def plot_Kiel_diagram(starl):
"""
Plot Kiel diagram.
"""
x = starl['temperature']
y = starl['g']
age = starl['age'] / 1e6
points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
cmap = pl.cm.spectral
norm = pl.Normalize(age.min(), age.max())
lc = LineCollection(segments, cmap=cmap, norm=norm)
lc.set_array(age)
lc.set_linewidth(3)
pl.gca().add_collection(lc)
pl.xlim(x.max(), x.min())
pl.ylim(y.max(), y.min())
pl.xlabel('Effective temperature [K]')
pl.ylabel('log(surface gravity [cm s$^{-2}$]) [dex]')
ax0 = pl.gca()
ax1 = pl.mpl.colorbar.make_axes(ax0)[0]
norm = pl.mpl.colors.Normalize(age.min(), age.max())
cb1 = pl.mpl.colorbar.ColorbarBase(ax1,
cmap=cmap,
norm=norm,
orientation='vertical')
cb1.set_label('Age [Myr]')
pl.axes(ax0)
def d3():
rtimes, rt, rp = np.loadtxt("data/data.txt").T
mask = np.logical_and(rtimes > 1391000000, rtimes < 1393000000)
rtimes = rtimes[mask]
rt = rt[mask]
rtimes = map(datetime.datetime.fromtimestamp, rtimes)
rtimes = matplotlib.dates.date2num(rtimes)
fig, axis = plt.subplots()
axis.xaxis_date()
fig.autofmt_xdate()
axis.plot_date(rtimes, rt, "-", linewidth=3, color="black")
forecast_list = []
for fname in glob.glob("data/forecast.1391*.txt"):
stamp = fname.split(".")[1]
times, tempa = np.loadtxt(fname).T
times = map(datetime.datetime.fromtimestamp, times)
times = matplotlib.dates.date2num(times)
points = np.array([times, tempa]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
lc = LineCollection(segments,
cmap=plt.get_cmap("jet"),
norm=plt.Normalize(0, 1))
lc.set_array(np.linspace(0, 1, len(times)))
lc.set_linewidth(1)
axis.add_collection(lc)
axis.plot_date(times, tempa, "-", linewidth=2)
return fig_to_html(fig)
def draw_disp_grid(boxsize, grid):
""" draw a slice of the displacement grid """
pl.rcParams.update(latexParams)
pl.figure(figsize=(6.64, 6.64), dpi=100)
limit = max(-grid.min(), grid.max())
norm = pl.Normalize(vmin=-limit, vmax=limit)
slice = grid[:, 0]
pl.subplot(221)
n = grid.shape[1]
x = arange(n) * (boxsize / n)
pl.imshow(slice[0], extent=(0, boxsize, 0, boxsize), norm=norm)
pl.colorbar().set_label(r'$\rm Mpc/h$')
pl.ylabel(r'$y \; \rm Mpc/h$')
pl.title(r'$\Psi_x$')
pl.subplot(222)
pl.imshow(slice[1], extent=(0, boxsize, 0, boxsize), norm=norm)
pl.title(r'$\Psi_y$')
pl.colorbar().set_label(r'$\rm Mpc/h$')
pl.subplot(223)
pl.imshow(slice[2], extent=(0, boxsize, 0, boxsize), norm=norm)
pl.colorbar().set_label(r'$\rm Mpc/h$')
#pl.ylabel(r'$y \; \rm Mpc/h$')
pl.title(r'$\Psi_z$')
pl.show()
def my_square_scatter(axes,
x_array,
y_array,
z_array,
min_z=None,
max_z=None,
size=0.5,
**kwargs):
size = float(size)
if min_z is None:
min_z = z_array.min()
if max_z is None:
max_z = z_array.max()
normal = pylab.Normalize(min_z, max_z)
colors = pylab.cm.jet(normal(z_array))
for x, y, c in zip(x_array, y_array, colors):
square = pylab.Rectangle((x - size / 2, y - size / 2),
size,
size,
color=c,
**kwargs)
axes.add_patch(square)
axes.autoscale()
cax, _ = cbar.make_axes(axes)
cb2 = cbar.ColorbarBase(cax, cmap=pylab.cm.jet, norm=normal)
return True
def cline(x, y, z, clim, cmap): # color line
norm = pl.Normalize(clim[0], clim[1])
points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
lc = LineCollection(segments, cmap=cmap, norm=norm)
if not hasattr(z, "__iter__"): # single value check
z = np.array([z])
lc.set_array(z)
lc.set_linewidth(1)
pl.gca().add_collection(lc)
