def test_zooming_with_inverted_axes():
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [1, 2, 3])
ax.axis([1, 3, 1, 3])
inset_ax = zoomed_inset_axes(ax, zoom=2.5, loc='lower right')
inset_ax.axis([1.1, 1.4, 1.1, 1.4])
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [1, 2, 3])
ax.axis([3, 1, 3, 1])
inset_ax = zoomed_inset_axes(ax, zoom=2.5, loc='lower right')
inset_ax.axis([1.4, 1.1, 1.4, 1.1])
def plot_us(lats, lons, save_name=None):
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
big_map = Basemap(resolution='h',
lat_0=36, lon_0=-107.5,
llcrnrlat=32, llcrnrlon=-125,
urcrnrlat=43, urcrnrlon=-110)
big_map.drawcoastlines()
big_map.drawstates()
big_map.drawcountries()
big_map.drawmapboundary(fill_color='#7777ff')
big_map.fillcontinents(color='#ddaa66', lake_color='#7777ff', zorder=0)
x, y = big_map(lons, lats)
big_map.plot(x[0], y[0], 'ro', markersize=2)
axins = zoomed_inset_axes(ax, 20, loc=1)
ll_lat, ll_lon = 37.8, -122.78
ur_lat, ur_lon = 38.08, -122.43
axins.set_xlim(ll_lon, ur_lon)
axins.set_ylim(ur_lon, ur_lat)
small_map = Basemap(resolution='h',
llcrnrlat=ll_lat, llcrnrlon=ll_lon,
urcrnrlat=ur_lat, urcrnrlon=ur_lon,
ax=axins)
small_map.drawcoastlines()
small_map.drawmapboundary(fill_color='#7777ff')
small_map.fillcontinents(color='#ddaa66', lake_color='#7777ff', zorder=0)
x, y = small_map(lons, lats)
small_map.plot(x, y, 'ro', markersize=3)
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
if save_name:
fig.savefig(save_name)
def render_input_spectrum():
folder = "/cosma/home/durham/rhgk18/ls_structure/pks"
bao = folder+"/wig.txt"
nbao = folder+"/nowig.txt"
pkbao = np.loadtxt(bao)
pknbao = np.loadtxt(nbao)
fig, ax = plt.subplots()
ax.loglog(pkbao[:,0] ,pkbao[:,1] , color='r', label='With BAO')
ax.loglog(pknbao[:,0] ,pknbao[:,1] , color='b', label='Without BAO')
ax.set_xlabel("Wavenumber, $k$ [$h/Mpc$]", size=28)
ax.set_ylabel("Power, $P(k)$ [$(Mpc/h)^3$]", size=28)
ax.legend(prop={'size':28})
axins = zoomed_inset_axes(ax, 5, loc=3)
axins.loglog(pkbao[:,0] ,pkbao[:,1] , color='r', label='iWith BAO')
axins.loglog(pknbao[:,0] ,pknbao[:,1] , color='b', label='iWithout BAO')
x1, x2, y1, y2 = 0.02, 0.1, 5000, 30000
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
plt.xticks(visible=False)
plt.yticks(visible=False)
mark_inset(ax, axins, loc1=2, loc2=1, fc="none", ec="0.5")
plt.show()
def setup_axes02(fig, rect, zoom=0.35, loc=4, axes_class=None, axes_kwargs=None):
"""
ax2 is an inset axes, but shares the x- and y-axis with ax1.
"""
from mpl_toolkits.axes_grid1.axes_grid import ImageGrid, CbarAxes
import mpl_toolkits.axes_grid1.inset_locator as inset_locator
grid = ImageGrid(fig, rect,
nrows_ncols=(1,1),
share_all=True, aspect=True,
label_mode='L', cbar_mode="each",
cbar_location='top', cbar_pad=None, cbar_size='5%',
axes_class=(axes_class, axes_kwargs))
ax1 = grid[0]
kwargs = dict(zoom=zoom, loc=loc)
ax2 = inset_locator.zoomed_inset_axes(ax1,
axes_class=axes_class,
axes_kwargs=axes_kwargs,
**kwargs
)
cax = inset_locator.inset_axes(ax2, "100%", 0.05, loc=3,
borderpad=0.,
bbox_to_anchor=(0, 0, 1, 0),
bbox_transform=ax2.transAxes,
axes_class=CbarAxes,
axes_kwargs=dict(orientation="top"),
)
ax2.cax = cax
return grid[0], ax2
def plot_target_pred(train_xdata, test_xdata, train_ydata, test_ydata, loc=5):
maxval = max(train_xdata.max(), test_xdata.max(), train_ydata.max(), test_ydata.max())
minval = min(train_xdata.min(), test_xdata.min(), train_ydata.min(), test_ydata.min())
fig, ax = plt.subplots()
ax.plot(train_xdata, train_ydata, '.', label="Train")
ax.plot(test_xdata, test_ydata, '.', label="Test")
plt.legend(loc="best")
ax.plot([minval, maxval], [minval, maxval], '--')
plt.xlabel("Target Value (kcal/mol)")
plt.ylabel("Predicted Value (kcal/mol)")
axins = zoomed_inset_axes(ax, 30, loc=loc) # 30 is zoom, loc is .... nuts
axins.plot(train_xdata, train_ydata, '.', label="Train")
axins.plot(test_xdata, test_ydata, '.', label="Test")
axins.plot([minval, maxval], [minval, maxval], '--')
# sub region of the original image
middle = test_xdata.mean() - 170
x1, x2, y1, y2 = -15+middle, 15+middle, -15+middle, 15+middle
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
mark_inset(ax, axins, loc1=2, loc2=3, fc="none", ec="0.5")
plt.draw()
plt.show()
def inset_momentum_axes(cd):
# TODO: This plot does not refresh correctly, skip the inset
fig = mpl.figure(cd.plotfigure.figno)
axes = fig.add_subplot(111)
# Plot main figure
axes.plot(cd.x, hu_1(cd), 'b-')
axes.plot(cd.x, hu_2(cd), 'k--')
axes.set_xlim(xlimits)
axes.set_ylim(ylimits_momentum)
momentum_axes(cd)
# Create inset plot
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
inset_axes = zoomed_inset_axes(axes, 0.5, loc=3)
inset_axes.plot(cd.x, hu_1(cd), 'b-')
inset_axes.plot(cd.x, hu_2(cd), 'k--')
inset_axes.set_xticklabels([])
inset_axes.set_yticklabels([])
x_zoom = [-120e3,-30e3]
y_zoom = [-10,10]
inset_axes.set_xlim(x_zoom)
inset_axes.set_ylim(y_zoom)
mark_inset(axes, inset_axes, loc1=2, loc2=4, fc='none', ec="0.5")
# mpl.ion()
mpl.draw()
def plot_linear_kernel_pairs(pairs):
xdata, ydata = zip(*pairs)
maxval = max(max(xdata), max(ydata))
fig, ax = plt.subplots()
ax.plot(xdata, ydata, '.')
ax.plot([0, maxval], [0, maxval], '--')
plt.xlabel("Linear Ridge Mean Absolute Error (kcal/mol)")
plt.ylabel("Kernel Ridge Mean Absolute Error (kcal/mol)")
# 15 is the zoom, loc is nuts
axins = zoomed_inset_axes(ax, 15, loc=5)
axins.plot(xdata, ydata, '.')
