sharppy setup

Log in to a machine and open a web browser and enter the following URL

Log in with your career account. Start a new python notebook by clicking on “New” in the upper right corner, select “Baldwin (EAPS) Python 2”. This should open a new notebook in a separate browser tab. Rename the notebook to something useful like "skewt_test"

copy and paste this block of code into the first cell in jupyter, then hit shift-enter to execute. these steps will set up the remainder of the code to read, analyze, and plot the data. this block of code only needs to be executed once at the start of the notebook.

import numpy as np 
import matplotlib.pyplot as plt
import sharppy
import sharppy.sharptab.profile as profile
import sharppy.sharptab.interp as interp
import sharppy.sharptab.winds as winds
import sharppy.sharptab.utils as utils
import sharppy.sharptab.params as params
import sharppy.sharptab.thermo as thermo
%matplotlib inline
# This serves as an intensive exercise of matplotlib's transforms
# and custom projection API. This example produces a so-called
# SkewT-logP diagram, which is a common plot in meteorology for
# displaying vertical profiles of temperature. As far as matplotlib is
# concerned, the complexity comes from having X and Y axes that are
# not orthogonal. This is handled by including a skew component to the
# basic Axes transforms. Additional complexity comes in handling the
# fact that the upper and lower X-axes have different data ranges, which
# necessitates a bunch of custom classes for ticks,spines, and the axis
# to handle this.

from matplotlib.axes import Axes
import matplotlib.transforms as transforms
import matplotlib.axis as maxis
import matplotlib.spines as mspines
import matplotlib.path as mpath
from matplotlib.projections import register_projection

# The sole purpose of this class is to look at the upper, lower, or total
# interval as appropriate and see what parts of the tick to draw, if any.
class SkewXTick(maxis.XTick):
    def draw(self, renderer):
        if not self.get_visible(): return

        lower_interval = self.axes.xaxis.lower_interval
        upper_interval = self.axes.xaxis.upper_interval

        if self.gridOn and transforms.interval_contains(
                self.axes.xaxis.get_view_interval(), self.get_loc()):

        if transforms.interval_contains(lower_interval, self.get_loc()):
            if self.tick1On:
            if self.label1On:

        if transforms.interval_contains(upper_interval, self.get_loc()):
            if self.tick2On:
            if self.label2On:


# This class exists to provide two separate sets of intervals to the tick,
# as well as create instances of the custom tick
class SkewXAxis(maxis.XAxis):
    def __init__(self, *args, **kwargs):
        maxis.XAxis.__init__(self, *args, **kwargs)
        self.upper_interval = 0.0, 1.0

    def _get_tick(self, major):
        return SkewXTick(self.axes, 0, '', major=major)

    def lower_interval(self):
        return self.axes.viewLim.intervalx

    def get_view_interval(self):
        return self.upper_interval[0], self.axes.viewLim.intervalx[1]

# This class exists to calculate the separate data range of the
# upper X-axis and draw the spine there. It also provides this range
# to the X-axis artist for ticking and gridlines
class SkewSpine(mspines.Spine):
    def _adjust_location(self):
        trans = self.axes.transDataToAxes.inverted()
        if self.spine_type == 'top':
            yloc = 1.0
            yloc = 0.0
        left = trans.transform_point((0.0, yloc))[0]
        right = trans.transform_point((1.0, yloc))[0]

        pts  = self._path.vertices
        pts[0, 0] = left
        pts[1, 0] = right
        self.axis.upper_interval = (left, right)

# This class handles registration of the skew-xaxes as a projection as well
# as setting up the appropriate transformations. It also overrides standard
# spines and axes instances as appropriate.
class SkewXAxes(Axes):
    # The projection must specify a name.  This will be used be the
    # user to select the projection, i.e. ``subplot(111,
    # projection='skewx')``.
    name = 'skewx'

    def _init_axis(self):
        #Taken from Axes and modified to use our modified X-axis
        self.xaxis = SkewXAxis(self)
        self.yaxis = maxis.YAxis(self)

    def _gen_axes_spines(self):
        spines = {'top':SkewSpine.linear_spine(self, 'top'),
                  'bottom':mspines.Spine.linear_spine(self, 'bottom'),
                  'left':mspines.Spine.linear_spine(self, 'left'),
                  'right':mspines.Spine.linear_spine(self, 'right')}
        return spines

    def _set_lim_and_transforms(self):
        This is called once when the plot is created to set up all the
        transforms for the data, text and grids.
        rot = 30

        #Get the standard transform setup from the Axes base class

        # Need to put the skew in the middle, after the scale and limits,
        # but before the transAxes. This way, the skew is done in Axes
        # coordinates thus performing the transform around the proper origin
        # We keep the pre-transAxes transform around for other users, like the
        # spines for finding bounds
        self.transDataToAxes = self.transScale + (self.transLimits +
                transforms.Affine2D().skew_deg(rot, 0))

        # Create the full transform from Data to Pixels
        self.transData = self.transDataToAxes + self.transAxes

        # Blended transforms like this need to have the skewing applied using
        # both axes, in axes coords like before.
        self._xaxis_transform = (transforms.blended_transform_factory(
                    self.transScale + self.transLimits,
                    transforms.IdentityTransform()) +
                transforms.Affine2D().skew_deg(rot, 0)) + self.transAxes

# Now register the projection with matplotlib so the user can select
# it.

from StringIO import StringIO

def parseSPC(spc_file):
    ## read in the file
    data = np.array([l.strip() for l in spc_file.split('\n')])

    ## necessary index points
    title_idx = np.where( data == '%TITLE%')[0][0]
    start_idx = np.where( data == '%RAW%' )[0] + 1
    finish_idx = np.where( data == '%END%')[0]

    ## create the plot title
    data_header = data[title_idx + 1].split()
    location = data_header[0]
    time = data_header[1][:11]

    ## put it all together for StringIO
    full_data = '\n'.join(data[start_idx : finish_idx][:])
    sound_data = StringIO( full_data )

    ## read the data into arrays
    p, h, T, Td, wdir, wspd = np.genfromtxt( sound_data, delimiter=',', comments="%", unpack=True )

    return p, h, T, Td, wdir, wspd