def plot_E_s(arrays, x, y, x0, y0, corner_indices, save_name=None):
figsize = 6
fig, ax = plt.subplots(1, 1, figsize=(figsize, figsize))
arrays = [np.abs(E_s) / np.abs(E_s).mean() for E_s in arrays]
vmin = min([E_s.min().value for E_s in arrays])
vmax = max([E_s.max().value for E_s in arrays])
norm = plt.Normalize(vmin, vmax)
to_colour = lambda w: plt.cm.jet(norm(w))
def _get_artists(artists, start):
_, img = plot_2d_image(arrays[start],
x,
y,
title="Scattered electric field",
corner_indices=corner_indices,
colorizer=to_colour,
ax=ax)
sc = ax.scatter(x0[start].value,
y0[start].value,
c='green',
label='source')
ax.set_xlim(x.min().value, x.max().value)
ax.set_ylim(y.min().value, y.max().value)
ax.legend()
artists.append(img)
artists.append(sc)
return artists
def init():
start = 0
ax.clear()
artists = []
artists = _get_artists(artists, start)
mappable = plt.cm.ScalarMappable(norm=norm, cmap=plt.cm.jet)
add_colorbar(mappable,
label='rel. electric field amplitude [{}]'.format(
arrays[0].unit),
ax=ax)
return artists
def update(start):
ax.clear()
artists = []
artists = _get_artists(artists, start)
return artists
ani = FuncAnimation(fig,
update,
frames=range(1, len(arrays)),
init_func=init,
blit=True)
ani.save(save_name, fps=5.) #len(arrays) / 6.)
def XVsY2DMap(self,
ax,
xVal,
yVal,
zVal,
cmap=None,
title=None,
xlabel=None,
xlim=None,
ylabel=None,
ylim=None,
zlabel=None,
zMax=None,
zMin=None):
m = Basemap(llcrnrlon=self.glonlim[0],
llcrnrlat=self.glatlim[0],
urcrnrlon=self.glonlim[-1],
urcrnrlat=self.glatlim[-1],
resolution='l')
m.drawcoastlines()
# Lines at constant "latitude"
parallelsLim = self._RoundLim([yVal[0], yVal[-1]])
m.drawparallels(arange(parallelsLim[0], parallelsLim[1], 20.),
labels=[True, False, False, True])
# Lines at constant "longitude"
meridiansLim = self._RoundLim([xVal[0], xVal[-1]])
m.drawmeridians(arange(meridiansLim[0], meridiansLim[1], 30.),
labels=[True, False, False, True])
X, Y = meshgrid(xVal, yVal)
ipc = m.pcolor(X,
Y,
transpose(zVal),
cmap=cmap,
edgecolors='None',
norm=pylab.Normalize(),
vmax=zMax,
vmin=zMin)
# m.contour(X, Y, transpose(self.data2D['dip']), colors='k', linestyles='--')
ax.set_xlim(xlim)
ax.set_ylim(ylim)
ax.set_title(title)
# ax.set_xlabel( xlabel )
# ax.set_ylabel( ylabel )
cbpn = m.colorbar(ipc)
cbpn.set_label(zlabel)
def __animate_nestmates(self, pos, crop):
normal = pl.Normalize(0, 1)
colors = cmap(normal(crop))
for p, c in zip(pos, colors):
if not isinstance(pos, str):
rect = pl.Rectangle([p, 0], 1, 3, color=c)
self.ax.add_patch(rect)
else:
pos = [ast.literal_eval(x) for x in pos]
rect = pl.Rectangle(pos, 1, 1, color=c)
self.ax.add_patch(rect)
def plot_data(ax):
#-- rectangle lower left corner positions
h = 0.7 # height of the rectangles
Y = np.arange(1-h/2,7+h/2,1) # y-coord
X = [1, 2, 1, 2, 4, 0.6, 200] # x-coord
W = [200, 14, 3, 2, 564, 1.4, 1e4] # width (+1) of the rectangles
vs = [0, -.5, 0.7, 0.8, 0.2, -0.3, 0.4] # [experimental --> theoretical] in [-1,1]
normal = pl.Normalize(-1,1)
colors = pl.cm.jet(normal(vs))
X_txt = [5, 18, 4, 4, 7, 1.3, 800]
txt = ['ICLS/GFM', 'droplet assembly',
'hydrodynamic interaction I',
'hydrodynamic interaction II',
'droplets in turbulence',
'liquid-infused surfaces','DLCD']
for i,y in enumerate(Y):
rect = patches.Rectangle( (X[i],y), W[i]-1,h, color=colors[i])
ax.add_patch(rect)
ax.text(X_txt[i],y+0.2, txt[i])
cax, _ = cbar.make_axes(ax)
