def add_histplot(self, target=False, loc=111, label='Kyoto $K_{p}$',
time_range=None, **kwargs):
'''
Make a quick histogram-style plot of the Kp data.
Returns
=======
fig : matplotlib figure object
ax : matplotlib axes object
Other Parameters
================
target : Figure or Axes
If None (default), a new figure is generated from scratch.
If a matplotlib Figure object, a new axis is created
to fill that figure.
If a matplotlib Axes object, the plot is placed
into that axis.
loc : int
Use to specify the subplot placement of the axis
(e.g. loc=212, etc.) Used if target is a Figure or None.
Default 111 (single plot).
label : string
The label applied to the line when a legend is added to the axes.
Defaults to 'Kyoto $K_{p}$'.
time_range : tuple of datetimes
The time range to plot. Only the first and last values in the
tuple (or list) are used if the number of elements is greater than
two. Defaults to **None**, meaning that the full time range
available is used.
Extra keyword arguments are passed to :function:`matplotlib.pyplot.plot`
to customize the line style.
Examples
========
>>> import spacepy.pybats.kyoto as kt
>>> kp = kt.fetch('kp', (1981, 11), (1981, 11)
>>> kp.add_histplot(lw=2.0, lc='r', label='Example KP')
'''
import matplotlib.pyplot as plt
# Shortcuts for the lazy.
bstart=self['binstart']
bstop =self['binstop']
npts =self.attrs['npts']
# Reformulate time to get histogram-type look.
newtime=np.zeros(npts*24, dtype=object)
newkp =np.zeros(npts*24)
# Set time range.
if not time_range:
time_range = newtime
for i in range(npts*8):
newtime[3*i ] = bstart[i]
newtime[3*i+1] = self['time'][i]
newtime[3*i+2] = bstop[i]
newkp[3*i:3*i+3] = self['kp'][i], self['kp'][i], self['kp'][i]
fig, ax = set_target(target, figsize=(10,4), loc=loc)
line=ax.plot(newtime, newkp, label=label, **kwargs)
applySmartTimeTicks(ax, time_range, dolabel=True)
return fig, ax
def add_ltut(self, target=None, loc=111, cmap='Greens_r', zlim=[1,1000],
add_cbar=True, clabel='Density $cm^{-3}$', xlabel='full',
title=None, grid=True, ntick=5):
'''
Plot log(density) as a contour against local time (y-axis) and
universal time (x-axis) using the PyBats *target* method of other
standard plotting methods. Four items are returned: the Matplotlib
Figure, Axes, Mesh, and ColorBar objects used (if cbar is set to
**False**, the returned ColorBar object is simply set to **False**.)
========== =======================================================
Kwarg Description
---------- -------------------------------------------------------
target Select plot destination. Defaults to new figure/axis.
loc The location of any generated subplots. Default is 111.
add_cbar Toggles the automatic colorbar. Default is**True**.
cmap Selects Matplotlib color table. Defaults to *Greens_r*.
zlim Limits for z-axis. Defaults to [0.1, 1000]
clabel Sets colorbar label. Defaults to units.
xlabel Sets x-axis labels, use 'full', 'ticks', or **None**.
title Sets axis title; defaults to **None**.
grid Show white dotted grid? Defaults to **True**
ntick Number of attempted cbar ticks. Defaults to 5.
========== =======================================================
'''
import matplotlib.pyplot as plt
from matplotlib.ticker import LogLocator, LogFormatterMathtext
from matplotlib.colors import LogNorm
# Set ax and fig based on given target.
if type(target) == plt.Figure:
fig = target
ax = fig.add_subplot(loc)
elif type(target).__base__ == plt.Axes:
ax = target
fig = ax.figure
else:
fig = plt.figure()
ax = fig.add_subplot(loc)
# Enforce values to be within limits.
z=np.where(self['n']>zlim[0], self['n'], 1.01*zlim[0])
z[z>zlim[1]] = zlim[1]
# Create plot:
mesh = ax.pcolormesh(self._dtime, self._y, z.transpose(),
cmap=plt.get_cmap(cmap), norm=LogNorm(),
vmin=zlim[0], vmax=zlim[-1])
# Use LT ticks and markers on y-axis:
ax.set_yticks([6, 12, 18])
ax.set_yticklabels(['Dawn', 'Noon', 'Dusk'])
ax.set_ylim([4,20])
# White ticks, slightly thicker:
ax.tick_params(axis='both', which='both', color='w', width=1.2)
# Grid marks:
if grid: ax.grid(c='w')
if title: ax.set_title(title)
if xlabel == 'full':
# Both ticks and label.
applySmartTimeTicks(ax, self['time'], dolabel=True)
elif xlabel == 'ticks':
# Ticks, but no date label.
applySmartTimeTicks(ax, self['time'], dolabel=False)
else:
# A blank x-axis is often useful.
applySmartTimeTicks(ax, self['time'], dolabel=False)
ax.set_xticklabels('')
# Add cbar as necessary:
if add_cbar:
#lct = LogLocator
cbar=plt.colorbar(mesh, ax=ax, pad=0.01, shrink=0.85)#, ticks=lct)
cbar.set_label(clabel)
else:
cbar=None
return fig, ax, mesh, cbar
