def preparePlot(self):
"""Prepare the plotting window"""
plt.hot()
self.figure = plt.figure(self.figNr)
self.figure.clear()
# this is black magic that makes the legend work with two axes
if self.hasSubplotHost:
self.axis1 = SubplotHost(self.figure, 111)
self.figure.add_subplot(self.axis1)
else:
self.axis1 = self.figure.add_subplot(111)
self.axis1.set_xlabel(self.xlabel)
self.axis1.set_ylabel(self.ylabel)
if len(self.alternate) > 0:
self.axis2 = self.axis1.twinx()
self.axis2.set_ylabel(self.ylabel2)
try:
if self.spec.logscale:
self.axis1.set_yscale("log")
if self.axis2:
self.axis2.set_yscale("log")
except AttributeError:
pass
def draw_heart_temp():
fig = plt.figure(1)
host = SubplotHost(fig, 111)
par1 = host.twinx()
par1.axis["right"].set_visible(True)
#offset = 60, 0
fig.add_axes(host)
plt.subplots_adjust(right=0.75)
host.set_xlabel("Time")
host.set_ylabel("HeartRate [bpm]")
par1.set_ylabel("Temperature [℃]")
host.set_xlim(time_s[0], time_s[len(time_s) - 1])
host.set_ylim(0, 100)
par1.set_ylim(0, 35)
p1, = host.plot(time_h, heart, label="HeartRate")
p2, = par1.plot(time_t, temp, color="r", label="Temperature")
host.legend()
host.axis["left"].label.set_color(p1.get_color())
par1.axis["right"].label.set_color(p2.get_color())
days = mdates.AutoDateLocator()
daysFmt = mdates.DateFormatter("%H:%M")
host.xaxis.set_major_locator(days)
host.xaxis.set_major_formatter(daysFmt)
par1.xaxis.set_major_locator(days)
par1.xaxis.set_major_formatter(daysFmt)
plt.show()
def draw_sleep_humid():
fig = plt.figure(1)
host = SubplotHost(fig, 111)
par1 = host.twinx()
par1.axis["right"].set_visible(True)
#offset = 60, 0
fig.add_axes(host)
plt.subplots_adjust(right=0.75)
host.set_xlabel("Time")
host.set_ylabel("State")
par1.set_ylabel("Humidity [%]")
host.set_xlim(time_s[0], time_s[len(time_s) - 1])
host.yaxis.set_major_locator(MultipleLocator(1))
par1.set_ylim(0, 90)
p1, = host.plot(time_s, state, label="State")
p2, = par1.plot(time_t, humid, color="r", label="Humidity")
host.legend()
host.axis["left"].label.set_color(p1.get_color())
par1.axis["right"].label.set_color(p2.get_color())
days = mdates.AutoDateLocator()
daysFmt = mdates.DateFormatter("%H:%M")
host.xaxis.set_major_locator(days)
host.xaxis.set_major_formatter(daysFmt)
par1.xaxis.set_major_locator(days)
par1.xaxis.set_major_formatter(daysFmt)
plt.show()
def draw_sleep_discomfort():
for i in range(0, len(time_t)):
tmp_t = float(temp[i])
tmp_h = float(humid[i])
value = 0.81 * tmp_t + 0.01 * tmp_h * (0.99 * tmp_t - 14.3) + 46.3
discomfort.append(value)
fig = plt.figure(1)
host = SubplotHost(fig, 111)
par1 = host.twinx()
par1.axis["right"].set_visible(True)
#offset = 60, 0
fig.add_axes(host)
plt.subplots_adjust(right=0.75)
host.set_xlabel("Time")
host.set_ylabel("State")
par1.set_ylabel("Discomfort Index")
host.set_xlim(time_s[0], time_s[len(time_s) - 1])
host.yaxis.set_major_locator(MultipleLocator(1))
par1.set_ylim(0, 100)
p1, = host.plot(time_s, state, label="State")
p2, = par1.plot(time_t, discomfort, color="r", label="Discomfort Index")
host.legend()
host.axis["left"].label.set_color(p1.get_color())
par1.axis["right"].label.set_color(p2.get_color())
days = mdates.AutoDateLocator()
daysFmt = mdates.DateFormatter("%H:%M")
host.xaxis.set_major_locator(days)
host.xaxis.set_major_formatter(daysFmt)
par1.xaxis.set_major_locator(days)
par1.xaxis.set_major_formatter(daysFmt)
plt.show()
print(discomfort)
def test_polar_box():
fig = plt.figure(figsize=(5, 5))
# PolarAxes.PolarTransform takes radian. However, we want our coordinate
# system in degree
tr = Affine2D().scale(np.pi / 180., 1.) + PolarAxes.PolarTransform()