cb2 = cbar.ColorbarBase(cax, cmap=pl.cm.jet,norm=normal)
ax.plot([1,1],[0,8], linestyle='--',color='C7', alpha=0.5)
#-- plot limit
xmin = 0.5
xmax = 1.5e4
ymin = 0.4
ymax = 7.6
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
ax.set_xscale('log')
ax.set_xlabel(r'$N$')
ax.set_ylabel(r'Paper')
return ax
def add_colorbar_to_axes(ax, cmap, norm=None, vmin=None, vmax=None):
"""
Add colorbar to axes easily.
Args:
ax: Axes
cmap: str or cmap
norm: Normalize or None
vmin: lower limit of color if norm is None
vmax: upper limit of color if norm is None
"""
divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size='5%', pad=0.05)
if norm is None:
norm = plt.Normalize(vmin=vmin, vmax=vmax)
sm = plt.cm.ScalarMappable(norm, cmap=plt.cm.get_cmap(cmap))
ax.figure.colorbar(sm, cax=cax, orientation='vertical')
def __init__(self, save_name=False, ant=False):
self._df_file = self.get_data()
self._df_file = '~/Agent-based-ants/Good_model/Data/singlerun_step_data.csv' if self._df_file is None else self._df_file
self.save_name = save_name
self.ant = ant
self.validate()
if re.search("[$.](.+)", self._df_file).group(1) == 'csv':
self.df = pd.read_csv(self._df_file)
elif re.search("[$.](.+)", self._df_file).group(1) in [
'xls', 'xlsx', 'xlsm', 'xlsb'
]:
self.df = pd.excel_read(self._df_file)
self.fig, self.ax = plt.subplots(figsize=(max(self.df.position) + 3,
5))
normal = pl.Normalize(0, 1)
self.cax, _ = cbar.make_axes(self.ax)
self.cb2 = cbar.ColorbarBase(self.cax, cmap=cmap, norm=normal)
# self.repeat_df = self.df[self.df.id <=23]
self.repeat_df = self.df
def plot_2d_image(a, x, y, colorbar_name=None, title=None, xlabel='x', ylabel='y', cmap='bone', colorizer=None, corner_indices=None, ax=None, save_name=None):
if ax is None:
fig, ax = plt.subplots(1,1, figsize=(5,5))
if colorizer is None:
norm = plt.Normalize(a.min().value, a.max().value)
colorizer = plt.cm.get_cmap(cmap)(norm)
img = ax.imshow(colorizer(a.T.value), interpolation='nearest', origin='lower',
extent=(x.min().value, x.max().value, y.min().value, y.max().value))
if corner_indices is not None:
ax.plot(Quantity([x[corner_indices[0]], x[-corner_indices[0]], x[-corner_indices[0]], x[corner_indices[0]], x[corner_indices[0]]]).value,
Quantity([y[corner_indices[1]], y[corner_indices[1]], y[-corner_indices[1]], y[-corner_indices[1]], y[corner_indices[1]]]).value, c='red')
if title is not None:
ax.set_title(title)
if xlabel is not None:
ax.set_xlabel('{} [{}]'.format(xlabel, x.unit))
if ylabel is not None:
ax.set_ylabel('{} [{}]'.format(ylabel, y.unit))
if colorbar_name is not None:
plt.colorbar(label='{} [{}]'.format(colorbar_name, a.unit))
if save_name is not None:
plt.savefig(save_name)
return ax, img
def animation(self):
if self.save_name:
ani = animation.FuncAnimation(
# self.fig, self.__animate_repeat, interval=1, frames=max(self.repeat_df.step))
self.fig,
self.__animate_repeat,
interval=1,
frames=(50))
ani.save(self.save_name, writer=writer)
else:
for i in range(10):
self.fig, self.ax = plt.subplots(
figsize=(max(self.df.position), 5))
normal = pl.Normalize(0, 1)
self.cax, _ = cbar.make_axes(self.ax)
self.cb2 = cbar.ColorbarBase(self.cax, cmap=cmap, norm=normal)
self.__animate_repeat(i)
plt.show(block=False)
plt.pause(0.001)
plt.close()
def plot_regions_states(self):
fig, ax = plt.subplots()
states_per_reg = [len(region.states) for region in self.regions]