axins.plot([0, maxval], [0, maxval], '--')
# sub region of the original image
axins.set_xlim(1, 6)
axins.set_ylim(1, 6)
# draw a bbox of the region of the inset axes in the parent axes and
# connecting lines between the bbox and the inset axes area
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
plt.draw()
plt.show()
def create_inset_axes(self):
ax_inset = zoomed_inset_axes(self.ax, 2, loc=1)
cm = plt.get_cmap('winter')
ax_inset.set_color_cycle([cm(1.*i/self.num_graphs)
for i in range(self.num_graphs)])
# hide every other tick label
for label in ax_inset.get_xticklabels()[::4]:
label.set_visible(False)
return ax_inset
def test_gettightbbox():
fig, ax = plt.subplots(figsize=(8, 6))
l, = ax.plot([1, 2, 3], [0, 1, 0])
ax_zoom = zoomed_inset_axes(ax, 4)
ax_zoom.plot([1, 2, 3], [0, 1, 0])
mark_inset(ax, ax_zoom, loc1=1, loc2=3, fc="none", ec='0.3')
remove_ticks_and_titles(fig)
bbox = fig.get_tightbbox(fig.canvas.get_renderer())
np.testing.assert_array_almost_equal(bbox.extents,
[-17.7, -13.9, 7.2, 5.4])
def makeplot(refl, winds, w, stride, map, gs, title, file_name, box=None):
pylab.figure()
axmain = pylab.axes((0, 0.025, 1, 0.9))
gs_x, gs_y = gs
nx, ny = refl.shape
xs, ys = np.meshgrid(gs_x * np.arange(nx), gs_y * np.arange(ny))
pylab.contourf(xs, ys, refl, levels=np.arange(10, 80, 10))
pylab.colorbar()
pylab.contour(xs, ys, w, levels=np.arange(-10, 0, 2), colors='#666666', style='--')
pylab.contour(xs, ys, w, levels=np.arange(2, 12, 2), colors='#666666', style='-')
u, v = winds
wind_slice = tuple([ slice(None, None, stride) ] * 2)
pylab.quiver(xs[wind_slice], ys[wind_slice], u[wind_slice], v[wind_slice])
if box:
lb_y, lb_x = [ b.start for b in box ]
ub_y, ub_x = [ b.stop for b in box ]
box_xs = gs_x * np.array([ lb_x, lb_x, ub_x, ub_x, lb_x])
box_ys = gs_y * np.array([ lb_y, ub_y, ub_y, lb_y, lb_y])
map.plot(box_xs, box_ys, '#660099')
axins = zoomed_inset_axes(pylab.gca(), 4, loc=4)
pylab.sca(axins)
pylab.contourf(xs[box], ys[box], refl[box], levels=np.arange(10, 80, 10))
pylab.contour(xs[box], ys[box], w[box], levels=np.arange(-10, 0, 2), colors='#666666', style='--')
pylab.contour(xs[box], ys[box], w[box], levels=np.arange(2, 12, 2), colors='#666666', style='-')
pylab.quiver(xs[box], ys[box], u[box], v[box])
drawPolitical(map)
pylab.xlim([lb_x * gs_x, ub_x * gs_x - 1])
pylab.ylim([lb_y * gs_y, ub_y * gs_y - 1])
mark_inset(axmain, axins, loc1=1, loc2=3, fc='none', ec='k')
pylab.sca(axmain)
drawPolitical(map)
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
def plotConfusion(confusion, grid, title, file_name, inset=None, fudge=16):
pylab.figure()
axmain = pylab.axes()
tick_labels = [ "Missing", "Correct\nNegative", "False\nAlarm", "Miss", "Hit" ]
min_label = -1
xs, ys = grid.getXY()
pylab.pcolormesh(xs, ys, confusion, cmap=confusion_cmap, vmin=min_label, vmax=(min_label + len(tick_labels) - 1))
tick_locs = np.linspace(-1, min_label + len(tick_labels) - 2, len(tick_labels))
tick_locs += (tick_locs[1] - tick_locs[0]) / 2
bar = pylab.colorbar()
bar.locator = FixedLocator(tick_locs)
bar.formatter = FixedFormatter(tick_labels)
pylab.setp(pylab.getp(bar.ax, 'ymajorticklabels'), fontsize='large')
bar.update_ticks()
grid.drawPolitical()
if inset:
lb_y, lb_x = [ b.start for b in inset ]
ub_y, ub_x = [ b.stop + fudge for b in inset ]
inset_exp = (slice(lb_y, ub_y), slice(lb_x, ub_x))
axins = zoomed_inset_axes(pylab.gca(), 2, loc=4)
pylab.sca(axins)
pylab.pcolormesh(xs[inset_exp], ys[inset_exp], confusion[inset_exp], cmap=confusion_cmap, vmin=min_label, vmax=(min_label + len(tick_labels) - 1))
grid.drawPolitical()
gs_x, gs_y = grid.getGridSpacing()
pylab.xlim([lb_x * gs_x, (ub_x - 1) * gs_x])
pylab.ylim([lb_y * gs_y, (ub_y - 1) * gs_y])
mark_inset(axmain, axins, loc1=1, loc2=3, fc='none', ec='k')
pylab.sca(axmain)
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
def test_inset_locator():
def get_demo_image():
from matplotlib.cbook import get_sample_data
import numpy as np
f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False)
z = np.load(f)
# z is a numpy array of 15x15
return z, (-3, 4, -4, 3)
fig, ax = plt.subplots(figsize=[5, 4])
# prepare the demo image
Z, extent = get_demo_image()
Z2 = np.zeros([150, 150], dtype="d")
ny, nx = Z.shape
Z2[30:30 + ny, 30:30 + nx] = Z
# extent = [-3, 4, -4, 3]
ax.imshow(Z2, extent=extent, interpolation="nearest",
origin="lower")
axins = zoomed_inset_axes(ax, zoom=6, loc='upper right')
axins.imshow(Z2, extent=extent, interpolation="nearest",
origin="lower")
axins.yaxis.get_major_locator().set_params(nbins=7)
axins.xaxis.get_major_locator().set_params(nbins=7)
# sub region of the original image
x1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
plt.xticks(visible=False)
plt.yticks(visible=False)
# draw a bbox of the region of the inset axes in the parent axes and
# connecting lines between the bbox and the inset axes area
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
asb = AnchoredSizeBar(ax.transData,
0.5,
'0.5',
loc='lower center',
pad=0.1, borderpad=0.5, sep=5,
frameon=False)
ax.add_artist(asb)
def draw_inset(plt, m, pos, lat, lon):
ax = plt.subplot(111)
zoom = 50
axins = zoomed_inset_axes(ax, zoom, loc=1)
m.plot(lon, lat, '.b--', zorder=10, latlon=True)
m.scatter(lon, lat, # longitude first!
latlon=True, # lat and long in degrees
zorder=11) # on top of all
x1, y1 = m(lon[1] - 0.005, lat[0] - 0.0025)
x2, y2 = m(lon[1] + 0.005, lat[0] + 0.0025)
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
plt.xticks(visible=False)
plt.yticks(visible=False)
# draw a bbox of the region of the inset axes in the parent axes and
# connecting lines between the bbox and the inset axes area
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
def plot_calibration(c, insert = True):
fig = plt.figure()
ax = plt.gca()
for baseline, correlation in c.time_domain_correlations_values.items():
correlation_max_val = correlation[np.argmax(correlation)]
correlation_max_time = c.time_domain_correlations_times[baseline][np.argmax(correlation)]
lines = ax.plot(
c.time_domain_correlations_times[baseline] * 1e9,
correlation / correlation_max_val,
label = baseline)
#ax.plot([correlation_max_time, correlation_max_time], [0, correlation_max_val], color = lines[0].get_color())
ax.set_ylim(top=1.2)
ax.xaxis.set_ticks(np.arange(
-200,
200,
2))
if insert == True:
#axins = zoomed_inset_axes(ax, 5, loc=1)
axins = zoomed_inset_axes(ax, 9, loc=1)
for baseline, correlation in c.time_domain_correlations_values.items():
correlation_max_val = correlation[np.argmax(correlation)]
correlation_max_time = c.time_domain_correlations_times[baseline][np.argmax(correlation)]
lines = axins.plot(
c.time_domain_correlations_times[baseline] * 1e9,
correlation / correlation_max_val,
label = baseline,
linewidth=2)
#axins.plot([correlation_max_time, correlation_max_time], [0, correlation_max_val], color = lines[0].get_color())
#axins.set_xlim(-0.4, 2.9)
axins.set_xlim(-0.4, 0.4)
#axins.set_ylim(0.90, 1.04)
axins.set_ylim(0.96, 1.03)
#axins.xaxis.set_ticks(np.arange(-0.4, 2.9, 0.4))
axins.xaxis.set_ticks(np.arange(-0.4, 0.4, 0.2))
mark_inset(ax, axins, loc1=2, loc2=3, fc='none', ec='0.5')
plt.xticks(visible=True)
plt.yticks(visible=False)
ax.set_title("Time domain cross correlations with broad band noise\n arriving through full RF chain AFTER calibration")
ax.set_xlabel("Time delay (ns)")
ax.set_ylabel("Cross correlation value (normalised)")
ax.legend(loc=2)
#ax.legend()
plt.show()
def plot_site(options):
options['prefix'] = 'site'
