### lab 8 setup code

 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``%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``        renderer.open_group(self.__name__)``        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()):``            self.gridline.draw(renderer)``        if transforms.interval_contains(lower_interval, self.get_loc()):``            if self.tick1On:``                self.tick1line.draw(renderer)``            if self.label1On:``                self.label1.draw(renderer)``        if transforms.interval_contains(upper_interval, self.get_loc()):``            if self.tick2On:``                self.tick2line.draw(renderer)``            if self.label2On:``                self.label2.draw(renderer)``        renderer.close_group(self.__name__)``# 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)``    @property``    def lower_interval(self):``        return self.axes.viewLim.intervalx``    def get_view_interval(self):``        return self.upper_interval, self.axes.viewLim.intervalx``# 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``        else:``            yloc = 0.0``        left = trans.transform_point((0.0, yloc))``        right = trans.transform_point((1.0, yloc))``        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.spines['top'].register_axis(self.xaxis)``        self.spines['bottom'].register_axis(self.xaxis)``        self.yaxis = maxis.YAxis(self)``        self.spines['left'].register_axis(self.yaxis)``        self.spines['right'].register_axis(self.yaxis)``    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``        Axes._set_lim_and_transforms(self)``        # 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.``register_projection(SkewXAxes)`