states_per_reg_lims = (min(states_per_reg), max(states_per_reg))
normal = pylab.Normalize(*states_per_reg_lims)
colors = pylab.cm.BuGn(normal(states_per_reg))
for region, color in zip(self.regions, colors):
lengths = region.max_border - region.min_border
ax.add_patch(
patches.Rectangle(region.min_border,
*lengths,
fill=True,
edgecolor='k',
facecolor=color))
cax, _ = cbar.make_axes(ax)
print("the interest lims are: ", states_per_reg_lims)
cb2 = cbar.ColorbarBase(cax, cmap=pylab.cm.BuGn, norm=normal)
ax.set_xlim(self.state_bounds[0][0], self.state_bounds[1][0])
ax.set_ylim(self.state_bounds[0][1], self.state_bounds[1][1])
def plot_regions_interest(self, ax=None):
if ax is None:
__, ax = plt.subplots()
interests = self.compute_all_interests()
interest_lims = (min(interests), max(interests))
normal = pylab.Normalize(*interest_lims)
colors = pylab.cm.YlOrRd(normal(interests))
for region, color in zip(self.regions, colors):
lengths = region.max_border - region.min_border
ax.add_patch(
patches.Rectangle(region.min_border,
*lengths,
fill=True,
edgecolor='k',
facecolor=color))
cax, _ = cbar.make_axes(ax)
print("the interest lims are: ", interest_lims)
cb2 = cbar.ColorbarBase(cax, cmap=pylab.cm.YlOrRd, norm=normal)
ax.set_xlim(self.state_bounds[0][0], self.state_bounds[1][0])
ax.set_ylim(self.state_bounds[0][1], self.state_bounds[1][1])
def plot_discretized_belief_halfcircle(belief_rewards, center_points, env,
observations):
fig = plt.figure()
env.plot_behavior(observations,
plot_env=True,
color=cols_deep[3],
linewidth=5)
res = center_points[1, 0] - center_points[0, 0]
normal = pl.Normalize(0., 1.)
colors = pl.cm.gray(normal(belief_rewards))
for (x, y), c in zip(center_points, colors):
rec = Rectangle((x, y),
res,
res,
facecolor=c,
alpha=0.85,
edgecolor='none')
plt.gca().add_patch(rec)
cax, _ = cbar.make_axes(plt.gca())
cb2 = cbar.ColorbarBase(cax, cmap=pl.cm.gray, norm=normal)
return fig
def hello():
rtimes, rt, rp = np.loadtxt("data/data.txt").T
rtimes = map(datetime.datetime.fromtimestamp, rtimes)
rtimes = matplotlib.dates.date2num(rtimes)
fig = Figure()
axis = fig.add_subplot(1, 1, 1)
axis.xaxis_date()
fig.autofmt_xdate()
forecast_list = []
for fname in glob.glob("data/forecast.*.txt"):
stamp = fname.split(".")[1]
times, tempa = np.loadtxt(fname).T
times = map(datetime.datetime.fromtimestamp, times)
times = matplotlib.dates.date2num(times)
points = np.array([times, tempa]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
lc = LineCollection(segments,
cmap=plt.get_cmap("jet"),
norm=plt.Normalize(0, 1))
lc.set_array(np.linspace(0, 1, len(times)))
lc.set_linewidth(1)
axis.add_collection(lc)
axis.plot_date(times, tempa, "-", linewidth=0)
axis.plot_date(rtimes, rt, "-", linewidth=1, color="black")
canvas = FigureCanvas(fig)
output = StringIO.StringIO()
canvas.print_png(output)
response = make_response(output.getvalue())
response.mimetype = 'image/png'
return response
def XVsY2DPlot(self,
ax,
xVal,
yVal,
zVal,
cmap=None,
title=None,
xlabel=None,
xlim=None,
ylabel=None,
ylim=None,
zlabel=None,
zMax=None,
zMin=None):
X, Y = pylab.meshgrid(xVal, yVal)
X = transpose(X)
Y = transpose(Y)
C = transpose(zVal)
ipn = ax.pcolor(X,
Y,
C,
cmap=cmap,
edgecolors='None',
norm=pylab.Normalize(),
vmax=zMax,
vmin=zMin)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
ax.set_title(title)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
