def __init__(self, *args, **kw):
xax = kw.pop("xax", "f")
self.xtrans = ReciprocalTransform()
# self._aux_trans = btf(ReciprocalTransform(), IdentityTransform())
SubplotHost.__init__(self, *args, **kw)
self.parasite = self.twin()
def __init__(self, *args, **kws):
# self.xtrans = kws.pop( 'xtrans', IdentityTransform() )
# self.ytrans = kws.pop( 'ytrans', IdentityTransform() )
self.aux_trans = kws.pop("aux_trans", btf(IdentityTransform(), IdentityTransform()))
# embed()
SubplotHost.__init__(self, *args, **kws) # self.__class__, self
# Initialize the parasite axis
self.parasite = self.twin(self.aux_trans) # ax2 is responsible for "top" axis and "right" axis
def create_datetime_mjd_axes(fig=None, *args, **kwargs):
'''
Create a plot with two x-axis, bottom axis using
dates, top axis using mjd.
Parameters
----------
fig: matplotlib.Figure or None
the figure to use, if None use plt.gcf()
Returns
-------
ax: mpl_toolkits.axes_grid1.parasite_axes.SubplotHost
The ax for the dates
mjd_ax: mpl_toolkits.axes_grid1.parasite_axes.ParasiteAxis
The axis with the mjd axis
'''
if fig is None:
fig = plt.gcf()
if args == []:
ax = SubplotHost(fig, 1, 1, 1, **kwargs)
else:
ax = SubplotHost(fig, *args, **kwargs)
# The second axis shows MJD if the first axis uses dates
mjd_ax = ax.twin(MJD_AXES_TRANSFORM)
mjd_ax.set_viewlim_mode('transform')
# disable unwanted axes
mjd_ax.axis['right'].toggle(ticklabels=False, ticks=False)
mjd_ax.axis['bottom'].toggle(ticklabels=False, ticks=False)
mjd_ax.axis['bottom'].toggle(label=False)
# add/remove label
mjd_ax.axis['top'].set_label('MJD')
# Deactivate offset
mjd_ax.ticklabel_format(useOffset=False)
fig.add_subplot(ax)
return ax, mjd_ax
def make_axes_grid_fig(num=None):
"""Create an mpl Figure and add to it an axes_grid.SubplotHost subplot
(`hostax`).
Returns
-------
fig, hostax
"""
if num is not None:
fig = plt.figure(num)
else:
fig = plt.figure()
hostax = SubplotHost(fig, 111)
fig.add_axes(hostax)
return fig, hostax
def get_axis_two_scales(fig, scale_x = 1.0, scale_y = 1.0, \
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 _plot(p1_infiles,p2_infiles2,bottom_label,left_label,tau_b=1000):
fig = plt.figure(figsize=(12,9))
ax_host = SubplotHost(fig, 1,1,1)
fig.add_subplot(ax_host)
for p1_file, p2_file in zip(p1_infiles,p2_infiles):
p1, p2 = get_ave_ste(p1_file, p2_file, tau_b=1000)
ax_host.errorbar(p1[0],p2[0], xerr=p1[1], yerr=p2[1],label=p1_file[:4])
ax_host.text(p1[0]*1.02,p2[0]*1.02,p1_file[:7])
ax_host.axis["bottom"].set_label(bottom_label)
ax_host.axis["left"].set_label(left_label)
ax_host.grid()
# if wanna legend, uncomment the following line
# plt.legend()
plt.show()
error = np.random.normal(0.,0.1,shape_z)
#Generating noisy data
for j in range (shape_z):
mu[j] = cosmo.dist_modulus(z[j],Omega_m,(1.-Omega_m),h) + error[j]
#generating the fitting function
for j in range (shape_z_ana):
mu_ana[j] = cosmo.dist_modulus(z_ana[j],Omega_m,(1.-Omega_m),h)
#---------------------
#Plotting the analytical models and the data
#-------------------
fig = pl.figure()
host = SubplotHost(fig, 1,1,1)
host.set_xlabel('$z$',fontsize=21)
host.set_ylabel('$\mu$',fontsize=21)
fig.add_subplot(host)
p1 = host.plot(z_ana,mu_ana,'r-',lw=1.5,label="$\Omega_m = 0.3$")
p2 = host.errorbar(z,mu,yerr=0.1,fmt='o',color='k',lw=1.5,label="SN data")
leg = pl.legend(loc=4,fontsize=18)
#host.set_ylim(0,48)
#pl.xticks(visible=False)
#pl.yticks(visible=False)
def plot_area_profile_outline(centered=True, peakwn=None,
set_size=(6.5, 4), ytop=1.2,
wholeblock=False, heights_instead=False,
show_water_ppm=True):
"""
Set up area profile outline and style defaults.
Default is for 0 to be the middle of the profile (centered=True).
"""
fig = plt.figure(figsize=set_size)
ax = SubplotHost(fig, 1,1,1)
fig.add_subplot(ax)
ax_ppm = ax.twinx()
ax_ppm.axis["top"].major_ticklabels.set_visible(False)
if show_water_ppm is True:
pass
else:
ax_ppm.axis["right"].major_ticklabels.set_visible(False)
ax.set_xlabel('Position ($\mu$m)')
# Set y-label
if wholeblock is True:
if heights_instead is False:
ax.set_ylabel('Area/Area$_0$')
else:
ax.set_ylabel('Height/Height$_0$')
else:
if heights_instead is False:
ax.set_ylabel('Area (cm$^{-2}$)')
else:
ax.set_ylabel('Height (cm$^{-1}$)')
ax.set_ylim(0, ytop)
ax.grid()
return fig, ax, ax_ppm
y = 2
for key in indiv_dict:
#pdb.set_trace()
a_hat = indiv_dict[key][0]
se = indiv_dict[key][1]
l95 = float(a_hat) - 1.96 * float(se)
new_se = float(a_hat) - l95
temp_list = [key, a_hat, y, new_se]
y += 1
plot_list.append(temp_list)
figsize = (6, 6)
fig = figure(figsize=figsize, dpi=80)
ax_kms = SubplotHost(fig, 1, 1, 1, aspect=1.)