fig = MyFig(options, figsize=(10, 8), legend=True, grid=False, xlabel=r'Probability $p_s$', ylabel=r'Reachability~$\reachability$', aspect='auto')
fig_vs = MyFig(options, figsize=(10, 8), legend=True, grid=False, xlabel=r'Fraction of Forwarded Packets~$\forwarded$', ylabel=r'Reachability~$\reachability$', aspect='auto')
if options['grayscale']:
colors = options['graycm'](pylab.linspace(0, 1.0, 3))
else:
colors = fu_colormap()(pylab.linspace(0, 1.0, 3))
nodes = 105
axins = None
if options['inset_loc'] >= 0:
axins = zoomed_inset_axes(fig_vs.ax, options['inset_zoom'], loc=options['inset_loc'])
axins.set_xlim(options['inset_xlim'])
axins.set_ylim(options['inset_ylim'])
axins.set_xticklabels([])
axins.set_yticklabels([])
mark_inset(fig_vs.ax, axins, loc1=2, loc2=3, fc="none", ec="0.5")
for i, (name, label) in enumerate(names):
data = parse_site_only(options['datapath'][0], name)
rs = numpy.array([r for p, r, std, conf, n, fw, fw_std, fw_conf in data if r <= options['limit']])
fws = numpy.array([fw for p, r, std, conf, n, fw, fw_std, fw_conf in data if r <= options['limit']])/(nodes-1)
ps = numpy.array([p for p, r, std, conf, n, fw, fw_std, fw_conf in data]) #[0:len(rs)])
yerr = numpy.array([conf for p,r,std,conf, n, fw, fw_std, fw_conf in data]) #[0:len(rs)])
patch_collection = PatchCollection([conf2poly(ps, list(rs+yerr), list(rs-yerr), color=colors[i])], match_original=True)
patch_collection.set_alpha(0.3)
patch_collection.set_linestyle('dashed')
fig.ax.add_collection(patch_collection)
fig.ax.plot(ps, rs, label=label, color=colors[i])
fig_vs.ax.plot(fws, rs, label=label, color=colors[i])
if axins:
axins.plot(fws, rs, color=colors[i])
fig.ax.set_ylim(0, options['limit'])
fig.legend_title = 'Graph'
fig.save('graphs')
fig_vs.legend_title = 'Graph'
fig_vs.ax.set_xlim(0,1)
fig_vs.ax.set_ylim(0,1)
fig_vs.save('vs_pb')
def pltimg(imname,loca,xl,xh,yh,yl,descrip):
## Main image
ax=subplot(3,3,loca);
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
ax.text(0.5,0.1,descrip,transform=ax.transAxes,color="white",weight='bold',horizontalalignment='center',fontsize=8)
ax.imshow(imname,cmap="YlOrBr_r");
axins=zoomed_inset_axes(ax,2,loc=1)
axins.imshow(imname,cmap="YlOrBr_r");
## Inset
x1, x2, y1, y2 = xl,xh,yh,yl
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
axins.xaxis.set_visible(False)
axins.yaxis.set_visible(False)
axins.spines['bottom'].set_color('white')
axins.spines['top'].set_color('white')
axins.spines['left'].set_color('white')
axins.spines['right'].set_color('white')
def zoomed_axis(ax=plt.gca(),xlim=[None,None],ylim=[None,None],
remove_ticks=True,zoom=1,borderpad=1,loc=4,**kw):
"""
Creates a (pretty) zoomed axis
Args:
ax: which axis to zoom on
<x/y>_lim: the axes limits
remove_ticks: if true, removes the x and y ticks, to reduce clutter
remaining args: passed to zoomed_inset_axes
Returns:
the inset axis
"""
axins = zoomed_inset_axes(ax, zoom=zoom, loc=loc,borderpad=borderpad)
axins.set_xlim(*xlim) # apply the x-limits
axins.set_ylim(*ylim) # apply the y-limits
if (remove_ticks):
PlotUtilities.no_x_anything(axins)
PlotUtilities.no_y_anything(axins)
return axins
def setup_inset_axes(parent_axes, f_hst, **kwargs):
import mpl_toolkits.axes_grid1.inset_locator as inset_locator
if "zoom" not in kwargs:
kwargs["zoom"] = 4
if "loc" not in kwargs:
kwargs["loc"] = 1
gh3 = pywcsgrid2.GridHelper(wcs=f_hst[0].header)
axes_class = pywcsgrid2.Axes
axes_kwargs=dict(grid_helper=gh3)
ax3 = inset_locator.zoomed_inset_axes(parent_axes,
axes_class=axes_class,
axes_kwargs=axes_kwargs,
**kwargs
)
ax3.axis[:].toggle(all=False)
return ax3
def test_fill_facecolor():
fig, ax = plt.subplots(1, 5)
fig.set_size_inches(5, 5)
for i in range(1, 4):
ax[i].yaxis.set_visible(False)
ax[4].yaxis.tick_right()
bbox = Bbox.from_extents(0, 0.4, 1, 0.6)
# fill with blue by setting 'fc' field
bbox1 = TransformedBbox(bbox, ax[0].transData)
bbox2 = TransformedBbox(bbox, ax[1].transData)
# set color to BboxConnectorPatch
p = BboxConnectorPatch(
bbox1, bbox2, loc1a=1, loc2a=2, loc1b=4, loc2b=3,
ec="r", fc="b")
p.set_clip_on(False)
ax[0].add_patch(p)
# set color to marked area
axins = zoomed_inset_axes(ax[0], 1, loc='upper right')
axins.set_xlim(0, 0.2)
axins.set_ylim(0, 0.2)
plt.gca().axes.get_xaxis().set_ticks([])
plt.gca().axes.get_yaxis().set_ticks([])
mark_inset(ax[0], axins, loc1=2, loc2=4, fc="b", ec="0.5")
# fill with yellow by setting 'facecolor' field
bbox3 = TransformedBbox(bbox, ax[1].transData)
bbox4 = TransformedBbox(bbox, ax[2].transData)
# set color to BboxConnectorPatch
p = BboxConnectorPatch(
bbox3, bbox4, loc1a=1, loc2a=2, loc1b=4, loc2b=3,
ec="r", facecolor="y")
p.set_clip_on(False)
ax[1].add_patch(p)
# set color to marked area
axins = zoomed_inset_axes(ax[1], 1, loc='upper right')
axins.set_xlim(0, 0.2)
axins.set_ylim(0, 0.2)
plt.gca().axes.get_xaxis().set_ticks([])
plt.gca().axes.get_yaxis().set_ticks([])
mark_inset(ax[1], axins, loc1=2, loc2=4, facecolor="y", ec="0.5")
# fill with green by setting 'color' field
bbox5 = TransformedBbox(bbox, ax[2].transData)
bbox6 = TransformedBbox(bbox, ax[3].transData)
# set color to BboxConnectorPatch
p = BboxConnectorPatch(
bbox5, bbox6, loc1a=1, loc2a=2, loc1b=4, loc2b=3,
ec="r", color="g")
p.set_clip_on(False)
ax[2].add_patch(p)
# set color to marked area
axins = zoomed_inset_axes(ax[2], 1, loc='upper right')
axins.set_xlim(0, 0.2)
axins.set_ylim(0, 0.2)
plt.gca().axes.get_xaxis().set_ticks([])
plt.gca().axes.get_yaxis().set_ticks([])
mark_inset(ax[2], axins, loc1=2, loc2=4, color="g", ec="0.5")
# fill with green but color won't show if set fill to False
bbox7 = TransformedBbox(bbox, ax[3].transData)
bbox8 = TransformedBbox(bbox, ax[4].transData)
# BboxConnectorPatch won't show green
p = BboxConnectorPatch(
bbox7, bbox8, loc1a=1, loc2a=2, loc1b=4, loc2b=3,
ec="r", fc="g", fill=False)
p.set_clip_on(False)
ax[3].add_patch(p)
# marked area won't show green
axins = zoomed_inset_axes(ax[3], 1, loc='upper right')
axins.set_xlim(0, 0.2)
axins.set_ylim(0, 0.2)
axins.get_xaxis().set_ticks([])
axins.get_yaxis().set_ticks([])
mark_inset(ax[3], axins, loc1=2, loc2=4, fc="g", ec="0.5", fill=False)
axesb.set_xlabel("$Number$ $of$ $EG's$", fontsize=40)
axesb.xaxis.set_tick_params(labelsize=20)
axesb.xaxis.set_major_formatter(mtick.FormatStrFormatter("%1.e"))
axesb.yaxis.set_tick_params(labelsize=20)
axesb.yaxis.set_major_formatter(mtick.FormatStrFormatter("%1.e"))
###
##########################################
for i in nhj.keys():
axesb.plot(nhjb[i][1], nhja[i][1], ".", markersize=1.0)
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
axins = zoomed_inset_axes(axesb, 3.0, loc=4)
for i in nhj.keys():
axins.plot(nhjb[i][1], nhja[i][1], "o", markersize=4.0)
x1, x2, y1, y2 = 350, 470, 900, 1100 # specify the limits
axins.set_xlim(x1, x2) # apply the x-limits
axins.set_ylim(y1, y2) # apply the y-limits
plt.yticks(visible=False)
plt.xticks(visible=False)
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
mark_inset(axesb, axins, loc1=1, loc2=2, fc="none", ec="0.0")
##############################################
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes, zoomed_inset_axes
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
def add_sizebar(ax, size):
asb = AnchoredSizeBar(ax.transData,
size,
str(size),
loc=8,
pad=0.1, borderpad=0.5, sep=5,
frameon=False)
ax.add_artist(asb)
fig = plt.figure(1, [5.5, 3])
ax = fig.add_subplot(1,2,1)
ax.set_aspect(1.)
axins = inset_axes(ax,
width="30%", # width = 30% of parent_bbox
height=1., # height : 1 inch
loc=3)
plt.xticks(visible=False)
plt.yticks(visible=False)
ax = fig.add_subplot(1,2,2)
ax.set_aspect(1.)