cbpn = pylab.colorbar(ipn)
cbpn.set_label(zlabel)
error[nanLocs]=0
fluxes = []
fluxErrors = []
for aperture in apertures:
print(aperture_photometry(data,aperture,error=error))
fluxErrors.append(list(aperture_photometry(data,aperture,error=error)['aperture_sum_err'])[0])
fluxes.append(list(aperture_photometry(data,aperture,error=error)['aperture_sum'])[0])
return np.array(fluxes), np.array(fluxErrors),pixelCenter
fluxes,fluxErrors,pixelCenter = calculateFluxes(data,WCS(hdu.header,naxis=2))
######################################################################################
norm = plt.Normalize()
colors = plt.cm.jet(norm(fluxes))
rainbowColorMapValue = plt.get_cmap('rainbow')
colors = rainbowColorMapValue(norm(fluxes))
infile = 'data/HZ7_Collapsed.fits'
hdu = fits.open(infile)
data = hdu[0].data[0][0]
wcs = WCS(hdu[0].header,naxis=2)
# Plotting low-res moment-1 plot
fig = plt.figure()
ax = fig.add_subplot(projection=wcs)
im = ax.imshow(data,cmap='rainbow')
embedding[1],
s=100 * d**2,
c=labels,
cmap=pl.cm.spectral)
# Plot the edges
start_idx, end_idx = np.where(non_zero)
#a sequence of (*line0*, *line1*, *line2*), where::
# linen = (x0, y0), (x1, y1), ... (xm, ym)
segments = [[embedding[:, start], embedding[:, stop]]
for start, stop in zip(start_idx, end_idx)]
values = np.abs(partial_correlations[non_zero])
lc = LineCollection(segments,
zorder=0,
cmap=pl.cm.hot_r,
norm=pl.Normalize(0, .7 * values.max()))
lc.set_array(values)
lc.set_linewidths(15 * values)
ax.add_collection(lc)
# Add a label to each node. The challenge here is that we want to
# position the labels to avoid overlap with other labels
for index, (name, label, (x, y)) in enumerate(zip(names, labels, embedding.T)):
dx = x - embedding[0]
dx[index] = 1
dy = y - embedding[1]
dy[index] = 1
this_dx = dx[np.argmin(np.abs(dy))]
this_dy = dy[np.argmin(np.abs(dx))]
if this_dx > 0:
def __init__(self,
data_file,
plot_groups=[1],
data_type=2,
color_map='jet',
plot_contours=False,
plot_label="",
plot_bands=None,
time_zone=0,
plot_max_freq=30.0,
run_quietly=False,
save_file='',
dpi=150,
parent=None):
self.data_type = data_type
self.run_quietly = run_quietly
self.dpi = dpi
self.df = VOAOutFile(data_file,
time_zone=time_zone,
data_type=self.data_type,
quiet=run_quietly)
self.image_defs = self.IMG_TYPE_DICT[self.data_type]
color_map = eval('P.cm.' + color_map)
if plot_groups[0] == 'a':
num_grp = self.df.get_number_of_groups()
plot_groups = range(0, num_grp)
self.subplots = []
number_of_subplots = len(plot_groups)
matplotlib.rcParams['axes.edgecolor'] = 'gray'
matplotlib.rcParams['axes.facecolor'] = 'white'
matplotlib.rcParams['axes.grid'] = True
matplotlib.rcParams['figure.facecolor'] = 'white'
matplotlib.rcParams['legend.fancybox'] = True
matplotlib.rcParams['legend.shadow'] = True
matplotlib.rcParams['figure.subplot.hspace'] = 0.45
matplotlib.rcParams['figure.subplot.wspace'] = 0.35
matplotlib.rcParams['figure.subplot.right'] = 0.85
colorbar_fontsize = 12
if number_of_subplots <= 1:
self.num_rows = 1
self.main_title_fontsize = 24
matplotlib.rcParams['legend.fontsize'] = 12
matplotlib.rcParams['axes.labelsize'] = 12
matplotlib.rcParams['axes.titlesize'] = 8
matplotlib.rcParams['xtick.labelsize'] = 10
matplotlib.rcParams['ytick.labelsize'] = 10
matplotlib.rcParams[
'figure.subplot.top'] = 0.79 # single figure plots have a larger title so require more space at the top.