fig.add_subplot(ax_kms)
## Add overall OR first
summ_lines = summ_handle.readlines()
summ_list = []
for line in summ_lines:
entry = line.rstrip('\n').split(' ')
summ_list.append(entry[2])
#pdb.set_trace()
ov_a_hat = summ_list[0]
ov_se = summ_list[1]
# ov_odds = math.exp(float(ov_a_hat))
##
plt.clf()
plt.plot(MI_vec)
plt.xlabel('MCMC iteration x ' + str(skip))
plt.ylabel('Mutual information')
plt.title('MI, ' + fnname + ' MI: %.5f' % MI)
plt.savefig(os.path.join(outputdir, fnname + '_MItrace.png'))
plt.close()
emat0 = emat_mean
emat = emat0 - emat0.min(axis=0)
# now make the plot
site_seq = plottingutils.getwtseq(infofn[namedict[fn]], barcodefn,
datafnbase)
fig = plt.figure()
ax1 = SubplotHost(fig, 1, 1, 1)
fig.add_subplot(ax1)
ax2 = ax1.twin()
ax1.imshow(emat, interpolation='nearest')
ax1.set_xlabel('Position w.r.t. transcription start site')
ax1.set_yticks([0, 1, 2, 3])
ax1.set_yticklabels(['A', 'C', 'G', 'T'])
# label positions with respect to transcription start site
tick_start = int(start_dict[info_dict['exp_name']]) + int(
info_dict['mut_region_start'])
tick_end = int(start_dict[info_dict['exp_name']]) + int(
info_dict['mut_region_start']) + int(
info_dict['mut_region_length'])
def plotTIMO(r, s, feaCmp, feaEq, filename):
a = r[0,0]; b = r[0,-1]
trX = Q_(1, 'inch').to('mm').magnitude
trY = Q_(1, 'ksi').to('MPa').magnitude
trans = mtransforms.Affine2D().scale(trX,trY)
fig = plt.figure(figsize=(4, 3.5))
ax = SubplotHost(fig, 1, 1, 1)
axa = ax.twin(trans)
axa.set_viewlim_mode("transform")
axa.axis["top"].set_label(r'\textsc{radius}, $r$ (in.)')
axa.axis["top"].label.set_visible(True)
axa.axis["right"].set_label(r'\textsc{stress component}, $\sigma$ (ksi)')
axa.axis["right"].label.set_visible(True)
ax = fig.add_subplot(ax)
ax.plot(r[0,:]*1e3, s.sigmaTheta[0,:]*1e-6, '-', color='C0')
ax.plot((a+feaCmp[:,0])*1e3, feaCmp[:,4]*1e-6, 'o', color='C0')
ax.plot(r[0,:]*1e3, s.sigmaR[0,:]*1e-6, '-', color='C1')
ax.plot((a+feaCmp[:,0])*1e3, feaCmp[:,5]*1e-6, '^', color='C1')
ax.plot(r[0,:]*1e3, s.sigmaZ[0,:]*1e-6, '-', color='C2')
ax.plot((a+feaCmp[:,0])*1e3, feaCmp[:,6]*1e-6, 'v', color='C2')
ax.plot(r[0,:]*1e3, s.sigmaEq[0,:]*1e-6, '-', color='C3')
ax.plot((a+feaEq[:,0])*1e3, feaEq[:,1]*1e-6, 's', color='C3')
ax.plot(r[0,:]*1e3, s.sigmaRTheta[0,:]*1e-6, '-', color='C4')
ax.plot((a+feaCmp[:,0])*1e3, feaCmp[:,7]*1e-6, '+', color='C4')
ax.set_xlabel(r'\textsc{radius}, $r$ (mm)')
ax.set_xlim((a*1e3)-10,(b*1e3)+10)
ax.set_ylabel(r'\textsc{stress component}, $\sigma$ (MPa)')
#ax.set_ylim(-400, 400)
c0line = Line2D([], [], color='C0', marker='o',
label=r'$\sigma_\theta$')
c1line = Line2D([], [], color='C1', marker='^',
label=r'$\sigma_r$')
c2line = Line2D([], [], color='C2', marker='v',
label=r'$\sigma_z$')
c3line = Line2D([], [], color='C3', marker='s',
label=r'$\sigma_\mathrm{eq}$')
c4line = Line2D([], [], color='C4', marker='+',
label=r'$\tau_{r\theta}$')
handles=[c0line, c1line, c2line, c4line, c3line]
labels = [h.get_label() for h in handles]
ax.legend([handle for i,handle in enumerate(handles)],
[label for i,label in enumerate(labels)], loc='best')
fig.tight_layout()
fig.savefig(filename, transparent=True)
plt.close(fig)
def plot_diffusion1D(x_microns,
model,
initial_value=None,
fighandle=None,
axishandle=None,
top=1.2,
style=None,
fitting=False,
show_km_scale=False,
show_initial=True):
"""Takes x and y diffusion data and plots 1D diffusion profile input"""
a_microns = (max(x_microns) - min(x_microns)) / 2.
a_meters = a_microns / 1e3
if fighandle is None and axishandle is not None:
print 'Remember to pass in handles for both figure and axis'
if fighandle is None or axishandle is None:
fig = plt.figure()
ax = SubplotHost(fig, 1, 1, 1)
ax.grid()
ax.set_ylim(0, top)
else:
fig = fighandle
ax = axishandle
if style is None:
if fitting is True:
style = {'linestyle': 'none', 'marker': 'o'}
else:
style = styles.style_lightgreen
if show_km_scale is True:
ax.set_xlabel('Distance (km)')
ax.set_xlim(0., 2. * a_meters / 1e3)
x_km = x_microns / 1e6
ax.plot((x_km) + a_meters / 1e3, model, **style)
else:
ax.set_xlabel('position ($\mu$m)')
ax.set_xlim(-a_microns, a_microns)
ax.plot(x_microns, model, **style)
if initial_value is not None and show_initial is True:
ax.plot(ax.get_xlim(), [initial_value, initial_value], '--k')
ax.set_ylabel('Unit concentration or final/initial')
fig.add_subplot(ax)
return fig, ax
mtransforms.Transform.__init__(self)
def transform_non_affine(self, wl):
return (wl * un.k).to(un.micron, equivalencies=un.spectral()).value
def inverted(self):
return Micron2WNTransform()
aux_trans = mtransforms.BlendedGenericTransform(
Micron2WNTransform(), mtransforms.IdentityTransform())
fig = plt.figure(1)
for n in range(1, 4, 1):
ax_mn = SubplotHost(fig, 1, 3, n)
fig.add_subplot(ax_mn)
ax_mn.set_xlabel('Wavelength (micron)')
# x_micron=np.array([15,10,5,3])
# ax_mn.set_xticks(x_micron)
# ax_mn.set_xlim(5,15)
ax_mn.set_ylim(0, 6)
ax_mn.set_xscale('log')
test_spectrum = np.genfromtxt('sample_spectrum.dpt', delimiter=',')
xvals = 10000 / test_spectrum[:, 0]
data = test_spectrum[:, 1]
ax_wn = ax_mn.twin(aux_trans)
ax_wn.set_viewlim_mode("transform")
def transform_non_affine(self, wl):
return (wl*un.micron).to(un.k, equivalencies=un.spectral()).value
def inverted(self):
return WN2MicronTransform()
aux_trans = mtransforms.BlendedGenericTransform(WN2MicronTransform(), mtransforms.IdentityTransform())
fig = plt.figure(1)
fig.set_size_inches(7,4)
offset=0
ax_wn = SubplotHost(fig, 1, 1, 1)
fig.add_subplot(ax_wn)
ax_mn = ax_wn.twin(aux_trans)
ax_mn.set_viewlim_mode("transform")
# x_micron=np.array([12, 10, 8, 6])
# ax_mn.set_xticks(x_micron)
ax_mn.xaxis.tick_bottom()
ax_mn.tick_params(axis='x', direction = 'in', labelsize=11)
ax_mn.tick_params(axis='y', right=False, labelright=False)
# ax_wn.set_xlim(675, 1700)
ax_wn.invert_xaxis()
# x_wn=np.array([800, 1000, 1200, 1400, 1600])
# ax_wn.set_xticks(x_wn)
def plot(self,
r1=None,
r2=None,
nav_im=None,
norm='log',
scroll_step=1,
alpha=0.3,
cmap=None,
pct=0.1,
mradpp=None,
widget=None):
'''
Interactive plotting of the virtual aperture images.