axins = zoomed_inset_axes(ax, 0.5, loc=1) # zoom = 0.5
plt.xticks(visible=False)
plt.yticks(visible=False)
add_sizebar(ax, 0.5)
add_sizebar(axins, 0.5)
plt.draw()
plt.show()
correlation_time_upped *= 1e9
fig = plt.figure()
ax1 = fig.gca()
ax1.plot(correlation_time_upped, correlation_upped, color='b', linewidth=2, label="Upsampled")
ax1.plot(correlation_time, correlation, color='r', linewidth=2, marker='.', markersize=15, label="Raw")
xy_before = (correlation_time[np.argmax(correlation)-1], correlation[np.argmax(correlation)-1])
ax1.annotate('higher', xy=xy_before,
xytext=(0.2, 0.4), textcoords='axes fraction',
arrowprops=dict(facecolor='black', shrink=0.09, width=2),
horizontalalignment='right', verticalalignment='top',)
xy_after = (correlation_time[np.argmax(correlation)+1], correlation[np.argmax(correlation)+1])
ax1.annotate('lower', xy=xy_after,
xytext=(0.6, 0.3), textcoords='axes fraction',
arrowprops=dict(facecolor='black', shrink=0.09, width=2),
horizontalalignment='right', verticalalignment='top',)
axins = zoomed_inset_axes(ax1, 9, loc=1)
axins.plot(correlation_time_upped, correlation_upped, color='b', marker='.', linewidth=2, label="Upsampled")
axins.plot(correlation_time, correlation, color='r', linewidth=2, marker='.', markersize=15, label="Raw")
axins.set_xlim(-3.2, -2.2)
axins.set_ylim(0.97, 1.05)
axins.xaxis.set_ticks(np.arange(-3.4, -1.9, 0.4))
#plt.xticks(visible=False)
plt.yticks(visible=False)
mark_inset(ax1, axins, loc1=2, loc2=3, fc='none', ec='0.5')
ax1.set_title("Comparison of raw time domain cross correlation with upsampled version")
ax1.set_xlabel("Time shift (ns)")
ax1.set_ylabel("Cross correlation value (normalised)")
ax1.legend(loc=2)
z = np.load(f)
# z is a numpy array of 15x15
return z, (-3,4,-4,3)
fig = plt.figure(1, [5,4])
ax = fig.add_subplot(111)
Z, extent = get_demo_image()
ax.set(aspect=1,
xlim=(-15, 15),
ylim=(-20, 5))
axins = zoomed_inset_axes(ax, 2, loc=2) # zoom = 6
im = axins.imshow(Z, extent=extent, interpolation="nearest",
origin="lower")
plt.xticks(visible=False)
plt.yticks(visible=False)
# colorbar
cax = inset_axes(axins,
width="5%", # width = 10% of parent_bbox width
height="100%", # height : 50%
loc=3,
bbox_to_anchor=(1.05, 0., 1, 1),
bbox_transform=axins.transAxes,
borderpad=0,
def plotmustarsigma(self, outputid = 0, zoomperc = 0.05, loc = 2):
'''
Plot the mu* vs sigma chart to interpret the combined effect of both.
Parameters
-----------
zoomperc : float (0-1)
the percentage of the output range to show in the zoomplot,
if 'none', no zoom plot is added
loc : int
matplotlib.pyplot.legend: location code (0-10)
outputid : int
the output to use whe multiple are compared; starts with 0
Returns
---------
fig : matplotlib.figure.Figure object
figure containing the output
ax1 : axes.AxesSubplot object
the subplot
txtobjects : list of textobjects
enbales the ad hoc replacement of labels when overlapping
Notes
-------
Visualization as proposed by [M2]_
'''
mustar2use = self.mustar[outputid*self._ndim:(outputid+1)*self._ndim]
sigma2use = self.sigma[outputid*self._ndim:(outputid+1)*self._ndim]
fig = plt.figure()
ax1 = fig.add_subplot(111)
axs, txtobjects = scatterwithtext(ax1, mustar2use, sigma2use,
self._namelist, 'ks', markersize = 8)
ax1.set_xlabel(r'$\mu^*$', fontsize=20)
ax1.set_ylabel(r'$\sigma$', fontsize=20)
ax1.grid()
ax1.yaxis.grid(linestyle = '--', color = '0.75')
ax1.xaxis.grid(linestyle = '--', color = '0.75')
ax1.set_xlim((0.0, mustar2use.max()+mustar2use.max()*0.1))
ax1.set_ylim((0.0, sigma2use.max()+sigma2use.max()*0.1))
majloc1 = MaxNLocator(nbins=4, prune='lower')
ax1.yaxis.set_major_locator(majloc1)
majloc2 = MaxNLocator(nbins=4)
ax1.xaxis.set_major_locator(majloc2)
if zoomperc != 'none':
#the zooming box size is ad hoc and can be improved
axins = zoomed_inset_axes(ax1, np.floor(1./zoomperc/2.5),
loc = loc)
axins.plot(mustar2use, sigma2use, 'ks', markersize = 3)
transOffset2 = offset_copy(axins.transData, fig=plt.gcf(),
x = -0.05, y=0.10, units='inches')
#plot in the subplot
ct2=0
txtobjects2=[]
for x, y in zip(mustar2use, sigma2use):
if x < mustar2use.max()*zoomperc and y < sigma2use.max()*zoomperc:
axins.plot((x,),(y,), 'ks', markersize = 3)
ls = axins.text(x, y, '%s' %self._namelist[ct2],
transform=transOffset2, color='k')
txtobjects2.append(ls)
ct2+=1
#zoomplot with labels right
axins.yaxis.set_ticks_position('right')
#set the limits of the zoom plot
axins.set_xlim((0.0, mustar2use.max()*zoomperc))
axins.set_ylim((0.0, sigma2use.max()*zoomperc))
#only minor number of ticks for cleaner overview
majloc3 = MaxNLocator(nbins=3, prune='lower')
axins.yaxis.set_major_locator(majloc3)
majloc4 = MaxNLocator(nbins=3, prune='lower')
axins.xaxis.set_major_locator(majloc4)
#smaller size for the ticklabels (different is actually not needed)
for tickx in axins.xaxis.get_major_ticks():
tickx.label.set_fontsize(10)
for label in axins.yaxis.get_majorticklabels():
label.set_fontsize(10)
label.set_rotation('vertical')
#create the subarea-plot in main frame and connect
mark_inset(ax1, axins, loc1=2, loc2=4, fc='none', ec='0.8')
axins.grid()
axins.yaxis.grid(linestyle = '--', color = '0.85')
axins.xaxis.grid(linestyle = '--', color = '0.85')
return fig, ax1, txtobjects
def map_ssp_view(self):
"""plot all the ssp in the database"""
view_rows = self._get_all_ssp_view_rows()
if view_rows is None:
raise DbError("Unable to retrieve ssp view rows > Empty database?")
if len(view_rows) == 0:
raise DbError("Unable to retrieve ssp view rows > Empty database?")
