"""
Specify contour levels
=======================
Use :func:`clfdict()` to create a set of contour levels and contour lines to plot.
"""
import pygeode as pyg, numpy as np
import pylab as pyl
pyl.ioff()
lat = pyg.regularlat(60)
lon = pyg.regularlon(120)
z = pyg.sin(2 * np.pi * lat /
180.)**10 + pyg.cos(10 +
(2 * np.pi / 180.)**2 * lat * lon) * pyg.cos(
2 * np.pi * lat / 180.)
ax = pyg.plot.AxesWrapper()
contour_dict = pyg.clfdict(min=-1.2,
axes=ax,
cdelt=0.4,
ndiv=3,
nf=2,
nl=1,
extend='both',
cmap='RdGy')
pyg.vcontour(z, **contour_dict)
ax.setp(title='Using helper function to set up contour levels')
"""
import pylab as pyl
pyl.ioff()
import pygeode as pyg
import numpy as np
from pygeode.tutorial import t2
# Generate some data for a scatter plot
N = 50
x_values = np.linspace(-1, 1, N)
colors = np.random.rand(N)
area = 1000 * np.random.rand(N) * (1 - abs(x_values))
x = pyg.NamedAxis(values=x_values, name='x')
y = pyg.NamedAxis(values=x_values + np.random.rand(N), name='y')
ax_scatter = pyg.vscatter(x, y, c=colors, s=area, alpha=0.5)
# Make a vcontour
ax_vcontour = pyg.vcontour(t2.Temp(pres=500, time='10 May 2012') *
(pyg.cos(t2.lat)) * pyg.sin(t2.lon),
cmap='Oranges')
# Stitch the axes together side by side into a grid
ax_both = pyg.plot.grid([[ax_scatter, ax_vcontour]])
pyl.ion()
ax_both.render()
import pylab as pyl
import pygeode as pyg, numpy as np
t = pyg.ModelTime365(values = np.arange(100), units='days', startdate=dict(year=1, month=1))
y1 = pyg.exp(-t / 30.) * pyg.cos(2*np.pi * t / 20.)
y2 = pyg.exp(-t / 30.) * pyg.sin(2*np.pi * t / 20.)
y1 = y1.rename('y1')
y2 = y2.rename('y2')
pyl.ioff()
ax = pyg.plot.AxesWrapper()
pyg.vplot(y1, label='y1', c='r', lw=2, axes=ax)
pyg.vplot(y2, label='y2', c='b', lw=2, axes=ax)
ax.setp(title = 'Two lines', ylabel='')
ax.setp_xaxis(major_formatter=pyg.timeticker.TimeFormatter(t, '$b'))
ax.legend(loc='lower right', frameon=False)
pyl.ion()
ax.render(1)
Ft = f.integrate('lon', dx = lam, type='trapz')
ax1 = pyg.showlines([1 - pyg.cosd(x), Fr, Fl, Ft],
fmts = ['k+', '_', '_', 'x'],
labels = [r'$1 - \cos x$', 'rectr', 'rectl', 'trapz'], fig=3)
# Set panel title and axes labels
ax1.setp(title = r"$\int_0^x \sin(x') dx'$", ylabel = '')
# Adjust size and axis padding
ax1.size = (4.1, 3)
ax1.pad = [0.5, 0.3, 0.1, 0.4]
# Example 2: f = cos(x)
# Different integration directions
f = pyg.cos(lam)
Ff = f.integrate('lon', dx = lam, order=1)
Fb = f.integrate('lon', dx = lam, order=-1)
ax2 = pyg.showlines([f, Ff, Fb],
fmts = ['k', '+', 'x'],
labels = [r'$\cos x$', 'order = 1', 'order = -1'], fig=3)
# Set panel title and axes labels
ax2.setp(title = r"$\int_0^x \cos x' dx'$, $\int_{2\pi}^x \cos x' dx'$", ylabel = '')
# Adjust size and axis padding
ax2.size = (4.1, 3)
ax2.pad = [0.5, 0.3, 0.1, 0.4]
ax = pyg.plot.grid([[ax1], [ax2]])
""" Cartopy: Quiver plot on a regional projection ============================================= """ import pygeode as pyg, numpy as np, pylab as pyl from cartopy import crs as ccrs import cartopy # Construct dummy data for quivers lat = pyg.gausslat(40) lon = pyg.regularlon(80, origin=-180) x = pyg.sin(2*np.pi * lon / 180.) * pyg.exp(-(lat - 30)**2 / (2*10**2)) y = pyg.cos(4*np.pi * lon / 180.) * pyg.exp(-(lat - 40)**2 / (4*10**2)) pyl.ioff() # Build CartopyAxis for a Lambert Conformal projection prj = dict(central_longitude=-90., central_latitude = 39.) ax = pyg.plot.CartopyAxes(projection = 'LambertConformal', prj_args = prj) ax.size = [6., 5.1] # Add ocean ax.add_feature(cartopy.feature.OCEAN) # Add quiver plot pyg.vquiver(x, y, axes=ax, width = 0.005) # Set plot title ax.setp(title = 'Lambert Conformal')