The sliders control the parameters and may be clicked, dragged or scrolled.
Clicking on inner (r1) and outer (r2) slider labels sets the radii values
to the minimum and maximum, respectively.
Parameters
----------
r1 : scalar
Inner radius of aperture in pixels.
r2 : scalar
Inner radius of aperture in pixels.
nav_im : None or ndarray
Image used for the navigation plot. If None, a blank image is used.
norm : None or string:
If not None and norm='log', a logarithmic cmap normalisation is used.
scroll_step : int
Step in pixels used for each scroll event.
alpha : float
Alpha for aperture plot in [0, 1].
cmap : None or a matplotlib colormap
If not None, the colormap used for both plots.
pct : scalar
Slice image percentile in [0, 50).
mradpp : None or scalar
mrad per pixel.
widget : Pop_Up_Widget
A custom class consisting of mutliple widgets
'''
from matplotlib.widgets import Slider
self._scroll_step = max([1, int(scroll_step)])
self._pct = pct
if norm is not None:
if norm.lower() == 'log':
from matplotlib.colors import LogNorm
norm = LogNorm()
# condition rs
if r1 is not None:
self.r1 = r1
else:
if self.r1 is None:
self.r1 = 0
if r2 is not None:
self.r2 = r2
else:
if self.r2 is None:
self.r2 = int((self.data_shape[-2:] / 4).mean())
self.rc = (self.r2 + self.r1) / 2.0
if nav_im is None:
nav_im = np.zeros(self.data_shape[-2:])
# calculate data
virtual_image = self.annular_slice(self.r1, self.r2)
print("MRADPP", mradpp)
# prepare plots
if mradpp is None:
if widget is not None:
print("True")
docked = widget.setup_docking("Virtual Annular",
"Bottom",
figsize=(8.4, 4.8))
fig = docked.get_fig()
fig.clf()
(ax_nav, ax_cntrst) = fig.subplots(1, 2)
self._f_nav = fig
else:
self._f_nav, (ax_nav, ax_cntrst) = plt.subplots(1,
2,
figsize=(8.4,
4.8))
else:
# add 2nd x-axis
# https://matplotlib.org/examples/axes_grid/parasite_simple2.html
from mpl_toolkits.axes_grid1.parasite_axes import SubplotHost
import matplotlib.transforms as mtransforms
if widget is not None:
print("False")
docked = widget.setup_docking("Virtual Annular",
"Bottom",
figsize=(8.4, 4.8))
self._f_nav = docked.get_fig()
self._f_nav.clf()
else:
self._f_nav = plt.figure(figsize=(8.4, 4.8))
ax_nav = SubplotHost(self._f_nav, 1, 2, 1)
ax_cntrst = SubplotHost(self._f_nav, 1, 2, 2)
aux_trans = mtransforms.Affine2D().scale(1.0 / mradpp, 1.0)
ax_mrad = ax_cntrst.twin(aux_trans)
ax_mrad.set_viewlim_mode("transform")
self._f_nav.add_subplot(ax_nav)
self._f_nav.add_subplot(ax_cntrst)
ax_mrad.axis["top"].set_label('mrad')
ax_mrad.axis["top"].label.set_visible(True)
ax_mrad.axis["right"].major_ticklabels.set_visible(False)
self._f_nav.subplots_adjust(bottom=0.3, wspace=0.3)
if widget is not None:
axr1 = fig.add_axes([0.10, 0.05, 0.80, 0.03])
axr2 = fig.add_axes([0.10, 0.10, 0.80, 0.03])
axr3 = fig.add_axes([0.10, 0.15, 0.80, 0.03])
else:
axr1 = plt.axes([0.10, 0.05, 0.80, 0.03])
axr2 = plt.axes([0.10, 0.10, 0.80, 0.03])
axr3 = plt.axes([0.10, 0.15, 0.80, 0.03])
val_max = self.r_pix.max()
try:
self._sr1 = Slider(axr1,
'r1',
0,
val_max - 1,
valinit=self.r1,
valfmt='%0.0f',
valstep=1)
self._sr2 = Slider(axr2,
'r2',
1,
val_max,
valinit=self.r2,
valfmt='%0.0f',
valstep=1)
except AttributeError:
self._sr1 = Slider(axr1,
'r1',
0,
val_max - 1,
valinit=self.r1,
valfmt='%0.0f')
self._sr2 = Slider(axr2,
'r2',
1,
val_max,
valinit=self.r2,
valfmt='%0.0f')
self._sr3 = Slider(axr3,
'rc',
1,
val_max,
valinit=self.rc,
valfmt='%0.1f')
# these don't seem to work
#self._sr1.slider_max = self._sr2
#self._sr2.slider_min = self._sr1
self._sr1.on_changed(self._update_r_from_slider)
self._sr2.on_changed(self._update_r_from_slider)
self._sr3.on_changed(self._update_rc_from_slider)
ax_nav.imshow(nav_im, norm=norm, cmap=cmap)
ax_nav.set_xlabel('Detector X (pixels)')
ax_nav.set_ylabel('Detector Y (pixels)')
# line plot
r_cntrst_max = int(np.abs(self.data_shape[-2:] - self.cyx).max())
dw = 1
rs = np.arange(dw, r_cntrst_max)
r1, r2 = self.r1, self.r2
sls = np.array([self.annular_slice(r - dw, r) for r in rs])
self.r1, self.r2 = r1, r2
self._contrast_y = np.std(sls, (1, 2))**2 / np.mean(sls, (1, 2))
self._contrast_x = rs - dw / 2.0
ax_cntrst.plot(self._contrast_x, self._contrast_y)
ax_cntrst.minorticks_on()
ax_cntrst.set_xlabel('Radius (pixels)')
ax_cntrst.set_ylabel('Contrast (std^2/mean)')
self._span = ax_cntrst.axvspan(self.r1,
self.r2,
color=[1, 0, 0, 0.1],
ec='r')
# wedges
fc = [0, 0, 0, alpha]
ec = 'r'
from matplotlib.patches import Wedge
self._rmax = val_max + 1
self._w2 = Wedge(self.cyx[::-1],
self._rmax,
0,
360,
width=self._rmax - self.r2,
fc=fc,
ec=ec)
self._w1 = Wedge(self.cyx[::-1],
self.r1,
0,
360,
width=self.r1,
fc=fc,
ec=ec)
ax_nav.add_artist(self._w2)
ax_nav.add_artist(self._w1)
if widget is not None:
docked = widget.setup_docking("Virtual Annular",
"Bottom",
figsize=(8.4, 4.8))
fig = docked.get_fig()