# prepare the data
ssp_x = list()
ssp_y = list()
ssp_label = list()
for vr in view_rows:
ssp_x.append(vr[b'cast_position'].x)
ssp_y.append(vr[b'cast_position'].y)
ssp_label.append(vr[b'pk'])
ssp_x_min = min(ssp_x)
ssp_x_max = max(ssp_x)
ssp_x_mean = (ssp_x_min + ssp_x_max) / 2
ssp_x_delta = max(0.05, abs(ssp_x_max - ssp_x_min)/5)
ssp_y_min = min(ssp_y)
ssp_y_max = max(ssp_y)
ssp_y_mean = (ssp_y_min + ssp_y_max) / 2
ssp_y_delta = max(0.05, abs(ssp_y_max - ssp_y_min)/5)
log.info("data boundary: %.4f, %.4f (%.4f) / %.4f, %.4f (%.4f)"
% (ssp_x_min, ssp_x_max, ssp_x_delta, ssp_y_min, ssp_y_max, ssp_y_delta))
# make the world map
fig = plt.figure()
#plt.title("SSP Map (%s profiles)" % len(view_rows))
ax = fig.add_subplot(111)
plt.ioff()
wm = self._world_draw_map()
# x, y = wm(ssp_x_mean, ssp_y_mean)
# wm.scatter(x, y, s=18, color='y')
if ssp_x_mean > 0:
ssp_loc = 2
else:
ssp_loc = 1
max_delta_range = max(abs(ssp_x_min-ssp_x_max),abs(ssp_y_min-ssp_y_max))
log.info("maximum delta range: %s" % max_delta_range)
if max_delta_range > 15:
ins_scale = 6
elif max_delta_range > 12:
ins_scale = 9
elif max_delta_range > 6:
ins_scale = 12
elif max_delta_range > 3:
ins_scale = 15
else:
ins_scale = 18
ax_ins = zoomed_inset_axes(ax, ins_scale, loc=ssp_loc)
ax_ins.set_xlim((ssp_x_min - ssp_x_delta), (ssp_x_max + ssp_x_delta))
ax_ins.set_ylim((ssp_y_min - ssp_y_delta), (ssp_y_max + ssp_y_delta))
m = self._inset_draw_map(llcrnrlon=(ssp_x_min - ssp_x_delta), llcrnrlat=(ssp_y_min - ssp_y_delta),
urcrnrlon=(ssp_x_max + ssp_x_delta), urcrnrlat=(ssp_y_max + ssp_y_delta),
ax_ins=ax_ins)
x, y = m(ssp_x, ssp_y)
m.scatter(x, y, marker='o', s=16, color='r')
m.scatter(x, y, marker='.', s=1, color='k')
if ssp_x_mean > 0:
mark_inset(ax, ax_ins, loc1=1, loc2=4, fc="none", ec='y')
else:
mark_inset(ax, ax_ins, loc1=2, loc2=3, fc="none", ec='y')
ax_ins.tick_params(color='y', labelcolor='y')
for spine in ax_ins.spines.values():
spine.set_edgecolor('y')
#fig.tight_layout()
plt.show()
axmain[2].text(0.02,0.02,'Impedance\n mismatched',transform=axmain[2].transAxes)
axx[2].set_ylim((-0.35,0.35))
axy[2].set_xlim((-0.2,0.2))
axx[2].xaxis.set_label_coords(1, -0.5)
# Bottom right
axmain[3].text(0.98,0.02,'Both\n mismatched',horizontalalignment='right',transform=axmain[3].transAxes)
axx[3].set_ylim((-0.27,0.27))
axy[3].set_xlim((-0.21,0.21))
#Inset
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
axins = zoomed_inset_axes(axmain[2],6,loc=1)
#skip = 1
#K = np.loadtxt('K.txt')
#rho = np.loadtxt('rho.txt')[::skip,::skip]
#eps = np.loadtxt('sw4_q0.txt')[::skip,::skip]
#sigma = np.exp(K*eps[-1])-1
#xmom = np.loadtxt('sw4_q1.txt')[::skip,::skip]
#ymom = np.loadtxt('sw4_q2.txt')[::skip,::skip]
#u = xmom/rho
#v = ymom/rho
#x = np.linspace(0,150,9600)
#y = np.linspace(0,1,128)
#X,Y = np.meshgrid(x,y,indexing='ij')
#x_skip = 8
def map_showXY(map12,map12m,map12my,map12mx,map13,map13m,map18,map18m,ta12,ta13,Tx12,Tx13,Tx18,wcs,y,x,dy,dx,dv,gf,s):
"""
To use with IPython interact
"""
#===================================Fitting======================
#gf=0.2 #gooud fit parameter ~10%
index_deviation=[10,2] #Max and Min Index Deviation for Second Derivative Mask
xx=velocity
#===========CO12=======================
s12=Spectra(map12,y,x)
#----mask------
der2=np.diff(s12,2.) #Second Derivative
m = np.ones(len(s12), dtype=bool)
ind1=np.argsort(der2)[0]+1
ind2=np.argsort(der2)[1]+1
i=0
while (np.abs(ind1-ind2)>index_deviation[0] or np.abs(ind1-ind2)<index_deviation[1]):
#print np.abs(ind1-ind2),np.abs(ind1-ind2)>15
ind2=np.argsort(der2)[i]+1
i=i+1
m[ind1:ind2]=False
m[ind2:ind1]=False
#---end of mask-----
try:
popt12, pcov12 = curve_fit(gaussian, xx[m], s12[m],p0=[s12.max(),xx[np.argmax(s12)],1.5],diag=(0.01,0.01))
except:
popt12,pcov12=np.zeros((3)),np.zeros((3,3))
sd12= np.sqrt(np.diag(pcov12)) #Standard Deviation
fit12 = (sd12<gf*np.abs(popt12)).all() #Good Fit?
FWHM12=2.355*np.abs(popt12[2]) #Fitted Full Width Half Maximum
FWTM12=1.865*FWHM12
outflow=False
if (FWTM12>4.):
outflow=True
FWHM12t=0.00001*2.355*np.sqrt(k_b*Tx12[y,x]/(m_CO12*amu)) #theoretical (thermal)
########################################
# s912=Spectra9(map12,y,x)[0]
# popt912, pcov912 = curve_fit(gaussian, xx, s912,p0=[s912.max(),xx[np.argmax(s12)],1.5],diag=(0.01,0.01))
# sd912= np.sqrt(np.diag(pcov912))
#===========CO13=======================
s13=Spectra(map13,y,x)
try:
popt13, pcov13 = curve_fit(gaussian, xx, s13,p0=[0.25*popt12[0],popt12[1],popt12[2]],diag=(0.005,0.005))
except:
popt13,pcov13=np.zeros((3)),np.zeros((3,3))
sd13= np.sqrt(np.diag(pcov13)) #Standard Deviation
fit13 = (sd13<gf*np.abs(popt13)).all() #Good Fit?
FWHM13=2.355*np.abs(popt13[2]) #Fitted Full Width Half Maximum
FWHM13t=0.00001*2.355*np.sqrt(k_b*Tx13[y,x]/(m_CO13*amu)) #theoretical (thermal)
########################################################
# s913=Spectra9(map13,y,x)[0]
# popt913, pcov913 = curve_fit(gaussian, xx, s913,p0=[0.25*popt912[0],popt912[1],popt912[2]],diag=(0.1,0.1))
# sd913= np.sqrt(np.diag(pcov913))
#===========CO18=======================
s18=Spectra(map18,y,x)
try:
popt18, pcov18 = curve_fit(gaussian, xx, s18,p0=[0.25*popt13[0],popt13[1],popt13[2]],diag=(0.001,0.001))
except:
popt18,pcov18=np.zeros((3)),np.zeros((3,3))
sd18= np.sqrt(np.diag(pcov18)) #Standard Deviation
fit18 = (sd18<gf*np.abs(popt18)).all() #Good Fit?
FWHM18=2.355*np.abs(popt18[2]) #Fitted Full Width Half Maximum
FWHM18t=0.00001*2.355*np.sqrt(k_b*Tx18[y,x]/(m_C18O*amu)) #theoretical (thermal)
#================TABLE========================================
col1=['$^{12}CO$ $T_{B}$','$^{13}CO$ $T_{B}$','$C^{18}O$ $T_{B}$',r'$\tau^{12}$',r'$\tau^{13}$','$^{12}CO$ $T_{X}$','$^{13}CO$ $T_{X}$',r'$^{12}CO$ FWHM',r'$^{12}CO$ FWTM',r'$^{13}CO$ FWHM',r'$C^{18}O$ FWHM',r'$^{12}CO$ Integral',r'$^{12}CO$ Core Integral', r'$^{12}CO$ Wings Integral','$^{12}CO$ Mass','$^{12}CO$ Core Mass','$^{12}CO$ Wings Mass',r'$^{12}CO$ Blue Wing Speed (Absolute)',r'$^{12}CO$ Red Wing Speed (Absolute)',r'$^{12}CO$ (Blue,Right) Wings Momentum']
i12all=s12.sum()*vres
i12b=s12[xx<popt13[1]-FWHM13/2].sum()*vres if fit13 else np.nan
i12r=s12[xx>popt13[1]+FWHM13/2].sum()*vres if fit13 else np.nan
i12=i12b+i12r
#i12=(s12[xx<popt13[1]-FWHM13/2].sum()+s12[xx>popt13[1]+FWHM13/2].sum())*vres if fit13 else np.nan
i12core=i12all-i12 if fit13 else np.nan
Mb=Integral_to_mass(i12b,Tx12[y,x],ta12[y,x]) if i12b else np.nan
Mr=Integral_to_mass(i12r,Tx12[y,x],ta12[y,x]) if i12r else np.nan
MG= Integral_to_mass(i12,Tx12[y,x],ta12[y,x]) if i12 else np.nan
MGall=Integral_to_mass(i12all,Tx12[y,x],ta12[y,x]) if i12 else np.nan
MGcore=Integral_to_mass(i12core,Tx12[y,x],ta12[y,x]) if i12 else np.nan
Vrel=popt13[1]
xx1=np.logical_and(xx<=Vrel-FWHM13/2,xx>=Vrel-10.)
xx2=np.logical_and(xx>=Vrel+FWHM13/2,xx<=Vrel+10.)