# polar projection, which involves cycle, and also has limits in
# its coordinates, needs a special method to find the extremes
# (min, max of the coordinate within the view).
extreme_finder = angle_helper.ExtremeFinderCycle(20,
20,
lon_cycle=360,
lat_cycle=None,
lon_minmax=None,
lat_minmax=(0, np.inf))
grid_locator1 = angle_helper.LocatorDMS(12)
tick_formatter1 = angle_helper.FormatterDMS()
grid_helper = GridHelperCurveLinear(tr,
extreme_finder=extreme_finder,
grid_locator1=grid_locator1,
tick_formatter1=tick_formatter1)
ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)
ax1.axis["right"].major_ticklabels.set_visible(True)
ax1.axis["top"].major_ticklabels.set_visible(True)
# let right axis shows ticklabels for 1st coordinate (angle)
ax1.axis["right"].get_helper().nth_coord_ticks = 0
# let bottom axis shows ticklabels for 2nd coordinate (radius)
ax1.axis["bottom"].get_helper().nth_coord_ticks = 1
fig.add_subplot(ax1)
ax1.axis["lat"] = axis = grid_helper.new_floating_axis(0, 45, axes=ax1)
axis.label.set_text("Test")
axis.label.set_visible(True)
axis.get_helper()._extremes = 2, 12
ax1.axis["lon"] = axis = grid_helper.new_floating_axis(1, 6, axes=ax1)
axis.label.set_text("Test 2")
axis.get_helper()._extremes = -180, 90
# A parasite axes with given transform
ax2 = ParasiteAxesAuxTrans(ax1, tr, "equal")
assert ax2.transData == tr + ax1.transData
# Anything you draw in ax2 will match the ticks and grids of ax1.
ax1.parasites.append(ax2)
ax2.plot(np.linspace(0, 30, 50), np.linspace(10, 10, 50))
ax1.set_aspect(1.)
ax1.set_xlim(-5, 12)
ax1.set_ylim(-5, 10)
ax1.grid(True)
def curvelinear_test2(fig):
"""
polar projection, but in a rectangular box.
"""
global ax1
import numpy as np
import mpl_toolkits.axes_grid.angle_helper as angle_helper
from matplotlib.projections import PolarAxes
from matplotlib.transforms import Affine2D
from mpl_toolkits.axes_grid.parasite_axes import SubplotHost
from mpl_toolkits.axes_grid.grid_helper_curvelinear import GridHelperCurveLinear
# see demo_curvelinear_grid.py for details
tr = Affine2D().scale(np.pi / 180., 1.) + PolarAxes.PolarTransform()
extreme_finder = angle_helper.ExtremeFinderCycle(
20,
20,
lon_cycle=360,
lat_cycle=None,
lon_minmax=None,
lat_minmax=(0, np.inf),
)
grid_locator1 = angle_helper.LocatorDMS(12)
tick_formatter1 = angle_helper.FormatterDMS()
grid_helper = GridHelperCurveLinear(tr,
extreme_finder=extreme_finder,
grid_locator1=grid_locator1,
tick_formatter1=tick_formatter1)
ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)
fig.add_subplot(ax1)
# Now creates floating axis
#grid_helper = ax1.get_grid_helper()
# floating axis whose first coordinate (theta) is fixed at 60
ax1.axis["lat"] = axis = ax1.new_floating_axis(0, 60)
axis.label.set_text(r"$\theta = 60^{\circ}$")
axis.label.set_visible(True)
# floating axis whose second coordinate (r) is fixed at 6
ax1.axis["lon"] = axis = ax1.new_floating_axis(1, 6)
axis.label.set_text(r"$r = 6$")
ax1.set_aspect(1.)