fig.clf()
ax_im = fig.subplots(1, 1)
self._f_im = fig
else:
self._f_im, ax_im = plt.subplots(1, 1)
vmin, vmax = np.percentile(virtual_image, [self._pct, 100 - self._pct])
self._vim = ax_im.imshow(virtual_image,
cmap=cmap,
vmin=vmin,
vmax=vmax)
if widget is not None:
self._cb = fig.colorbar(self._vim)
else:
self._cb = plt.colorbar(self._vim)
self._cb.set_label('Counts')
ax_im.set_xlabel('Scan X (pixels)')
ax_im.set_ylabel('Scan Y (pixels)')
cid = self._f_nav.canvas.mpl_connect('scroll_event', self._onscroll)
self._sr1.label.set_picker(True)
self._sr2.label.set_picker(True)
cid_pick = self._f_nav.canvas.mpl_connect('pick_event', self._onpick)
potential_shapes= saved_data['potential_shapes']
dts = saved_data['dts']
E_t0 = saved_data['E_t0']
E_t1 = saved_data['E_t1']
E_delta = saved_data['E_delta']
NumericalHeating= saved_data['NumericalHeating']
print "base_potentials =", base_potentials
print "potential_shapes =", potential_shapes
print "dts =", dts
fig1 = plot.figure()
fig2 = plot.figure()
figs = [fig1, fig2]
ax1_eV = SubplotHost(fig1, 1,1,1)
ax1_eV_to_Eh = mtransforms.Affine2D().scale(1.0, cst.Eh_to_eV)
ax1_Eh = ax1_eV.twin(ax1_eV_to_Eh)
ax1_Eh .set_viewlim_mode("transform")
fig1.add_subplot(ax1_eV)
ax2_eV = SubplotHost(fig2, 1,1,1)
ax2_eV_to_Eh = mtransforms.Affine2D().scale(cst.eV_to_Eh, cst.Eh_to_eV)
ax2_Eh = ax2_eV.twin(ax2_eV_to_Eh)
ax2_Eh .set_viewlim_mode("transform")
fig2.add_subplot(ax2_eV)
# Plot NumericalHeating as a function of dt for every potential depth
dbase_potentials = (base_potentials[-1] - base_potentials[0]) / float(max_plot-1) # -1 since we want the number of intervals
c = 0
def transform_non_affine(self, wl):
return (wl * un.micron).to(un.k, equivalencies=un.spectral()).value
def inverted(self):
return WN2MicronTransform()
aux_trans = mtransforms.BlendedGenericTransform(
WN2MicronTransform(), mtransforms.IdentityTransform())
fig = plt.figure(1)
name = ['Tetracene', 'Chrysene', 'Pyrene']
for n in range(1, 4, 1):
ax_wn = SubplotHost(fig, 1, 3, n)
fig.add_subplot(ax_wn)
ax_wn.set_xlim(700, 1700)
x_wn = np.array([800, 1000, 1200, 1400, 1600])
ax_wn.tick_params(axis='x',
direction='in',
labelsize=11,
labelbottom=False,
labeltop=True,
top=True,
bottom=False)
ax_wn.tick_params(axis='y', left=False, labelleft=False)
ax_wn.set_xticks(x_wn)
ax_mn = ax_wn.twin(aux_trans)
mu_data[i] = float(columns[2])
sigma_data[i] = float(columns[3])
i += 1
f.close()
print len(sigma_data)
#mu_data = np.array(data.mu(), dtype=dtype)
#z_data = data[:]['z']
#sigma_data = data[:]['sigma']
#---------------------
#Plotting the analytical models and the data
#-------------------
fig = pl.figure()
host = SubplotHost(fig, 1,1,1)
host.set_xlabel('$z$',fontsize=21)
host.set_ylabel('$\mu$',fontsize=21)
fig.add_subplot(host)
p1 = host.plot(z,mu[0,:],'r-',lw=1.5,label="$\Omega_m = 0.2$")
p2 = host.plot(z,mu[1,:],'b--',lw=1.5,label="$\Omega_m = 0.3$")
p3 = host.plot(z,mu[2,:],'k-.',lw=1.5,label="$\Omega_m = 0.4$")
p4 = host.plot(z,mu[3,:],'m:',lw=1.5,label="$\Omega_m = 0.5$")
p5 = host.errorbar(z_data,mu_data,yerr=sigma_data,fmt='o',color='k',lw=1.5,label="SN data")
leg = pl.legend(loc=4,fontsize=18)
#host.set_ylim(0,48)
plt.subplots_adjust(hspace=0.0)
figs_N = 1
fig1_y = 1
fig2_y = 1
else:
fig1 = plot.figure()
fig2 = fig1
figs = [fig1]
figs_N = 2
fig1_y = 1
fig2_y = 2
plt.subplots_adjust(hspace=0.0)
axprops = dict()
if plot_V:
ax_V_Eh = SubplotHost(fig1, figs_N, 1, fig1_y, **axprops)
ax_V_Eh_to_Volt = mtransforms.Affine2D().scale(1.0, cst.eV_to_Eh)
ax_V_Volt = ax_V_Eh.twin(ax_V_Eh_to_Volt)
ax_V_Volt.set_viewlim_mode("transform")
ax_V_Eh.grid(True)
ax_V_Volt.set_ylabel("Potential (Volt)")
ax_V_Eh.set_ylabel("Potential energy of a 1+ (Hartree)")
axprops["sharex"] = ax_V_Volt
plt.setp(ax_V_Volt.get_xticklabels(), visible=False)
# ax_V_Eh.set_title(r"Potential")
if plot_U:
plotStress(g.theta, g.r, s.sigmaZ,
s.sigmaZ.min(), s.sigmaZ.max(),
'S31609_liquidSodium_sigmaZ.pdf')
plotStressAnnotate(g.theta, g.r, s.sigmaEq,
s.sigmaEq.min(), s.sigmaEq.max(),
'right', 'S31609_liquidSodium_sigmaEq.pdf')
headerprint(' Tube property parameter variation ')
iterator='numpy'
nr=12; nt=61
trX = Q_(1, 'ksi').to('MPa').magnitude
trans = mtransforms.Affine2D().scale(trX,1)
fig1 = plt.figure(figsize=(5, 3))
ax1 = SubplotHost(fig1, 1, 1, 1)
ax1a = ax1.twin(trans)
ax1a.set_viewlim_mode("transform")
ax1a.axis["top"].set_label(r'\textsc{max. equiv. stress}, '+\
'$\max\sigma_\mathrm{Eq}$ (ksi)')
ax1a.axis["top"].label.set_visible(True)
ax1a.axis["right"].label.set_visible(False)
ax1a.axis["right"].major_ticklabels.set_visible(False)
ax1a.axis["right"].major_ticks.set_visible(False)
ax1 = fig1.add_subplot(ax1)
P_i = 0e5 # internal pipe pressure
# salt
h_int = 10e3
def plot_em(ifile, lf_file, cmd_file, age, z, track):
print 'Plotting', ifile
logL, logTe, mbol, j, k, mcore, co, dmdt = np.loadtxt(ifile,
