
+# ``'high_clouds'``, and ``'pressure_tendency'``.
+#
+# ``colors``
+#
+# This attribute can change the color of the plotted observation. Default is ``‘black’``.
+# Acceptable colors are those available through Matplotlib:
+# https://matplotlib.org/3.1.1/_images/sphx_glr_named_colors_003.png
+#
+# ``vector_field``
+#
+# This attribute can be set to a list of wind component values for plotting
+# (e.g., ``[‘uwind’, ‘vwind’]``)
+#
+# ``vector_field_color``
+#
+# Same as colors except only controls the color of the wind barbs. Default is ``‘black’``.
+#
+# ``reduce_points``
+#
+# This attribute can be set to a real number to reduce the number of stations that are plotted.
+# Default value is zero (e.g., no points are removed from the plot).