ax1.set_xlim(-5, 12)
ax1.set_ylim(-5, 10)
ax1.grid(True)
def setup_rot_axes(fig, rect):
tr = Affine2D().rotate_deg(90.0)
grid_helper = gh.GridHelperCurveLinear(tr)
ax1 = SubplotHost(fig, rect, grid_helper=grid_helper)
fig.add_subplot(ax1)
ax2 = ParasiteAxesAuxTrans(ax1, tr, "equal")
ax1.set_ylim([end, start])
ax1.set_xlim([-8, 4])
ax2 = ax1.get_aux_axes(tr)
ax1.set_aspect('auto')
ax1.axis['top', 'right', 'left', 'bottom'].set_visible(False)
return ax1, ax2
def __init__(self, height, width, X, LY, Xlabel, LYlabel, linecolor,
set_marker, set_linestyle, fontsize, set_markersize,
set_linewidth, set_mfc, set_mew, set_mec):
self.X = X
fig = plt.figure(figsize=(height, width))
self.host = SubplotHost(fig, 111)
plt.rc("font", size=fontsize)
self.host.set_xlabel(Xlabel)
self.host.set_ylabel(LYlabel)
p1, = self.host.plot(X, LY, color=linecolor, marker=set_marker, ls=set_linestyle, ms=set_markersize, lw=set_linewidth, mfc=set_mfc, mew=set_mew, mec=set_mec)
fig.add_axes(self.host)
self.host.axis["left"].label.set_color(p1.get_color())
self.host.tick_params(axis='y', color=p1.get_color())
def get_axis_two_scales(fig, scale_x, scale_y, \
ax2_xlabel = None, ax2_ylabel = None, \
subplot = 111,
sharex = None,
sharey = None):
kargs = {}
if (sharex != None):
kargs['sharex'] = sharex
if (sharey != None):
kargs['sharey'] = sharey
ax1 = SubplotHost(fig, subplot, **kargs)
ax1_to_2 = mtransforms.Affine2D().scale(1.0 / scale_x, 1.0 / scale_y)
ax2 = ax1.twin(ax1_to_2)
ax2.set_viewlim_mode("transform")
fig.add_subplot(ax1)
if (ax2_xlabel != None):
ax2.set_xlabel(ax2_xlabel)
if (ax2_ylabel != None):
ax2.set_ylabel(ax2_ylabel)
if (scale_x == 1.0):
ax2.get_xaxis().set_visible(False)
if (scale_y == 1.0):
ax2.get_yaxis().set_visible(False)
return ax1, ax2
def test_axis_direction():
fig = plt.figure(figsize=(5, 5))
# PolarAxes.PolarTransform takes radian. However, we want our coordinate
# system in degree
tr = Affine2D().scale(np.pi / 180., 1.) + PolarAxes.PolarTransform()
# polar projection, which involves cycle, and also has limits in
# its coordinates, needs a special method to find the extremes
# (min, max of the coordinate within the view).
# 20, 20 : number of sampling points along x, y direction
extreme_finder = angle_helper.ExtremeFinderCycle(20, 20,
lon_cycle=360,
lat_cycle=None,
lon_minmax=None,
lat_minmax=(0, np.inf),
)
grid_locator1 = angle_helper.LocatorDMS(12)
tick_formatter1 = angle_helper.FormatterDMS()
grid_helper = GridHelperCurveLinear(tr,
extreme_finder=extreme_finder,
grid_locator1=grid_locator1,
tick_formatter1=tick_formatter1)
ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)
for axis in ax1.axis.values():
axis.set_visible(False)
fig.add_subplot(ax1)
ax1.axis["lat1"] = axis = grid_helper.new_floating_axis(
0, 130,
axes=ax1, axis_direction="left")
axis.label.set_text("Test")
axis.label.set_visible(True)
axis.get_helper()._extremes = 0.001, 10
ax1.axis["lat2"] = axis = grid_helper.new_floating_axis(
0, 50,
axes=ax1, axis_direction="right")
axis.label.set_text("Test")
axis.label.set_visible(True)
axis.get_helper()._extremes = 0.001, 10
ax1.axis["lon"] = axis = grid_helper.new_floating_axis(
1, 10,
axes=ax1, axis_direction="bottom")
axis.label.set_text("Test 2")
axis.get_helper()._extremes = 50, 130
axis.major_ticklabels.set_axis_direction("top")
axis.label.set_axis_direction("top")
grid_helper.grid_finder.grid_locator1.den = 5
grid_helper.grid_finder.grid_locator2._nbins = 5
ax1.set_aspect(1.)