usecols=(4, 5, 10, 11, 13, 14, 15, 18),
unpack=True)
nAGB = (mcore == 0)
cAGB = ((co >= 1) & (dmdt <= -5))
oAGB = ((co <= 1) & (logL >= 3.3) & (dmdt < -5))
jk = j - k
bins = np.arange(-10, 20, 0.1)
###### HRD
fig = plt.figure()
ax = fig.add_axes([.1, .1, .8, .8])
ax.plot(logTe[nAGB], logL[nAGB], '.k')
ax.plot(logTe[cAGB], logL[cAGB], 'o', mfc='None', ms=5, mew=1, mec=colorC,
alpha=0.3)
ax.plot(logTe[oAGB], logL[oAGB], 'o', mfc='None', ms=5, mew=1, mec=colorO,
alpha=0.3)
ax.annotate('Age=%.2e' % age, (.7, .1), va='center',
xycoords='axes fraction')
ax.annotate('Z=%.2e' % z, (.7, .15), va='center',
xycoords='axes fraction')
ax.annotate('[M/H]=%.2f' % ztomh(z), (.7, .2), va='center',
xycoords='axes fraction')
ax.annotate(r'$%s$' % track.replace('_', '\ '), (.1, .9), va='center',
xycoords='axes fraction')
ax.set_xlim(ax.get_xlim()[::-1])
#ax.set_ylim(-3.1, -9.5)
ax.set_xlabel(r'$\log\ T_{\\eff}$')
ax.set_ylabel(r'$\log L$')
plt.savefig(cmd_file.replace('cmd', 'hrd'))
###### CMD
fig = plt.figure()
ax = fig.add_axes([.1, .1, .8, .8])
ax.plot(jk[nAGB], k[nAGB], '.k')
ax.plot(jk[cAGB], k[cAGB], 'o', mfc='None', ms=5, mew=1, mec=colorC,
alpha=0.3)
ax.plot(jk[oAGB], k[oAGB], 'o', mfc='None', ms=5, mew=1, mec=colorO,
alpha=0.3)
ax.annotate('Age=%.2e' % age, (.7, .1), va='center',
xycoords='axes fraction')
ax.annotate('Z=%.2e' % z, (.7, .15), va='center',
xycoords='axes fraction')
ax.annotate('[M/H]=%.2f' % ztomh(z), (.7, .2), va='center',
xycoords='axes fraction')
ax.annotate(r'$%s$' % track.replace('_', '\ '), (.1, .9), va='center',
xycoords='axes fraction')
ax.set_xlim(.1, 2.4)
ax.set_ylim(-3.1, -9.5)
ax.set_xlabel(r'$J-K$')
ax.set_ylabel(r'$K$')
plt.savefig(cmd_file)
plt.close()
###### LF
fig = plt.figure()
ax1 = SubplotHost(fig, 2, 1, 1)
ax2 = SubplotHost(fig, 2, 1, 2)
fig.add_subplot(ax1)
fig.add_subplot(ax2)
aux_trans = mtransforms.Affine2D().scale(-2.5, 1.).translate(4.77, 0)
ax_logl = ax1.twin(aux_trans)
ax_logl.set_viewlim_mode('transform')
if sum(cAGB):
pdf, bins, patches = ax1.hist(mbol[cAGB], bins, histtype='stepfilled',
color=colorC)
ax1.set_ylim(0.01, pdf.max()*1.1)
if sum(oAGB):
pdf, bins, patches = ax2.hist(mbol[oAGB], bins, histtype='stepfilled',
color=colorO)
ax2.set_ylim(0.01, pdf.max() * 1.1)
ax1.annotate('Age=%.2e' % age, (.7, .1), va='center',
xycoords='axes fraction')
ax1.annotate('Z=%.2e' % z, (.7, .2), va='center',
xycoords='axes fraction')
ax1.annotate('[M/H]=%.2f' % ztomh(z), (.7, .3), va='center',
xycoords='axes fraction')
ax1.annotate('C-rich', (.7, .8), va='center',
xycoords='axes fraction')
ax2.annotate('O-rich', (.7, .8), va='center',
xycoords='axes fraction')
ax2.annotate(r'$%s$' % track.replace('_', '\ '), (.1, .8), va='center',
xycoords='axes fraction')
for ax in [ax1, ax2]:
ax.set_xlim(0.2, -7.2)
ax.axis['left'].set_label('N')
#ax1.set_xlabel('Log L/L_sun')
ax1.set_ylabel(r'$N$')
ax2.set_ylabel(r'$N$')
ax_logl.axis['right'].major_ticklabels.set_visible(False)
ax1.axis['bottom'].major_ticklabels.set_visible(False)
ax2.axis['bottom'].set_label('M$_\mathsf{bol}$')
ax_logl.annotate(r'$\log L/L_\mathsf{sun}$', (.5, 1.1),
xycoords='axes fraction')
#ax_logl.axis['top'].set_label('log L/L$_{\odot}$')
fig.subplots_adjust(left=.1, bottom=None, right=0.98, top=None,
wspace=0, hspace=0)
plt.savefig(lf_file)
plt.close()
def plotComponentStress(r, sigmaR, sigmaTheta, sigmaZ,
sigmaEq, filename, i, loc):
a = r[0,0]; b = r[0,-1]
trX = Q_(1, 'inch').to('mm').magnitude
trY = Q_(1, 'ksi').to('MPa').magnitude
trans = mtransforms.Affine2D().scale(trX,trY)
fig = plt.figure(figsize=(4, 3.5))
ax = SubplotHost(fig, 1, 1, 1)
axa = ax.twin(trans)
axa.set_viewlim_mode("transform")
axa.axis["top"].set_label(r'\textsc{radius}, $r$ (in.)')
axa.axis["top"].label.set_visible(True)
axa.axis["right"].set_label(r'\textsc{stress component}, $\sigma$ (ksi)')
axa.axis["right"].label.set_visible(True)
ax = fig.add_subplot(ax)
ax.plot(r[i,:]*1e3, sigmaR[i,:]*1e-6, '^-',
label='$\sigma_r$')
ax.plot(r[i,:]*1e3, sigmaTheta[i,:]*1e-6, 'o-',
label=r'$\sigma_\theta$')
ax.plot(r[i,:]*1e3, sigmaZ[i,:]*1e-6, 'v-',
label='$\sigma_z$')
ax.plot(r[i,:]*1e3, sigmaEq[i,:]*1e-6, 's-',
label='$\sigma_\mathrm{eq}$')
ax.set_xlabel(r'\textsc{radius}, $r$ (mm)')
ax.set_xlim((a*1e3)-0.1,(b*1e3)+0.1)
ax.set_ylabel(r'\textsc{stress component}, $\sigma$ (MPa)')
ax.legend(loc=loc)
#labels = ax.get_xticklabels()
#plt.setp(labels, rotation=30)
fig.tight_layout()
fig.savefig(filename, transparent=True)
plt.close(fig)
y = 2
for key in indiv_dict:
#pdb.set_trace()
a_hat = indiv_dict[key][0]
se = indiv_dict[key][1]
l95 = float(a_hat) - 1.96*float(se)
new_se = float(a_hat) - l95
temp_list = [key,a_hat, y, new_se]
y += 1
plot_list.append(temp_list)
figsize = (6,6)
fig = figure(figsize=figsize, dpi=80)
ax_kms = SubplotHost(fig, 1,1,1, aspect=1.)
