def testBinCenterToEdges(self):
"""Convert a set of bin centers to bin edges"""
inputs = [[1, 2, 3, 4, 5],
[12.4, 77, 100],]
outputs = [[0.5, 1.5, 2.5, 3.5, 4.5, 5.5],
[-19.9, 44.7, 88.5, 111.5],]
for i, val in enumerate(inputs):
numpy.testing.assert_almost_equal(outputs[i], tb.bin_center_to_edges(val))
def testBinCenterToEdges(self):
"""Convert a set of bin centers to bin edges"""
inputs = [[1, 2, 3, 4, 5],
[12.4, 77, 100],]
outputs = [[0.5, 1.5, 2.5, 3.5, 4.5, 5.5],
[-19.9, 44.7, 88.5, 111.5],]
for i, val in enumerate(inputs):
numpy.testing.assert_almost_equal(outputs[i], tb.bin_center_to_edges(val))
def plot_mult(self,
windows,
data,
min=None,
max=None,
cbar_label=None,
figsize=None,
dpi=80,
xlabel='Lag',
ylabel='Window Size'):
"""Plots a 2D function of window size and lag
Parameters
==========
windows : list
list of window sizes (y axis)
data : list
list of data, dimensioned (windows x lags)
min : float, optional
clip L{data} to this minimum value
max : float, optional
clip L{data} to this maximum value
"""
import matplotlib.pyplot as plt
x = np.array(tb.bin_center_to_edges(self.lags))
y = np.array(tb.bin_center_to_edges(windows))
fig = plt.figure(figsize=figsize, dpi=dpi)
ax0 = fig.add_subplot(111)
cax = ax0.pcolormesh(x, y, data, vmin=min, vmax=max, shading='flat')
ax0.set_xlim((x[0], x[-1]))
ax0.set_ylim((y[0], y[-1]))
plt.xlabel(xlabel)
plt.ylabel(ylabel)
if cbar_label == None:
if plt.rcParams['text.usetex']:
cbar_label = r'\% confident above asymptotic association'
else:
cbar_label = r'% confident above asymptotic association'
plt.colorbar(cax, fraction=0.05).set_label(cbar_label)
return fig
def plot_mult(self, windows, data, min=None, max=None, cbar_label=None,
figsize=None, dpi=80,
xlabel='Lag', ylabel='Window Size'):
"""Plots a 2D function of window size and lag
Parameters
==========
windows : list
list of window sizes (y axis)
data : list
list of data, dimensioned (windows x lags)
min : float, optional
clip L{data} to this minimum value
max : float, optional
clip L{data} to this maximum value
"""
import matplotlib.pyplot as plt
x = np.array(tb.bin_center_to_edges(self.lags))
y = np.array(tb.bin_center_to_edges(windows))
fig = plt.figure(figsize=figsize, dpi=dpi)
ax0 = fig.add_subplot(111)
cax = ax0.pcolormesh(x, y, data, vmin=min, vmax=max,
shading='flat')
ax0.set_xlim((x[0], x[-1]))
ax0.set_ylim((y[0], y[-1]))
plt.xlabel(xlabel)
plt.ylabel(ylabel)
if cbar_label == None:
if plt.rcParams['text.usetex']:
cbar_label = r'\% confident above asymptotic association'
else:
cbar_label = r'% confident above asymptotic association'
plt.colorbar(cax, fraction=0.05).set_label(cbar_label)
return fig
def simpleSpectrogram(*args, **kwargs):
"""
Plot a spectrogram given Z or X,Y,Z. This is a wrapper around pcolormesh()
that can handle Y being a 2d array of time dependent bins. Like in the
Van Allen Probes HOPE and MagEIS data files.