Vb=(xx[xx1]*s12[xx1]).sum()/s12[xx1].sum()
Vr=(xx[xx2]*s12[xx2]).sum()/s12[xx2].sum()
Pb=Mb*Vb/np.cos(57.3)
Pr=Mr*Vr/np.cos(57.3)
if outflow:
si='%0.2f $K\,km\,s^{-1}$'%i12
sm='%0.2f $M_{\odot}$ ($M_{B}$: %0.2f, $M_{R}$: %0.2f) // Ratio (all): %0.2f (core): %0.2f'%(MG,Mb,Mr,MG/MGall,MG/MGcore)
siall='%0.2f $K\,km\,s^{-1}$'%i12all
small='%0.2f $M_{\odot}$'%MGall
sicore='%0.2f $K\,km\,s^{-1}$'%i12core
smcore='%0.2f $M_{\odot}$'%MGcore
else:
si='0.0 (%0.2f) $K\,km\,s^{-1}$'%i12
sm='0.0 (%0.2f) $M_{\odot}$'%MG
siall='0.0 (%0.2f) $K\,km\,s^{-1}$'%i12all
small='0.0 (%0.2f) $M_{\odot}$'%MGall
sicore='0.0 (%0.2f) $K\,km\,s^{-1}$'%i12core
smcore='0.0 (%0.2f) $M_{\odot}$'%MGcore
col2=np.array(['%0.2f'%map12m[y,x],'%0.2f'%map13m[y,x],'%0.2f'%map18m[y,x],'%0.2f'%ta12[y,x],'%0.2f'%ta13[y,x],'%0.2f K'%Tx12[y,x],'%0.2f K'%Tx13[y,x],'%0.2f $km\,s^{-1}$ (thermal: %0.2f)'%(FWHM12,FWHM12t),'%0.2f $km\,s^{-1}$'%FWTM12,'%0.2f $km\,s^{-1}$ (thermal: %0.2f)'%(FWHM13,FWHM13t),'%0.2f $km\,s^{-1}$ (thermal: %0.2f)'%(FWHM18,FWHM18t),siall,sicore,si,small,smcore,sm,'%0.2f (%0.2f) $km\,s^{-1}$'%(Vb-Vrel,Vb),'%0.2f (%0.2f) $km\,s^{-1}$'%(Vr-Vrel,Vr),'%0.2f,%0.2f // %0.2f $M_{\odot} km\,s^{-1}$'%(Pb,Pr,Pb+Pr)])
t=Table([col1,col2],names=('Name','Value'),meta={'name': 'first table'})
display(t)
#col1=np.array(['$^{12}CO$ $T_{B}$','%0.2f'%map12m[y,x]])
#col2=np.array(['$^{13}CO$ $T_{B}$','%0.2f'%map13m[y,x]])
#col3=np.array(['$C^{18}O$ $T_{B}$','%0.2f'%map18m[y,x]])
#col4=np.array([r'$\tau^{12}$','%0.2f'%ta12[y,x]])
#col5=np.array([r'$\tau^{13}$','%0.2f'%ta13[y,x]])
#col6=np.array(['$^{12}CO$ $T_{X}$','%0.2f K'%Tx12[y,x]])
#col7=np.array(['$^{13}CO$ $T_{X}$','%0.2f K'%Tx13[y,x]])
#col8=np.array([r'$^{12}CO$ FWHM','%0.2f $km\,s^{-1}$ (thermal: %0.2f)'%(FWHM12,FWHM12t)])
#col9=np.array([r'$^{13}CO$ FWHM','%0.2f $km\,s^{-1}$'%FWTM12])
#col10=np.array(['$^{13}CO$ $T_{B}$','%0.2f $km\,s^{-1}$ (thermal: %0.2f)'%(FWHM13,FWHM13t)])
#col11=np.array([r'$C^{18}O$ FWHM','%0.2f $km\,s^{-1}$ (thermal: %0.2f)'%(FWHM18,FWHM18t)])
#col12=np.array([r'$^{12}CO$ Wings Integral',si])
#col13=np.array(['$^{12}CO$ Wings Mass',sm])
#t=Table([col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11,col12,col13])
#display(t)
#t.write('test.tex',format='latex')
#================Print==============================================
# print 'tau12: %0.3f'%ta12[y,x]
# print 'tau13: %0.3f'%ta13[y,x]
# print '12CO Excitation Temperature: %0.2f'%Tx12[y,x]
# print '13CO Excitation Temperature: %0.2f'%Tx13[y,x]
# print 'C18O Excitation Temperature: %0.2f'%Tx18[y,x]
# print '12CO FWHM: %0.2f km/s || Theoretical (thermal): %0.2f km/s (Ratio:%0.2f) || Larson L: %0.2f pc || Larson M: %0.2f Mo'%(FWHM12,FWHM12t,FWHM12/FWHM12t,(FWHM12/1.1)**(1/0.38),(FWHM12/0.42)**(1/0.2))
# print '13CO FWHM: %0.2f km/s || Theoretical (thermal): %0.2f km/s (Ratio:%0.2f) || Larson L: %0.2f pc || Larson M: %0.2f Mo'%(FWHM13,FWHM13t,FWHM13/FWHM13t,(FWHM13/1.1)**(1/0.38),(FWHM13/0.42)**(1/0.2))
# print 'C18O FWHM: %0.2f km/s || Theoretical (thermal): %0.2f km/s (Ratio:%0.2f) || Larson L: %0.2f pc || Larson M: %0.2f Mo'%(FWHM18,FWHM18t,FWHM18/FWHM18t,(FWHM18/1.1)**(1/0.38),(FWHM18/0.42)**(1/0.2))
# if fit13:
# print 'Wings Integral in $^{12}CO$: %0.2f K'%(s12[xx<popt13[1]-FWHM13/2].sum()+s12[xx>popt13[1]+FWHM13/2].sum())
#===========================================================================
#===========PLOTS=======================
#===========================================================================
plt.rcParams['figure.figsize'] = 22, 35
#dy,dx,dv=20,20,10
vmax=np.argmax(s13)
v1,v2=vmax-dv,vmax+dv
#===========================================
#===Map=====================================
gs = gridspec.GridSpec(11, 8)
#====ax1-Main Map===============================
ax1=plt.subplot(gs[1:5,2:7]) #Axis for Main Map
ax1.set_title('$^{12}CO$ Excitation Temperatures')
#mim=ax1.imshow(map12m,origin='low',cmap='coolwarm')
mim=ax1.imshow(Tx12.data,origin='low',cmap='coolwarm') #Excitation Map
ax1.axvline(x=x,color='k',ls='dashed',linewidth=1.0,alpha=0.6) #Current X
ax1.annotate('$x$=%d'%x,(x+5,5),color='k',size=20)
ax1.axhline(y=y,color='k',ls='dashed',linewidth=1.0,alpha=0.6) #Current Y
ax1.annotate('$y$=%d'%y,(5,y+5),color='k',size=20)
ax1bar = inset_axes(ax1, width='2%', height='40%', loc=4) #axis for colorbar
ax1.annotate(s='', xy=(530,40), xytext=(579,40), arrowprops={'arrowstyle':'|-|','linewidth':2.0})
ax1.text(542,43,'3 pc',color='white',fontsize=15)
plt.colorbar(mim,cax=ax1bar) #colorbar
#=====================================
axv0=plt.subplot(gs[0,2:7],sharex=ax1) #Axis for (X,Velocity) Map
axv0.set_title(' $^{12}CO$ Max $T_{B}$')
lev=np.linspace(0,map12mx.max(),20) #Contourf Levels
mimx=axv0.contourf(np.arange(0,map12mx.shape[1]),velocity,map12mx,levels=lev,cmap='coolwarm')
axv0.xaxis.tick_top()
axv0.axvline(x=x,color='k',ls='dashed',linewidth=1.0,alpha=0.75) #Current X
axv1=plt.subplot(gs[1:5,7],sharey=ax1) #Axis for (X,Velocity) Map
axv1.set_title('$^{12}CO$ Max $T_{B}$')
lev=np.linspace(0,map12my.max(),20) #Contourf Levels
mimx=axv1.contourf(velocity,np.arange(0,map12my.shape[1]),np.rot90(map12my,3),levels=lev,cmap='coolwarm')
axv1.yaxis.tick_right()
axv1.axhline(y=y,color='k',ls='dashed',linewidth=1.0,alpha=0.75) #Current Y
#===========================
#==Zoom=======
x1, x2, y1, y2 = x-dx, x+dx, y-dy, y+dy #Define Region
region12=map12m[y1:y2,x1:x2]
region13=map13m[y1:y2,x1:x2]
if ((x+dx>350) and (y+dy>250)): #dx=20, 7
axins = zoomed_inset_axes(ax1, 140./np.max([dx,dy]), loc=3) #Axis for Zoom
else:
axins = zoomed_inset_axes(ax1, 140./np.max([dx,dy]), loc=1) #Axis for Zoom
#axins.set_title('$^{12}CO$ and $^{13}CO$ (contours) \n max$T_{B}$ $(K)$')
c12=axins.contourf(map12m,levels=np.linspace(region12.min(),region12.max(),15))
c13=axins.contour(map13m,cmap='gnuplot',levels=np.linspace(region13.min(),region13.max(),7),alpha=0.75)
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
mark_inset(ax1, axins, loc1=2, loc2=3, fc="none", ec="1.",lw=2.)