fig.add_subplot(ax_kms)
## Add overall OR first
summ_lines = summ_handle.readlines()
summ_list =[]
for line in summ_lines:
entry = line.rstrip('\n').split(' ')
summ_list.append(entry[2])
#pdb.set_trace()
ov_a_hat = summ_list[0]
ov_se = summ_list[1]
# ov_odds = math.exp(float(ov_a_hat))
##
def plotNACA(r, sigma, fea, i, filename, loc, ylabel):
a = r[0,0]; b = r[0,-1]
trX = Q_(1, 'inch').to('mm').magnitude
trY = Q_(1, 'ksi').to('MPa').magnitude
trans = mtransforms.Affine2D().scale(trX,trY)
fig = plt.figure(figsize=(4, 3.5))
ax = SubplotHost(fig, 1, 1, 1)
axa = ax.twin(trans)
axa.set_viewlim_mode("transform")
axa.axis["top"].set_label(r'\textsc{radius}, $r$ (in.)')
axa.axis["top"].label.set_visible(True)
axa.axis["right"].set_label(ylabel+' (ksi)')
axa.axis["right"].label.set_visible(True)
ax = fig.add_subplot(ax)
ax.plot(r[0,:]*1e3, sigma[0,:]*1e-6, '-',
color='C0',label=r'$\theta=0^\circ$')
ax.plot((a+fea[0][:,0])*1e3, fea[0][:,i]*1e-6, 'o',
color='C0', markevery=1)
ax.plot(r[0,:]*1e3, sigma[20,:]*1e-6, '-',
color='C1', label=r'$\theta=60^\circ$')
ax.plot((a+fea[1][:,0])*1e3, fea[1][:,i]*1e-6, '^',
color='C1', markevery=1)
ax.plot(r[0,:]*1e3, sigma[40,:]*1e-6, '-',
color='C2', label=r'$\theta=120^\circ$')
ax.plot((a+fea[2][:,0])*1e3, fea[2][:,i]*1e-6, 'v',
color='C2', markevery=1)
ax.plot(r[0,:]*1e3, sigma[60,:]*1e-6, '-',
color='C3', label=r'$\theta=180^\circ$')
ax.plot((a+fea[3][:,0])*1e3, fea[3][:,i]*1e-6, 's',
color='C3', markevery=1)
ax.set_xlabel(r'\textsc{radius}, $r$ (mm)')
ax.set_xlim((a*1e3)-10,(b*1e3)+10)
ax.set_ylabel(ylabel+' (MPa)')
#ax.set_ylim(-400, 400)
c0line = Line2D([], [], color='C0', marker='o',
label=r'$\theta=0^\circ$')
c1line = Line2D([], [], color='C1', marker='^',
label=r'$\theta=60^\circ$')
c2line = Line2D([], [], color='C2', marker='v',
label=r'$\theta=120^\circ$')
c3line = Line2D([], [], color='C3', marker='s',
label=r'$\theta=180^\circ$')
handles=[c0line, c1line, c2line, c3line]
labels = [h.get_label() for h in handles]
ax.legend([handle for i,handle in enumerate(handles)],
[label for i,label in enumerate(labels)], loc=loc)
fig.tight_layout()
fig.savefig(filename, transparent=True)
plt.close(fig)
def Arrhenius_outline(low=6.,
high=11.,
bottom=-18.,
top=-8.,
celsius_labels=np.arange(0, 2000, 100),
figsize_inches=(6, 4),
shrinker_for_legend=0.3,
generic_legend=True,
sunk=-2.,
ncol=2):
"""Make Arrhenius diagram outline. Returns figure, axis, legend handle"""
fig = plt.figure(figsize=figsize_inches)
ax = SubplotHost(fig, 1, 1, 1)
ax_celsius = ax.twin()
parasite_tick_locations = 1e4 / (celsius_labels + 273.15)
ax_celsius.set_xticks(parasite_tick_locations)
ax_celsius.set_xticklabels(celsius_labels)
fig.add_subplot(ax)
ax.axis["bottom"].set_label("10$^4$/Temperature (K$^{-1}$)")
ax.axis["left"].set_label("log$_{10}$diffusivity (m$^{2}$/s)")
ax_celsius.axis["top"].set_label("Temperature ($\degree$C)")
ax_celsius.axis["top"].label.set_visible(True)
ax_celsius.axis["right"].major_ticklabels.set_visible(False)
ax.set_xlim(low, high)
ax.set_ylim(bottom, top)
ax.grid()
# main legend below
legend_handles_main = []
box = ax.get_position()
ax.set_position([
box.x0, box.y0 + box.height * shrinker_for_legend, box.width,
box.height * (1.0 - shrinker_for_legend)
])
main_legend = plt.legend(handles=legend_handles_main,
numpoints=1,
ncol=ncol,
bbox_to_anchor=(low, bottom, high - low, sunk),
bbox_transform=ax.transData,
mode='expand')
plt.gca().add_artist(main_legend)
return fig, ax, legend_handles_main
bulk = np.array([Jaipur.whatIsD(Celsius, orient=direction),
Nushan.whatIsD(Celsius, orient=direction),
-11.02,
-11.1,
Jaipur.whatIsD(Celsius, orient='y')
])
x = np.log10(Al)
#%% Plotting
# setup plot, twin axes, labels
fig = plt.figure(figsize=(3, 4))
gs = gridspec.GridSpec(1,1)
ax = SubplotHost(fig, 1,1,1)
fig.add_subplot(ax)
ax.set_ylim(-12.5, -10.5)
ax_Al = ax.twin()
#Al_labels = list(np.arange(0.0, 0.1, 0.02)) + list(np.arange(0.1, 0.9, 0.1))
Al_labels = [0.01, 0.02, 0.05, 0.1, 0.2]
parasite_tick_locations = np.log10(Al_labels)
ax_Al.set_xticks(parasite_tick_locations)
ax_Al.set_xticklabels(Al_labels)
ax_Al.axis["top"].set_label("IV-Al (a.p.f.u.)")
ax_Al.axis["top"].label.set_visible(True)
ax_Al.axis["right"].major_ticklabels.set_visible(False)
ax.set_ylabel('log$_{10}$ diffusivity$_{H}$ $(m^{-2}/s)$ at 800 $\degree$C')
ax.set_xlabel('log$_{10}$ IV-Al (a.p.f.u.)')