ax1.set_xlim(-8, 8)
ax1.set_ylim(-4, 12)
ax1.grid(True)
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid.parasite_axes import SubplotHost
import numpy as np
fig = plt.figure(1, (4, 3))
ax = SubplotHost(fig, 111)
fig.add_subplot(ax)
xx = np.arange(0, 2 * np.pi, 0.01)
ax.plot(xx, np.sin(xx))
ax2 = ax.twin() # ax2 is responsible for "top" axis and "right" axis
ax2.set_xticks([0., .5 * np.pi, np.pi, 1.5 * np.pi, 2 * np.pi])
ax2.set_xticklabels(
["0", r"$\frac{1}{2}\pi$", r"$\pi$", r"$\frac{3}{2}\pi$", r"$2\pi$"])
ax2.axis["right"].major_ticklabels.set_visible(False)
plt.draw()
plt.show()
## list: species (y-axis)
## list: number of individuals > 3 (x-axis)
## list: number of pictures > 3 (x-axis)
## value: mean number of pictures genus (horizontal line)
## value: mean number of pictures species (excluding sp.) (horizontal line)
## code runs in pythontex
## read the data from pkl file
with open('/home/stine/repositories/MSCCode/dictinventory.pkl', 'rb') as di:
dictgenusspecies, dictspeciesspnr, dictspnrpath = pickle.load(di)
## initialize figure1 and two with given sizes
## initialize four axes objects, with right y-axis invisible
fig1 = plt.figure(figsize=(10, 4))
ax1 = SubplotHost(fig1, 121)
ax1.axis["right"].set_visible(False)
ax2 = SubplotHost(fig1, 122)
ax2.axis["right"].set_visible(False)
fig2 = plt.figure(figsize=(10, 10))
ax3 = SubplotHost(fig2, 121)
ax3.axis["right"].set_visible(False)
ax4 = SubplotHost(fig2, 122)
ax4.axis["right"].set_visible(False)
## add axes objects to figures
fig1.add_subplot(ax1)
fig1.add_subplot(ax2)
cooutf,
cutoff=[50, 100, 150],
binsize=[0.1, 0.5, 1.0],
lmin=-10.5,
lmax=90.5)
cobinpt1 = readcol(prefix + 'cofillfact_v1.0.2_binsize0.10.txt',
asStruct=True)
cobinpt5 = readcol(prefix + 'cofillfact_v1.0.2_binsize0.50.txt',
asStruct=True)
cobin1 = readcol(prefix + 'cofillfact_v1.0.2_binsize1.00.txt',
asStruct=True)
fig = figure(1)
clf()
from mpl_toolkits.axes_grid.parasite_axes import SubplotHost
host = SubplotHost(fig, 111)
rcParams['xtick.labelsize'] = 24
rcParams['ytick.labelsize'] = 24
rcParams['font.size'] = 24
import scipy.stats as stats
sig3 = 1 - stats.halfnorm.cdf(3)
host.plot(bin1.longitude_bin100,
bin1.fraction_over_300 - sig3,
'k',
drawstyle='steps-mid')
host.set_xlim(-10.5, 90.5)
host.set_ylim(0, 0.35)
host.set_xlabel("Galactic Longitude", fontsize='24')
host.set_ylabel("Fraction above 3$\sigma$ ", fontsize='24')
ax2 = host.twinx()
ax2.plot(cobin1.longitude_bin100,
def curvelinear_test3(fig):
"""
polar projection, but in a rectangular box.
"""
global ax1, axis
import numpy as np
from . import angle_helper
from matplotlib.projections import PolarAxes
from matplotlib.transforms import Affine2D
from mpl_toolkits.axes_grid.parasite_axes import SubplotHost
# PolarAxes.PolarTransform takes radian. However, we want our coordinate
# system in degree
tr = Affine2D().scale(np.pi / 180., 1.) + PolarAxes.PolarTransform()