Parameters
==========
*args : 1 or 3 arraylike
Call Signatures::
simpleSpectrogram(Z, **kwargs)
simpleSpectrogram(X, Y, Z, **kwargs)
Other Parameters
================
zlog : bool
Plot the color with a log colorbar (default: True)
ylog : bool
Plot the Y axis with a log scale (default: True)
alpha : scalar (0-1)
The alpha blending value (default: None)
cmap : string
The name of the colormap to use (default: system default)
vmin : float
Minimum color value (default: Z.min(), if log non-zero min)
vmax : float
Maximum color value (default: Z.max())
ax : matplotlib.axes
Axes to plot the spectrogram on (default: None - new axes)
cb : bool
Plot a colorbar (default: True)
cbtitle : string
Label to go on the colorbar (default: None)
Returns
=======
ax : matplotlib.axes._subplots.AxesSubplot
Matplotlib axes object that the plot is on
"""
if len(args) not in [1,3]:
raise(TypeError("simpleSpectrogram, takes Z or X, Y, Z"))
if len(args) == 1: # just Z in, makeup an X and Y
Z = np.ma.masked_invalid(np.asarray(args[0])) # make sure not a dmarray or VarCopy
X = np.arange(Z.shape[0]+1)
Y = np.arange(Z.shape[1]+1)
else:
# we really want X and Y to be one larger than Z
X = np.asarray(args[0]) # make sure not a dmarray or VarCopy
Y = np.asarray(args[1]) # make sure not a dmarray or VarCopy
Z = np.ma.masked_invalid(np.asarray(args[2]))
if X.shape[0] == Z.shape[0]: # same length, expand X
X = tb.bin_center_to_edges(X) # hopefully evenly spaced
if len(Y.shape) == 1: # 1d, just use as axis
Y = tb.bin_center_to_edges(Y) # hopefully evenly spaced
elif len(Y.shape) == 2:
Y_orig = Y
# 2d this is time dependent and thus need to overplot several
Y = tb.unique_columns(Y, axis=1)
if Y.shape[1] == Z.shape[1]: # hopefully evenly spaced
Y_uniq = Y
Y_tmp = np.empty((Y.shape[0], Y.shape[1]+1), dtype=Y.dtype)
for ii in range(Y.shape[0]):
Y_tmp[ii] = tb.bin_center_to_edges(Y[ii])
Y = Y_tmp
# deal with all the default keywords
zlog = kwargs.pop('zlog', True)
ylog = kwargs.pop('ylog', True)
alpha = kwargs.pop('alpha', None)
cmap = kwargs.pop('cmap', None)
vmin = kwargs.pop('vmin', np.min(Z) if not zlog else np.min(Z[np.nonzero(Z)]))
vmax = kwargs.pop('vmax', np.max(Z))
ax = kwargs.pop('ax', None)
cb = kwargs.pop('cb', True)
cbtitle = kwargs.pop('cbtitle', None)
# the first case is that X, Y are 1d and Z is 2d, just make the plot
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
else:
fig = ax.get_figure()
Z = Z.filled(0)
if len(Y.shape) > 1:
for yy in Y_uniq:
# which indices in X have these values
ind = (yy == Y_orig).all(axis=1)
print(Y_orig[ind][0].min(), Y_orig[ind][0].max())
Y_tmp = np.zeros_like(Y_orig)
Y_tmp[ind] = Y_orig[ind][0]
Z_tmp = np.zeros_like(Z)
Z_tmp[ind] = Z[ind]
pc = ax.pcolormesh(X, Y_tmp[ind][0], Z_tmp.T,
norm=LogNorm(vmin=vmin, vmax=vmax), cmap=cmap,
alpha=alpha)
else:
pc = ax.pcolormesh(X, Y, Z.T, norm=LogNorm(vmin=vmin, vmax=vmax),
cmap=cmap,
alpha=alpha)
if ylog: ax.set_yscale('log')
if cb: # add a colorbar
cb_ = fig.colorbar(pc)
cb_.set_label(cbtitle)
return ax
def simpleSpectrogram(*args, **kwargs):
"""
Plot a spectrogram given Z or X,Y,Z. This is a wrapper around pcolormesh()
that can handle Y being a 2d array of time dependent bins. Like in the
Van Allen Probes HOPE and MagEIS data files.
Parameters
==========
*args : 1 or 3 arraylike
Call Signatures::
simpleSpectrogram(Z, **kwargs)
simpleSpectrogram(X, Y, Z, **kwargs)
Other Parameters
================
zlog : bool
Plot the color with a log colorbar (default: True)
ylog : bool
Plot the Y axis with a log scale (default: True)
alpha : scalar (0-1)
The alpha blending value (default: None)
cmap : string
The name of the colormap to use (default: system default)
vmin : float
Minimum color value (default: Z.min(), if log non-zero min)
vmax : float
Maximum color value (default: Z.max())
ax : matplotlib.axes
Axes to plot the spectrogram on (default: None - new axes)
cb : bool
Plot a colorbar (default: True)
cbtitle : string
Label to go on the colorbar (default: None)
Returns
=======