def plotSpectrum(paths):
if type(paths) == str:
paths = [paths]
fig = plt.figure()
fig.patch.set_alpha(0)
ax1 = SubplotHost(fig, 111)
fig.add_subplot(ax1)
for p in paths:
data = np.loadtxt(p, skiprows=9)
ax1.plot(eV_To_nm / data[:, 0], data[:, 1])
ax1.set_ylabel('Intensity (arb. units)')
ax2 = ax1.twin()
ax1.set_xlabel('Energy (eV)')
# ax2 is responsible for "top" axis and "right" axis
# ticks = ax1.get_xticks()
tticks = np.round(eV_To_nm / ax1.get_xticks(), 2)
tticks = np.array(tticks, np.int)
ax2.set_xticks([eV_To_nm / t for t in tticks])
ax2.set_xticklabels(tticks)
#ax2.axis["top"].label.set_visible(True)
ax1.ticklabel_format(axis='y', style='sci', scilimits=(0, 0))
ax2.set_xlabel('Wavelength (nm)')
ax2.set_yticks([])
#def main():
# path = input("Enter the path of your file: ")
# path=path.replace('"','')
# path=path.replace("'",'')
## path = r'C:/Users/sylvain.finot/Documents/data/2019-03-11 - T2597 - 5K/Fil3/TRCL-cw455nm/TRCL.dat'
# plotSpectrum(path)
#
#if __name__ == '__main__':
# main()
import matplotlib.transforms as mtransforms
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.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]")
def make_3DWB_water_profile(final_profile,
water_ppmH2O_initial=None,
initial_profile=None,
initial_area_list=None,
initial_area_positions_microns=None,
show_plot=True,
top=1.2,
fig_ax=None):
"""Take a profile and initial water content.
Returns the whole-block water concentration profile based on
the profile's attribute wb_areas. If wb_areas have not been made,
some initial profile information and various options are passed
to make_3DWB_area_profile().
Default makes a plot showing A/Ao and water on parasite y-axis
"""
fin = final_profile
init = initial_profile
# Set initial water
if water_ppmH2O_initial is not None:
w0 = water_ppmH2O_initial
else:
if fin.sample is not None:
if fin.sample.initial_water is not None:
w0 = fin.sample.initial_water
elif init is not None:
if init.sample is not None:
if init.sample.initial_water is not None:
w0 = init.sample.initial_water
else:
print 'Need initial water content.'
return False
# Set whole-block areas
if (fin.wb_areas is not None) and (len(fin.wb_areas) > 0):
wb_areas = fin.wb_areas
else:
wb_areas = make_3DWB_area_profile(fin, initial_profile,
initial_area_list,
initial_area_positions_microns)
water = wb_areas * w0
if show_plot is True:
# Use a parasite y-axis to show water content
fig = plt.figure()
ax_areas = SubplotHost(fig, 1, 1, 1)
fig.add_subplot(ax_areas)
area_tick_marks = np.arange(0, 100, 0.2)
ax_areas.set_yticks(area_tick_marks)
ax_water = ax_areas.twin()
ax_water.set_yticks(area_tick_marks)
if isinstance(w0, uncertainties.Variable):
ax_water.set_yticklabels(area_tick_marks * w0.n)
else:
ax_water.set_yticklabels(area_tick_marks * w0)
ax_areas.axis["bottom"].set_label('Position ($\mu$m)')
ax_areas.axis["left"].set_label('Final area / Initial area')
ax_water.axis["right"].set_label('ppm H$_2$O')
ax_water.axis["top"].major_ticklabels.set_visible(False)
ax_water.axis["right"].major_ticklabels.set_visible(True)
ax_areas.grid()
ax_areas.set_ylim(0, 1.2)
if fin.len_microns is not None:
leng = fin.len_microns
else:
leng = fin.set_len()
ax_areas.set_xlim(-leng / 2.0, leng / 2.0)
style = fin.choose_marker_style()
ax_areas.plot([-leng / 2.0, leng / 2.0], [1, 1], **style_1)
ax_areas.plot(fin.positions_microns - leng / 2.0, wb_areas, **style)
return water, fig, ax_areas
else:
return water
# parasite_tick_locations = 1e4/(celsius_labels + 273.15)
# ax_celsius.set_xticks(parasite_tick_locations)
# ax_celsius.set_xticklabels(celsius_labels)
# fig.add_subplot(ax)
# ax.axis["bottom"].set_label("10$^4$/Temperature (K$^{-1}$)")
# ax.axis["left"].set_label("log$_{10}$diffusivity (m$^{2}$/s)")
# ax_celsius.axis["top"].set_label("Temperature ($\degree$C)")
# ax_celsius.axis["top"].label.set_visible(True)
# ax_celsius.axis["right"].major_ticklabels.set_visible(False)
# ax.set_xlim(low, high)
# ax.set_ylim(bottom, top)
x = np.log10(Fe)
fig = plt.figure(figsize=(6, 5))
gs = gridspec.GridSpec(1,1)
#ax = plt.subplot(gs[0, 0])
ax = SubplotHost(fig, 1,1,1)
fig.add_subplot(ax)
ax.set_ylim(-17, -10)
ax_Fe = ax.twin()
Fe_labels = list(np.arange(0.0, 0.1, 0.02)) + list(np.arange(0.1, 0.9, 0.1))
parasite_tick_locations = np.log10(Fe_labels)
ax_Fe.set_xticks(parasite_tick_locations)
ax_Fe.set_xticklabels(Fe_labels)
ax_Fe.axis["top"].set_label("Fe (a.p.f.u.)")
ax_Fe.axis["top"].label.set_visible(True)
ax_Fe.axis["right"].major_ticklabels.set_visible(False)
ax.set_ylabel('log$_{10}$ diffusivity$_{H}$ $(m^2/s)$ at 800 $\degree$C')
ax.set_xlabel('log$_{10}$ Fe (a.p.f.u.)')
def diffusion1D(length_microns, log10D_m2s, time_seconds, init=1., fin=0.,
erf_or_sum='erf', show_plot=True,
style=styles.style_blue, infinity=100, points=100,
centered=True, axes=None, symmetric=True,
maximum_value=1.):
"""
Simplest implementation for 1D diffusion.
Takes required inputs length, diffusivity, and time
and plots diffusion curve on new or specified figure.
Optional inputs are unit initial value and final values.
Defaults assume diffusion out, so init=1. and fin=0.
Reverse these for diffusion in.
Change scale of y-values with maximum_value keyword.
Returns figure, axis, x vector in microns, and model y data.