# polar projection, which involves cycle, and also has limits in
# its coordinates, needs a special method to find the extremes
# (min, max of the coordinate within the view).
# 20, 20 : number of sampling points along x, y direction
extreme_finder = angle_helper.ExtremeFinderCycle(
20,
20,
lon_cycle=360,
lat_cycle=None,
lon_minmax=None,
lat_minmax=(0, np.inf),
)
grid_locator1 = angle_helper.LocatorDMS(12)
# Find a grid values appropriate for the coordinate (degree,
# minute, second).
tick_formatter1 = angle_helper.FormatterDMS()
# And also uses an appropriate formatter. Note that,the
# acceptable Locator and Formatter class is a bit different than
# that of mpl's, and you cannot directly use mpl's Locator and
# Formatter here (but may be possible in the future).
grid_helper = GridHelperCurveLinear(tr,
extreme_finder=extreme_finder,
grid_locator1=grid_locator1,
tick_formatter1=tick_formatter1)
ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)
for axis in list(six.itervalues(ax1.axis)):
axis.set_visible(False)
fig.add_subplot(ax1)
grid_helper = ax1.get_grid_helper()
ax1.axis["lat1"] = axis = grid_helper.new_floating_axis(
0, 130, axes=ax1, axis_direction="left")
axis.label.set_text("Test")
axis.label.set_visible(True)
axis.get_helper()._extremes = 0.001, 10
grid_helper = ax1.get_grid_helper()
ax1.axis["lat2"] = axis = grid_helper.new_floating_axis(
0, 50, axes=ax1, axis_direction="right")
axis.label.set_text("Test")
axis.label.set_visible(True)
axis.get_helper()._extremes = 0.001, 10
ax1.axis["lon"] = axis = grid_helper.new_floating_axis(
1, 10, axes=ax1, axis_direction="bottom")
axis.label.set_text("Test 2")
axis.get_helper()._extremes = 50, 130
axis.major_ticklabels.set_axis_direction("top")
axis.label.set_axis_direction("top")
grid_helper.grid_finder.grid_locator1.den = 5
grid_helper.grid_finder.grid_locator2._nbins = 5
# # A parasite axes with given transform
# ax2 = ParasiteAxesAuxTrans(ax1, tr, "equal")
# # note that ax2.transData == tr + ax1.transData
# # Anthing you draw in ax2 will match the ticks and grids of ax1.
# ax1.parasites.append(ax2)
# intp = cbook.simple_linear_interpolation
# ax2.plot(intp(np.array([0, 30]), 50),
# intp(np.array([10., 10.]), 50))
ax1.set_aspect(1.)
ax1.set_xlim(-5, 12)
ax1.set_ylim(-5, 10)
ax1.grid(True)
def test3():
import numpy as np
from matplotlib.transforms import Transform
from matplotlib.path import Path
class MyTransform(Transform):
input_dims = 2
output_dims = 2
is_separable = False
def __init__(self, resolution):
"""
Create a new Aitoff transform. Resolution is the number of steps
to interpolate between each input line segment to approximate its
path in curved Aitoff space.
"""
Transform.__init__(self)
self._resolution = resolution
def transform(self, ll):
x = ll[:, 0:1]
y = ll[:, 1:2]
return np.concatenate((x, y - x), 1)
transform.__doc__ = Transform.transform.__doc__
transform_non_affine = transform
transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__
def transform_path(self, path):
vertices = path.vertices
ipath = path.interpolated(self._resolution)
return Path(self.transform(ipath.vertices), ipath.codes)
transform_path.__doc__ = Transform.transform_path.__doc__
transform_path_non_affine = transform_path
transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__
def inverted(self):
return MyTransformInv(self._resolution)
inverted.__doc__ = Transform.inverted.__doc__
class MyTransformInv(Transform):
input_dims = 2
output_dims = 2
is_separable = False
def __init__(self, resolution):
Transform.__init__(self)
self._resolution = resolution
def transform(self, ll):
x = ll[:, 0:1]
y = ll[:, 1:2]
return np.concatenate((x, y + x), 1)
transform.__doc__ = Transform.transform.__doc__
def inverted(self):
return MyTransform(self._resolution)
inverted.__doc__ = Transform.inverted.__doc__
import matplotlib.pyplot as plt
fig = plt.figure(1)
fig.clf()
tr = MyTransform(1)
grid_helper = GridHelperCurveLinear(tr)
from mpl_toolkits.axes_grid1.parasite_axes import host_subplot_class_factory
from .axislines import Axes
SubplotHost = host_subplot_class_factory(Axes)
ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)
fig.add_subplot(ax1)
ax2 = ParasiteAxesAuxTrans(ax1, tr, "equal")
ax1.parasites.append(ax2)
ax2.plot([3, 6], [5.0, 10.])
ax1.set_aspect(1.)
ax1.set_xlim(0, 10)
ax1.set_ylim(0, 10)
ax1.grid(True)
plt.draw()
def curvelinear_test2(fig):
"""
polar projection, but in a rectangular box.