axins.set_title('Max $T_{B}$ Contours:$^{13}CO$ \n Max $T_{B}$ Filled Contours:$^{12}CO$ ')
# axins12 = inset_axes(axins, width='2%', height='25%', loc=2)
# cbar12=plt.colorbar(c12,cax=axins12)
# cbar12.ax.set_title('$^{12}CO$ \n maxT $(K)$')
#
# axins13 = inset_axes(axins, width='2%', height='25%', loc=1)
# cbar13=plt.colorbar(c13,cax=axins13)
# cbar13.ax.set_title('$^{13}CO$ \n maxT $(K)$')
#==Map-Rectangles
dd=0.5
rect1 = [Rectangle((x-dx,y-dd), width=2.*dx, height=2.*dd, fill=False,color='red',linewidth=1.2,alpha=0.75)]
axins.add_artist(rect1[0])
rect2 = [Rectangle((x-dd,y-dy), width=2.*dd, height=2.*dy, fill=False,color='red',linewidth=1.2,alpha=0.75)]
axins.add_artist(rect2[0])
rect3= [Rectangle((x-1.5,y-1.5), width=3, height=3, fill=False,color='green',linewidth=1.5,alpha=0.95)]
axins.add_artist(rect3[0])
#=================3D Velocities==================================================
#Parameters
hl_lw=3 #Highlight LineWidth
lev_18 = [popt18[0]/2.] #C18O Contour Display
lev_13 = popt13[0]/2. #CO13 Contour Display
min13=0.75
min12=0.
num_levels_12=10
num_levels_13=8
#======X-line=========================
ax1y=plt.subplot(gs[5:7,2:])
ax1y.set_ylabel('Velocity $(km\,s^{-1})$')
ax1y.set_xlabel('X-Pixel')
region13y=map13[v1:v2,y,x-dx:x+dx] #CO13 zoom region
region12y=map12[v1:v2,y,x-dx:x+dx] #CO12 zoom region
region18y=map18[v1:v2,y,x-dx:x+dx] #CO18 zoom region
lev13y = np.linspace(np.abs(region13y.min())+min13,region13y.max(),num_levels_13)
lev12y = np.linspace(min12,region12y.max(),num_levels_12)
lev18y=lev_18
cy=ax1y.contour(np.arange(x-dx,x+dx,1),velocity[v1:v2],region13y,levels=lev13y,linewidths=2.1,cmap='gnuplot',alpha=0.8)
if fit13:
ax1y.contour(np.arange(x-dx,x+dx,1),velocity[v1:v2],region13y,[lev_13],linestyles='--',linewidths=hl_lw,colors='red',alpha=1.)
c2y=ax1y.contourf(np.arange(x-dx,x+dx,1),velocity[v1:v2],region12y,levels=lev12y)
if fit18:
ax1y.contour(np.arange(x-dx,x+dx,1),velocity[v1:v2],region18y,lev18y,linestyles='--',linewidths=hl_lw,colors='black',alpha=0.8)
ax1y.contour(np.arange(x-dx,x+dx,1),velocity[v1:v2],region18y,[s18.max()*0.8],linewidths=hl_lw+1,colors='black',alpha=0.8)
ax1y.plot(np.ones(velocity[v1:v2].shape)*x,velocity[v1:v2],'--')
#===CO12 Colorbar Hacks======================================
axinsy12 = inset_axes(ax1y, width='35%', height='3%', loc=2)
cbary12=plt.colorbar(c2y,cax=axinsy12,orientation='horizontal')
cbary12.ax.set_title('$^{12}CO$ $T_{B}$ $(K)$')
#===CO13 Colorbar Hacks======================================
axinsy13 = inset_axes(ax1y, width='30%', height='3%', loc=1)
cbary13=plt.colorbar(cy,cax=axinsy13,orientation='horizontal')
cbary13.ax.set_title('$^{13}CO$ $T_{B}$ $(K)$')
#==========Y-line===============================================================
ax1x=plt.subplot(gs[1:5,:2])
ax1x.set_xlabel('Velocity $(km\,s^{-1})$')
ax1x.set_ylabel('Y-Pixel')
region13x=map13[v1:v2,y-dy:y+dy,x]
region12x=map12[v1:v2,y-dy:y+dy,x]
region18x=map18[v1:v2,y-dy:y+dy,x]
lev13x = np.linspace(np.abs(region13x.min())+min13,region13x.max(),num_levels_13)
lev12x = np.linspace(min12,region12x.max(),num_levels_12)
lev18x=lev_18
cx=ax1x.contour(velocity[v1:v2],np.arange(y-dy,y+dy),np.rot90(region13x,3),levels=lev13x,linewidths=2.1,cmap='gnuplot',alpha=0.8)
if fit13:
ax1x.contour(velocity[v1:v2],np.arange(y-dy,y+dy),np.rot90(region13x,3),[lev_13],linestyles='--',linewidths=hl_lw,colors='red',alpha=1.)
c2x=ax1x.contourf(velocity[v1:v2],np.arange(y-dy,y+dy),np.rot90(region12x,3),levels=lev12x)
if fit18:
ax1x.contour(velocity[v1:v2],np.arange(y-dy,y+dy),np.rot90(region18x,3),lev18x,linestyles='--',linewidths=hl_lw,colors='black',alpha=0.8)
ax1x.contour(velocity[v1:v2],np.arange(y-dy,y+dy),np.rot90(region18y,3),[s18.max()*0.8],linewidths=hl_lw+1,colors='black',alpha=0.8)
ax1x.plot(velocity[v1:v2],np.ones(velocity[v1:v2].shape)*y,'--')
#===CO12 Colorbar Hacks======================================
axinsx12 = inset_axes(ax1x, width='3%', height='30%', loc=2)
cbarx12=plt.colorbar(c2x,cax=axinsx12)
cbarx12.ax.set_title('$^{12}CO$ \n $T_{B}$ $(K)$')
#===CO13 Colorbar Hacks======================================
axinsx13 = inset_axes(ax1x, width='3%', height='30%', loc=1)
cbarx13=plt.colorbar(cx,cax=axinsx13)
cbarx13.ax.set_title('$^{13}CO$ \n $T_{B}$ $(K)$')
axinsx13.yaxis.set_ticks_position("left")
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#============================================================================
x_p=xx.min()
#x_mean= xx.mean() #For Text Annotation
ax2=plt.subplot(gs[7,:])
y_p=s12.max()
ax2.set_title('$^{12}CO$ // $FWHM=$%0.3f'%(FWHM12))
ax2.plot(xx,s12,label='CO12')
ax2.plot(xx[m],s12[m],'ko',label='Masked CO12 Data')
ax2.plot(xx,gaussian(xx,popt12[0],popt12[1],popt12[2]),label='Fit to Masked Data')
if fit12:
ax2.fill_between(xx,gaussian(xx,popt12[0]-sd12[0],popt12[1]-sd12[1],popt12[2]-sd12[2]),gaussian(xx,popt12[0]+sd12[0],popt12[1]+sd12[1],popt12[2]+sd12[2]),alpha=0.25)
ax2.annotate(r'Fit Parameters for CO12: $A=$%0.3f +/-%0.3f, $x_0=$%0.3f +/-%0.3f, $\sigma=$%0.3f +/-%0.3f'%(popt12[0],sd12[0],popt12[1],sd12[1],popt12[2],sd12[2]),(x_p,y_p))
hline=np.linspace(popt12[1]-FWHM12/2,popt12[1]+FWHM12/2,10)
ax2.plot(hline,np.ones(hline.shape)*popt12[0]/2,color='r',label='HalfMaximum of fit CO12')
ax2.plot(xx,s13,alpha=0.7,label='CO13')
ax2.plot(xx,s18,alpha=0.5,label='C18O')
if fit18:
ax2.axvspan(popt18[1]-FWHM18/2.,popt18[1]+FWHM18/2.,alpha=0.15)
if fit13:
ax2.axvspan(popt13[1]-FWHM13/2.,popt13[1]+FWHM13/2.,alpha=0.15,color='green')
ax2.legend()
ax3=plt.subplot(gs[8,:],sharex=ax2)
y_p=s13.max()
ax3.set_title('$^{13}CO$ // $FWHM=$%0.3f'%(FWHM13))
ax3.plot(xx,s13,'ko',label='CO13')
ax3.plot(xx,gaussian(xx,popt13[0],popt13[1],popt13[2]),label='Fit to CO13')
if fit13:
ax3.fill_between(xx,gaussian(xx,popt13[0]-sd13[0],popt13[1]-sd13[1],popt13[2]-sd13[2]),gaussian(xx,popt13[0]+sd13[0],popt13[1]+sd13[1],popt13[2]+sd13[2]),alpha=0.25)