ax : matplotlib.axes._subplots.AxesSubplot
Matplotlib axes object that the plot is on
"""
if len(args) not in [1, 3]:
raise (TypeError("simpleSpectrogram, takes Z or X, Y, Z"))
if len(args) == 1: # just Z in, makeup an X and Y
Z = np.ma.masked_invalid(np.asarray(
args[0])) # make sure not a dmarray or VarCopy
X = np.arange(Z.shape[0] + 1)
Y = np.arange(Z.shape[1] + 1)
else:
# we really want X and Y to be one larger than Z
X = np.asarray(args[0]) # make sure not a dmarray or VarCopy
Y = np.asarray(args[1]) # make sure not a dmarray or VarCopy
Z = np.ma.masked_invalid(np.asarray(args[2]))
if X.shape[0] == Z.shape[0]: # same length, expand X
X = tb.bin_center_to_edges(X) # hopefully evenly spaced
if len(Y.shape) == 1: # 1d, just use as axis
if Y.shape[0] == Z.shape[1]: # same length, expand Y
Y = tb.bin_center_to_edges(Y) # hopefully evenly spaced
elif len(Y.shape) == 2:
Y_orig = Y
# 2d this is time dependent and thus need to overplot several
Y = tb.unique_columns(Y, axis=1)
if Y.shape[1] == Z.shape[1]: # hopefully evenly spaced
Y_uniq = Y
Y_tmp = np.empty((Y.shape[0], Y.shape[1] + 1), dtype=Y.dtype)
for ii in range(Y.shape[0]):
Y_tmp[ii] = tb.bin_center_to_edges(Y[ii])
Y = Y_tmp
# deal with all the default keywords
zlog = kwargs.pop('zlog', True)
ylog = kwargs.pop('ylog', True)
alpha = kwargs.pop('alpha', None)
cmap = kwargs.pop('cmap', None)
vmin = kwargs.pop('vmin',
np.min(Z) if not zlog else np.min(Z[np.nonzero(Z)]))
vmax = kwargs.pop('vmax', np.max(Z))
ax = kwargs.pop('ax', None)
cb = kwargs.pop('cb', True)
cbtitle = kwargs.pop('cbtitle', None)
# the first case is that X, Y are 1d and Z is 2d, just make the plot
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
else:
fig = ax.get_figure()
Z = Z.filled(0)
if len(Y.shape) > 1:
for yy in Y_uniq:
# which indices in X have these values
ind = (yy == Y_orig).all(axis=1)
print(Y_orig[ind][0].min(), Y_orig[ind][0].max())
Y_tmp = np.zeros_like(Y_orig)
Y_tmp[ind] = Y_orig[ind][0]
Z_tmp = np.zeros_like(Z)
Z_tmp[ind] = Z[ind]
pc = ax.pcolormesh(X,
Y_tmp[ind][0],
Z_tmp.T,
norm=LogNorm(vmin=vmin, vmax=vmax),
cmap=cmap,
alpha=alpha)
else:
pc = ax.pcolormesh(X,
Y,
Z.T,
norm=LogNorm(vmin=vmin, vmax=vmax),
cmap=cmap,
alpha=alpha)
if ylog: ax.set_yscale('log')
if cb: # add a colorbar
cb_ = fig.colorbar(pc)
if cbtitle is not None:
cb_.set_label(cbtitle)
return ax
def testBinEdgesToCenterToEdges_datetimeroundtrip(self):
"""Convert a set of datetime bin edges to centers and back to bin edges"""
inputs = [datetime.datetime(2012,9,3,n) for n in range(10)]
computedOut = tb.bin_edges_to_center(inputs)
roundtripResults = tb.bin_center_to_edges(computedOut)
self.assertTrue((inputs == roundtripResults).all())
def testBinEdgesToCenterToEdges_datetimeroundtrip(self):
"""Convert a set of datetime bin edges to centers and back to bin edges"""
inputs = [datetime.datetime(2012,9,3,n) for n in range(10)]
computedOut = tb.bin_edges_to_center(inputs)
roundtripResults = tb.bin_center_to_edges(computedOut)
self.assertTrue((inputs == roundtripResults).all())
axes.append(fig.add_subplot(nplots, 1, i + 1))
# set the titles
axes[0].set_title(conf['panel']['title'], fontsize='larger')
for i, ax in enumerate(axes, 1):
ax.set_ylabel(conf['plot{0}'.format(i)]['ylabel'], fontsize='larger')
# plot in the color
ax.imshow(out[i - 1][ind_d][0], cmap=cmap, vmin=0, vmax=3,
interpolation='nearest', aspect='auto')
yticklabels = _get_yticklabels(conf, i)
ax.set_yticks(np.arange(out[0][ind_d][0].shape[0]))
ax.set_yticklabels(yticklabels)
ax.set_ylim(tb.bin_center_to_edges(range(out[0][ind_d][0].shape[0]))[0],
tb.bin_center_to_edges(range(out[0][ind_d][0].shape[0]))[-1])
for i in tb.bin_center_to_edges(range(out[0][ind_d][0].shape[0])):
ax.axhline(i, color='k')
steps = np.arange(out[0][ind_d][0].shape[1])
diff = np.diff(steps)[0] / 2
for ii, i in enumerate(steps):
if ii % 3 == 0:
ax.axvline(i + diff, color='k', lw=2)
else:
ax.axvline(i + diff, color='k', lw=1)
## ax.set_xlim(xlim)
ax.set_xticks(steps[::3]) # +diff)
ax.set_xticklabels(out[0][ind_d][1][::3])