"""
# PolarAxes.PolarTransform takes radian. However, we want our coordinate
# system in degree
tr = Affine2D().scale(np.pi / 180., 1.) + PolarAxes.PolarTransform()
# polar projection, which involves cycle, and also has limits in
# its coordinates, needs a special method to find the extremes
# (min, max of the coordinate within the view).
# 20, 20 : number of sampling points along x, y direction
extreme_finder = angle_helper.ExtremeFinderCycle(
20,
20,
lon_cycle=360,
lat_cycle=None,
lon_minmax=None,
lat_minmax=(0, np.inf),
)
grid_locator1 = angle_helper.LocatorDMS(12)
# Find a grid values appropriate for the coordinate (degree,
# minute, second).
tick_formatter1 = angle_helper.FormatterDMS()
# And also uses an appropriate formatter. Note that,the
# acceptable Locator and Formatter class is a bit different than
# that of mpl's, and you cannot directly use mpl's Locator and
# Formatter here (but may be possible in the future).
grid_helper = GridHelperCurveLinear(tr,
extreme_finder=extreme_finder,
grid_locator1=grid_locator1,
tick_formatter1=tick_formatter1)
ax1 = SubplotHost(fig, 1, 2, 2, grid_helper=grid_helper)
# make ticklabels of right and top axis visible.
ax1.axis["right"].major_ticklabels.set_visible(True)
ax1.axis["top"].major_ticklabels.set_visible(True)
# let right axis shows ticklabels for 1st coordinate (angle)
ax1.axis["right"].get_helper().nth_coord_ticks = 0
# let bottom axis shows ticklabels for 2nd coordinate (radius)
ax1.axis["bottom"].get_helper().nth_coord_ticks = 1
fig.add_subplot(ax1)
# A parasite axes with given transform
ax2 = ParasiteAxesAuxTrans(ax1, tr, "equal")
# note that ax2.transData == tr + ax1.transData
# Anthing you draw in ax2 will match the ticks and grids of ax1.
ax1.parasites.append(ax2)
intp = cbook.simple_linear_interpolation
ax2.plot(intp(np.array([0, 30]), 50), intp(np.array([10., 10.]), 50))
ax1.set_aspect(1.)
ax1.set_xlim(-5, 12)
ax1.set_ylim(-5, 10)
ax1.grid(True)
def test_custom_transform():
class MyTransform(Transform):
input_dims = 2
output_dims = 2
is_separable = False
def __init__(self, resolution):
"""
Resolution is the number of steps to interpolate between each input
line segment to approximate its path in transformed space.
"""
Transform.__init__(self)
self._resolution = resolution
def transform(self, ll):
x = ll[:, 0:1]
y = ll[:, 1:2]
return np.concatenate((x, y - x), 1)
transform_non_affine = transform
def transform_path(self, path):
vertices = path.vertices
ipath = path.interpolated(self._resolution)
return Path(self.transform(ipath.vertices), ipath.codes)
transform_path_non_affine = transform_path
def inverted(self):
return MyTransformInv(self._resolution)
class MyTransformInv(Transform):
input_dims = 2
output_dims = 2
is_separable = False
def __init__(self, resolution):
Transform.__init__(self)
self._resolution = resolution
def transform(self, ll):
x = ll[:, 0:1]
y = ll[:, 1:2]
return np.concatenate((x, y+x), 1)
def inverted(self):
return MyTransform(self._resolution)
fig = plt.figure()
SubplotHost = host_subplot_class_factory(Axes)
tr = MyTransform(1)
grid_helper = GridHelperCurveLinear(tr)
ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)
fig.add_subplot(ax1)
ax2 = ParasiteAxesAuxTrans(ax1, tr, "equal")
ax1.parasites.append(ax2)
ax2.plot([3, 6], [5.0, 10.])
ax1.set_aspect(1.)
ax1.set_xlim(0, 10)
ax1.set_ylim(0, 10)
ax1.grid(True)
import matplotlib.transforms as mtransforms
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid.parasite_axes import SubplotHost
obs = [["01_S1", 3.88, 0.14, 1970, 63], ["01_S4", 5.6, 0.82, 1622, 150],
["02_S1", 2.4, 0.54, 1570, 40], ["03_S1", 4.1, 0.62, 2380, 170]]
fig = plt.figure()
ax_kms = SubplotHost(fig, 1, 1, 1, aspect=1.)