"""
if symmetric is True:
params = params_setup1D(length_microns, log10D_m2s, time_seconds,
init=init, fin=fin)
x_diffusion, y_diffusion = diffusion1D_params(params, points=points)
if centered is False:
a_length = (max(x_diffusion) - min(x_diffusion)) / 2
x_diffusion = x_diffusion + a_length
else:
# multiply length by two
params = params_setup1D(length_microns*2, log10D_m2s, time_seconds,
init=init, fin=fin)
x_diffusion, y_diffusion = diffusion1D_params(params, points=points)
# divide elongated profile in half
x_diffusion = x_diffusion[int(points/2):]
y_diffusion = y_diffusion[int(points/2):]
if centered is True:
a_length = (max(x_diffusion) - min(x_diffusion)) / 2
x_diffusion = x_diffusion - a_length
if show_plot is True:
if axes is None:
fig = plt.figure()
ax = SubplotHost(fig, 1,1,1)
ax.grid()
ax.set_ylim(0, maximum_value)
ax.set_xlabel('position ($\mu$m)')
ax.set_xlim(min(x_diffusion), max(x_diffusion))
ax.plot(x_diffusion, y_diffusion*maximum_value, **style)
ax.set_ylabel('Unit concentration or final/initial')
fig.add_subplot(ax)
else:
axes.plot(x_diffusion, y_diffusion*maximum_value, **style)
fig = None
ax = None
else:
fig = None
ax = None
return fig, ax, x_diffusion, y_diffusion
Omega_m = np.array([0.2,0.3,0.4,0.5])
shape_Omega = np.asarray(Omega_m.shape, dtype=np.int)
shape_z = np.asarray(z.shape, dtype=np.int)
mu = np.zeros((shape_Omega, shape_z), dtype = np.float32, order = 'C')
for i in range (shape_Omega):
for j in range (shape_z):
mu[i,j] = cosmo.dist_modulus(z[j],Omega_m[i],(1.-Omega_m[i]),h)
fig = pl.figure()
host = SubplotHost(fig, 1,1,1)
host.set_xlabel('$z$', fontsize=20)
host.set_ylabel('$\mu$',fontsize=20)
fig.add_subplot(host)
p1 = host.plot(z,mu[0,:],'r-',lw=1.5,label="$\Omega_m = 0.2$")
p2 = host.plot(z,mu[1,:],'b--',lw=1.5,label="$\Omega_m = 0.3$")
p3 = host.plot(z,mu[2,:],'k-.',lw=1.5,label="$\Omega_m = 0.4$")
p4 = host.plot(z,mu[3,:],'m:',lw=1.5,label="$\Omega_m = 0.5$")
leg = pl.legend(loc=4,fontsize=18)
host.set_ylim(0,48)
#pl.xticks(visible=False)
================
"""
import matplotlib.transforms as mtransforms
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.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")
plt.plot(MI_vec)
plt.xlabel('MCMC iteration x '+str(skip))
plt.ylabel('Mutual information')
plt.title('MI, ' + fnname + ' MI: %.5f' % MI )
plt.savefig(os.path.join(outputdir,fnname+'_MItrace.png'))
plt.close()
emat0 = emat_mean
emat = emat0 - emat0.min(axis=0)
# now make the plot
site_seq = plottingutils.getwtseq(infofn[namedict[fn]],barcodefn,datafnbase)
fig = plt.figure()
ax1 = SubplotHost(fig, 1,1,1)
fig.add_subplot(ax1)
ax2 = ax1.twin()
ax1.imshow(emat,interpolation='nearest')
ax1.set_xlabel('Position w.r.t. transcription start site')
ax1.set_yticks([0,1,2,3])
ax1.set_yticklabels(['A','C','G','T'])
# label positions with respect to transcription start site
tick_start = int(start_dict[info_dict['exp_name']])+int(info_dict['mut_region_start'])
tick_end = int(start_dict[info_dict['exp_name']])+int(info_dict['mut_region_start']) + int(info_dict['mut_region_length'])
indices, xtick_labels = clean_up_xticklabels(tick_start,tick_end-tick_start)
# parasite_tick_locations = 1e4/(celsius_labels + 273.15)
# ax_celsius.set_xticks(parasite_tick_locations)
# ax_celsius.set_xticklabels(celsius_labels)
# fig.add_subplot(ax)
# ax.axis["bottom"].set_label("10$^4$/Temperature (K$^{-1}$)")
# ax.axis["left"].set_label("log$_{10}$diffusivity (m$^{2}$/s)")
# ax_celsius.axis["top"].set_label("Temperature ($\degree$C)")
# ax_celsius.axis["top"].label.set_visible(True)
# ax_celsius.axis["right"].major_ticklabels.set_visible(False)
# ax.set_xlim(low, high)
# ax.set_ylim(bottom, top)
x = np.log10(Fe)
fig = plt.figure(figsize=(6, 5))
gs = gridspec.GridSpec(1, 1)
#ax = plt.subplot(gs[0, 0])
ax = SubplotHost(fig, 1, 1, 1)
fig.add_subplot(ax)
ax.set_ylim(-17, -10)
ax_Fe = ax.twin()
Fe_labels = list(np.arange(0.0, 0.1, 0.02)) + list(np.arange(0.1, 0.9, 0.1))
parasite_tick_locations = np.log10(Fe_labels)
ax_Fe.set_xticks(parasite_tick_locations)
ax_Fe.set_xticklabels(Fe_labels)
ax_Fe.axis["top"].set_label("Fe (a.p.f.u.)")
ax_Fe.axis["top"].label.set_visible(True)
ax_Fe.axis["right"].major_ticklabels.set_visible(False)
ax.set_ylabel('log$_{10}$ diffusivity$_{H}$ $(m^2/s)$ at 800 $\degree$C')
ax.set_xlabel('log$_{10}$ Fe (a.p.f.u.)')
def Arrhenius_outline(xlow=6., xhigh=11., ybottom=-18., ytop=-8.,
celsius_labels = np.arange(0, 2000, 100),
shrink_axes_to_fit_legend_by = 0.3, make_legend=False,
lower_legend_by=-2., ncol=2):
"""
Make Arrhenius diagram outline.
Returns figure, axis, legend handle.
low, high, top, and bottom set the x and y axis limits.
celsius_labels sets where to make the temperature tick marks.
If you have issues with the legend position or overlap with main diagram,
play with the numbers for shrink_legend_by and lower_legend_by
ncol sets the number of columns in the legend.
"""
fig = plt.figure()
ax = SubplotHost(fig, 1,1,1)
ax_celsius = ax.twin()
parasite_tick_locations = 1e4/(celsius_labels + 273.15)
ax_celsius.set_xticks(parasite_tick_locations)
ax_celsius.set_xticklabels(celsius_labels)
fig.add_subplot(ax)
ax.axis["bottom"].set_label("10$^4$/Temperature (K$^{-1}$)")
ax.axis["left"].set_label("log$_{10}$diffusivity (m$^{2}$/s)")
ax_celsius.axis["top"].set_label("Temperature ($\degree$C)")
ax_celsius.axis["top"].label.set_visible(True)
ax_celsius.axis["right"].major_ticklabels.set_visible(False)
ax.set_xlim(xlow, xhigh)
ax.set_ylim(ybottom, ytop)
ax.grid()
# main legend below
if make_legend is True:
legend_handles_main = []
box = ax.get_position()
ax.set_position([box.x0, box.y0 + box.height*shrink_axes_to_fit_legend_by,
box.width, box.height*(1.0-shrink_axes_to_fit_legend_by)])
main_legend = plt.legend(handles=legend_handles_main, numpoints=1,
ncol=ncol,
bbox_to_anchor=(xlow, ybottom, xhigh-xlow,
lower_legend_by),
bbox_transform=ax.transData, mode='expand')
plt.gca().add_artist(main_legend)
else:
legend_handles_main = None
return fig, ax, legend_handles_main