ax3.annotate(r'Fit Parameters for CO13: $A=$%0.3f +/-%0.3f, $x_0=$%0.3f +/-%0.3f, $\sigma=$%0.3f +/-%0.3f'%(popt13[0],sd13[0],popt13[1],sd13[1],popt13[2],sd13[2]),(x_p,y_p))
hline=np.linspace(popt13[1]-FWHM13/2,popt13[1]+FWHM13/2,10)
ax3.plot(hline,np.ones(hline.shape)*popt13[0]/2,color='r',label='HalfMaximum of fit CO13')
ax3.plot(xx,s18,alpha=0.5,label='C18O')
if fit18:
ax3.axvspan(popt18[1]-FWHM18/2,popt18[1]+FWHM18/2,alpha=0.15)
if fit13:
ax3.axvspan(popt13[1]-FWHM13/2.,popt13[1]+FWHM13/2.,alpha=0.15,color='green')
ax3.legend()
ax4=plt.subplot(gs[9,:],sharex=ax2)
y_p=s18.max()
ax4.set_title(r'$C^{18}O$ // $FWHM=$%0.3f'%(FWHM18))
ax4.plot(xx,s18,'ko',label='C18O')
if fit18:
ax4.plot(xx,gaussian(xx,popt18[0],popt18[1],popt18[2]),label='Fit to CO18')
ax4.fill_between(xx,gaussian(xx,popt18[0]-sd18[0],popt18[1]-sd18[1],popt18[2]-sd18[2]),gaussian(xx,popt18[0]+sd18[0],popt18[1]+sd18[1],popt18[2]+sd18[2]),alpha=0.25)
ax4.annotate(r'Fit Parameters for CO18: $A=$%0.3f +/-%0.3f, $x_0=$%0.3f +/-%0.3f, $\sigma=$%0.3f +/-%0.3f'%(popt18[0],sd18[0],popt18[1],sd18[1],popt18[2],sd18[2]),(x_p,y_p))
hline=np.linspace(popt18[1]-FWHM18/2,popt18[1]+FWHM18/2,10)
ax4.plot(hline,np.ones(hline.shape)*popt18[0]/2,color='r',label='HalfMaximum of fit CO18')
ax4.axvspan(popt18[1]-FWHM18/2,popt18[1]+FWHM18/2,alpha=0.15)
ax4.legend()
ax5=plt.subplot(gs[10,:],sharex=ax2)
ax5.set_title(r'$^{12}CO$ Wings')
ax5.fill_between(xx[xx1],s12[xx1],alpha=0.7,color='blue')
ax5.fill_between(xx[xx2],s12[xx2],alpha=0.7,color='red')
if outflow:
ax5.annotate(r'Outflow Mass Estimation: %0.2f $M_{\odot}$'%MG,(x_p,s12.max()-2),fontsize=17)
ax5.annotate(r'Outflow Momentum Estimation: %0.2f $M_{\odot}\, km \, s^{-1}$'%(Pb+Pr),(x_p,s12.max()-0.4*s12.max()),fontsize=17)
ax5.annotate(r'Blue Outflow: %0.2f $M_{\odot}$'%Mb,(popt13[1]-10,y_p),fontsize=13)
ax5.annotate(r'$V_B:$ %0.2f $km\,s^{-1}$'%Vb,(Vb,y_p+5),fontsize=12)
ax5.annotate(r'$V_R:$ %0.2f $km\,s^{-1}$'%Vr,(Vr,y_p+5),fontsize=12)
ax5.annotate(r'Red Outflow: %0.2f $M_{\odot}$'%Mr,(popt13[1]+5,y_p),fontsize=13)
ax5.vlines(Vb,0,s12.max())
ax5.vlines(Vr,0,s12.max())
#ax6=plt.subplot(gs[11:,:],sharex=ax2)
#ax6.set_title('3X3 Mean and Standard Deviation Spectrum')
#ave12=Spectra9(map12,y,x)
#ave13=Spectra9(map13,y,x)
#ave18=Spectra9(map18,y,x)
#ax6.plot(xx,ave12[0],color='blue',label='CO12 Mean')
#ax6.fill_between(xx,ave12[0]-ave12[1],ave12[0]+ave12[1],color='blue',alpha=0.25)
#ax6.plot(xx,ave13[0],color='green',label='CO12 Mean')
#ax6.fill_between(xx,ave13[0]-ave13[1],ave13[0]+ave13[1],color='green',alpha=0.25)
#ax6.plot(xx,ave18[0],color='red',label='CO12 Mean')
#ax6.fill_between(xx,ave18[0]-ave18[1],ave18[0]+ave18[1],color='red',alpha=0.25)
# ax6.plot(xx,gaussian(xx,popt913[0],popt913[1],popt913[2]),'--',linewidth=3.,label='Fit')
#ax6.legend()
plt.tight_layout()
if s:
plt.savefig('full%d-%d'%(y,x),bbox_inches='tight')
z = np.load(f)
# z is a numpy array of 15x15
return z, (-3, 4, -4, 3)
fig, ax = plt.subplots(figsize=[5, 4])
# prepare the demo image
Z, extent = get_demo_image()
Z2 = np.zeros([150, 150], dtype="d")
ny, nx = Z.shape
Z2[30:30 + ny, 30:30 + nx] = Z
# extent = [-3, 4, -4, 3]
ax.imshow(Z2, extent=extent, interpolation="nearest",
origin="lower")
axins = zoomed_inset_axes(ax, 6, loc=1) # zoom = 6
axins.imshow(Z2, extent=extent, interpolation="nearest",
origin="lower")
# sub region of the original image
x1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
plt.xticks(visible=False)
plt.yticks(visible=False)
# draw a bbox of the region of the inset axes in the parent axes and
# connecting lines between the bbox and the inset axes area
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
plt.draw()
plt.show()
# and for Gibraltar
patch = create_rect_patch(coordinates3, m, 0.2)
ax.add_patch(patch)
# CTD and gliders
# m.plot(lonCTD,latCTD,'ko',ms=1, zorder=6)
# Coastline and continent
m.plot(loncoast2, latcoast2, "k-", lw=0.1, zorder=4)
m.fillcontinents(ax=ax, color="0.9", zorder=2)
plt.title(r"25$-$31 May, 2014", fontsize=20)
# Plot inset with CTD and gliders
if doplotinset:
axins = zoomed_inset_axes(ax, 4.5, loc=2)
n1, n2 = m(coordinates2[0], coordinates2[2])
n3, n4 = m(coordinates2[1], coordinates2[3])
axins.set_xlim(n1, n3)
axins.set_ylim(n2, n4)
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
# Add gliders tracks and CTD casts
NN = 34
axins.plot(
lonCTD[:NN],
latCTD[:NN],
"ks-",
lw=0.5,
axes_class=(pywcsgrid2.Axes, dict(header=h)))
main_axes = grid[0]
main_axes.locator_params(nbins=5)
cb_axes = grid.cbar_axes[0] # colorbar axes
im = main_axes.imshow(d, origin="lower", cmap=plt.cm.gray_r,
vmin=4.e-05, vmax=0.00018,
interpolation="nearest")
cb_axes.colorbar(im)
cb_axes.axis["right"].toggle(ticklabels=False)
axins = zoomed_inset_axes(main_axes, zoom=3, loc=1,
axes_class=pywcsgrid2.Axes,
axes_kwargs=dict(wcs=h))
im2 = axins.imshow(d, origin="lower", interpolation="nearest",
vmin=9.e-05, vmax=18.e-05,
cmap=plt.cm.gray_r)
axins.set_xlim(120, 160)
axins.set_ylim(120, 160)
axins.set_ticklabel_type("delta")
axins.axis[:].invert_ticklabel_direction()
mark_inset(main_axes, axins, loc1=2, loc2=4, fc="none", ec="0.5")
axes[0].axis(False)
axes = axes[1]
# Plot over whole domain
levels = 51
cbar = figure.colorbar(tricontourf(eta0,
levels=levels,
axes=axes,
cmap='coolwarm'),
ax=axes)
cbar.set_label(r"Dislocation $[\mathrm m]$")
op.annotate_plot(axes)
axes.axis(False)
# Add zoom
axins = zoomed_inset_axes(axes, 2.5, bbox_to_anchor=[750,
525]) # zoom-factor: 2.5
tricontourf(eta0, levels=51, axes=axins, cmap='coolwarm')
axins.axis(False)
axins.set_xlim(xlim)
axins.set_ylim(ylim)
mark_inset(axes, axins, loc1=1, loc2=1, fc="none", ec="0.5")
# Save
fname = "dislocation_{:d}_{:s}".format(level, categories)
if not loaded:
fname += '_ig'
savefig(fname, "plots", extensions=["jpg"], tight=False)
# --- Setup tsunami propagation problem
b = Function(P1).assign(op.set_bathymetry(P1))