# angular proper motion("/yr) to linear velocity(km/s) at distance=2.3kpc
pm_to_kms = 1. / 206265. * 2300 * 3.085e18 / 3.15e7 / 1.e5
aux_trans = mtransforms.Affine2D().scale(pm_to_kms, 1.)
ax_pm = ax_kms.twin(aux_trans)
ax_pm.set_viewlim_mode("transform")
fig.add_subplot(ax_kms)
for n, ds, dse, w, we in obs:
time = ((2007 + (10. + 4 / 30.) / 12) - 1988.5)
v = ds / time * pm_to_kms
ve = dse / time * pm_to_kms
ax_kms.errorbar([v], [w], xerr=[ve], yerr=[we], color="k")
ax_kms.axis["bottom"].set_label("Linear velocity at 2.3 kpc [km/s]")
ax_kms.axis["left"].set_label("FWHM [km/s]")
ax_pm.axis["top"].set_label("Proper Motion [$^{''}$/yr]")
ax_pm.axis["top"].label.set_visible(True)
ax_pm.axis["right"].major_ticklabels.set_visible(False)
def multiplot(metaAry, size=(10, 7.5), dpi=75, grid=True, \
legend=0, fontsize=15, real_label=None, imag_label=None, \
fig=None, host=None, par=None):
"""
metaArray function to do a simple 1D plot of complex array as real and imaginary parts.
legend:
'best' 0
'upper right' 1
'upper left' 2
'lower left' 3
'lower right' 4
'right' 5
'center left' 6
'center right' 7
'lower center' 8
'upper center' 9
'center' 10
"""
if legend is None:
legend = 0
if real_label is None:
real_label = "Real"
if imag_label is None:
imag_label = "Imaginary"
axis = metaAry['range']
rdata = metaAry.data.real
idata = metaAry.data.imag
# Load the plotting ranges and units
x0 = axis['begin'][0]
x1 = axis['end'][0]
ry0 = min(rdata)
ry1 = max(rdata)
iy0 = min(idata)
iy1 = max(idata)
xunit = axis['unit'][0]
ryunit = metaAry['unit']
iyunit = metaAry['unit']
# Leave 10% margin in the y axis
rmean = np.average((ry0, ry1))
rreach = np.abs(ry0 - ry1) / 2 / 0.9
ry0 = np.sign(ry0 - rmean) * rreach + rmean
ry1 = np.sign(ry1 - rmean) * rreach + rmean
imean = np.average((iy0, iy1))
ireach = np.abs(iy0 - iy1) / 2 / 0.9
iy0 = np.sign(iy0 - imean) * ireach + imean
iy1 = np.sign(iy1 - imean) * ireach + imean
# Apply unit prefix if unit is defined
xunit, x0, x1, xscale = prettyunit(xunit, x0, x1)
ryunit, ry0, ry1, ryscale = prettyunit(ryunit, ry0, ry1)
iyunit, iy0, iy1, iyscale = prettyunit(iyunit, iy0, iy1)
if ryscale != 1:
rdata = rdata.copy() * ryscale
if iyscale != 1:
idata = idata.copy() * iyscale
xlabl = lbl_repr(axis['label'][0], xunit)
rylabl = lbl_repr(metaAry['label'], ryunit, real_label + ' part')
iylabl = lbl_repr(metaAry['label'], iyunit, imag_label + ' part')
title = metaAry['name']
fig = figure(figsize=size, dpi=dpi)
host = SubplotHost(fig, 111)
fig.add_subplot(host)
par = host.twinx()
#if axis['log'][0] == False:
# x = linspace(x0, x1, len(metaAry))
#else:
# raise NotImplemented, "Log axis is not yet implemented."
x = metaAry.get_axis()
host.plot(x, rdata, 'b-', label=lbl_repr(axis['label'][0], '', real_label))
par.plot(x, idata, 'r--', label=lbl_repr(axis['label'][0], '', real_label))
host.grid(grid)
host.set_xlabel(xlabl, fontsize=fontsize)
host.set_ylabel(rylabl, fontsize=fontsize)
par.set_ylabel(iylabl, fontsize=fontsize)
host.set_xlim([x0, x1])
host.set_ylim([ry0, ry1])
par.set_ylim([iy0, iy1])
if fontsize is not None:
host.set_title(title, fontsize=int(fontsize * 1.3))
else:
host.set_title(title)
if legend >= 0:
host.legend(loc=legend)
return fig, host, par