self.x_axes_ticks = P.arange(0, 25, 2)
elif ((number_of_subplots >= 2) and (number_of_subplots <= 6)):
self.num_rows = 2
self.main_title_fontsize = 18
matplotlib.rcParams['legend.fontsize'] = 10
matplotlib.rcParams['axes.labelsize'] = 10
matplotlib.rcParams['axes.titlesize'] = 11
matplotlib.rcParams['xtick.labelsize'] = 8
matplotlib.rcParams['ytick.labelsize'] = 8
self.x_axes_ticks = P.arange(0, 25, 4)
else:
self.num_rows = 3
self.main_title_fontsize = 16
matplotlib.rcParams['legend.fontsize'] = 8
matplotlib.rcParams['axes.labelsize'] = 8
matplotlib.rcParams['axes.titlesize'] = 10
matplotlib.rcParams['xtick.labelsize'] = 6
matplotlib.rcParams['ytick.labelsize'] = 6
self.x_axes_ticks = P.arange(0, 25, 4)
self.num_cols = int(
math.ceil(float(number_of_subplots) / float(self.num_rows)))
self.fig = Figure(figsize=(7, 6.5))
self.main_title_label = self.fig.suptitle(
plot_label + unicode(self.image_defs['title'], 'utf-8'),
fontsize=self.main_title_fontsize)
for chan_grp in plot_groups:
(group_name, group_info, fot, muf, hpf,
image_buffer) = self.df.get_group_data(chan_grp)
ax = self.fig.add_subplot(self.num_rows, self.num_cols,
plot_groups.index(chan_grp) + 1)
self.subplots.append(ax)
if number_of_subplots > 4:
#save a little space by only labelling the outer edges of the plot
ax.label_outer()
_sign = '+' if (time_zone >= 0) else ''
self.x_label = ax.set_xlabel(
_('Time (UTC%(sig)s%(tz)s)') % {
'sig': _sign,
'tz': time_zone
})
self.y_label = ax.set_ylabel(_('Frequency (MHz)'))
## Autoscale y (frequency axis)
if (plot_max_freq == self.AUTOSCALE):
y_max = math.ceil(max(muf) / 5.0) * 5.0
y_max = min(plot_max_freq, 30.0)
y_max = max(plot_max_freq, 5.0)
else:
y_max = math.ceil(plot_max_freq / 5.0) * 5.0
#resize the image
image_buffer = image_buffer[0:y_max - 1, :]
y_ticks = [2, 5]
for y_tick_value in P.arange(10, y_max + 1, 5):
y_ticks.append(y_tick_value)
ax.plot(range(0, 25), muf, 'r-', range(0, 25), fot, 'g-')
ax.set_ylim([2, y_max])
ax.set_xticks(self.x_axes_ticks)
ax.set_yticks(y_ticks)
self.add_legend(ax)
title_str = group_info.strip()
if number_of_subplots > 1:
title_str = self.get_small_title(title_str)
self.subplot_title_label = ax.set_title(title_str,
multialignment='left',
**self.mono_font)
if (self.data_type > 0):
im = ax.imshow(image_buffer,
interpolation='bicubic',
extent=(0, 24, 2, y_max),
origin='lower',
cmap=color_map,
alpha=0.95,
norm=P.Normalize(clip=False,
vmin=self.image_defs['min'],
vmax=self.image_defs['max']))
if plot_contours:
ax.contour(image_buffer,
self.image_defs['y_labels'],
extent=(0, 24, 2, y_max),
linewidths=1.0,
colors='k',
alpha=0.6)
if plot_bands:
for a, b in plot_bands:
ax.axhspan(a, b, alpha=0.5, ec='k', fc='k')
if (self.data_type > 0):
self.cb_ax = self.fig.add_axes(self.get_cb_axes())
self.fig.colorbar(im,
cax=self.cb_ax,
orientation='vertical',
format=P.FuncFormatter(
eval('self.' +
self.image_defs['formatter'])))
for t in self.cb_ax.get_yticklabels():
t.set_fontsize(colorbar_fontsize)
canvas = FigureCanvasGTKAgg(self.fig)
self.fig.canvas.mpl_connect('draw_event', self.on_draw)
canvas.show()
if save_file:
self.save_plot(canvas, save_file)
if not self.run_quietly:
# TODO consider using a scrolled pane here...
dia = VOAPlotWindow('pythonProp - ' + self.image_defs['title'],
canvas,
parent,
dpi=self.dpi)
return
def plot_regions_states(self, maze_id=0, report=None):
fig, ax = plt.subplots()
states_per_reg = [len(region.states) for region in self.regions]
states_per_reg_lims = (min(states_per_reg), max(states_per_reg))
normal = pylab.Normalize(*states_per_reg_lims)
colors = pylab.cm.BuGn(normal(states_per_reg))
for region, color in zip(self.regions, colors):
lengths = region.max_border - region.min_border
ax.add_patch(
patches.Rectangle(region.min_border,
*lengths,
fill=True,
edgecolor='k',
facecolor=color))
cax, _ = cbar.make_axes(ax)
print("the interest lims are: ", states_per_reg_lims)
cb2 = cbar.ColorbarBase(cax, cmap=pylab.cm.BuGn, norm=normal)
ax.set_xlim(self.state_bounds[0][0], self.state_bounds[1][0])
ax.set_ylim(self.state_bounds[0][1], self.state_bounds[1][1])
if maze_id == 0:
ax.add_patch(
patches.Rectangle((-3, -3),
10,
2,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((-3, -1),
2,
6,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((-3, 5),
10,
2,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((5, -1),
2,
6,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((-1, 1),
4,
2,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
elif maze_id == 11:
ax.add_patch(
patches.Rectangle((-7, 5),
14,
2,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((5, -5),
2,
10,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((-7, -7),
14,
2,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((-7, -5),
2,
10,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((-1, 1),
6,
2,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((-3, -3),
2,
6,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((-1, -3),
4,
2,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
regions_fig = save_image(fig)
if report is None:
return regions_fig
else:
report.add_image(
regions_fig,
'States per region\nTotal number of states: {}'.format(
sum([len(region.states) for region in self.regions])))
def choroplethNYC(df,
column=None,
cmap='viridis',
ax=None,
cb=True,
kind='continuous',
alpha=1,
color=None,
edgecolor=None,
scheme=None,
k=10,
spacing=False,
lw=1,
width=None,
side=False):
'''creates a choroplath from a dataframe column - NYC tuned
Arguments:
df : a GeoDataFrame
column : a column name
cmap : colorman name (string optional)
ax : axis in figure object (string, optiona, is None a figure is created)
cb : put the color bar. Bool, default is True
kind :
spacing : the spacing for the colorbar (bool, optional)
lw : line width (float, optional, default is 1)
width : with width of the color bar (figure frction, float)
side : default False is left (west), True switches to right (east). If a float is passed that is the location
Returns the figure and the axis, for further manipulation
'''
if ax == None:
ax = pl.figure(figsize=(10, 10)).add_subplot(111)
if column == None:
if color == None:
ax = df.plot(cmap=cmap, alpha=alpha, ax=ax, linewidth=lw)
else:
ax = df.plot(alpha=alpha,
ax=ax,
linewidth=lw,
color=color,
edgecolor=edgecolor)
elif not scheme == None:
ax = df.plot(column=column,
edgecolor=edgecolor,
cmap=cmap,
alpha=alpha,
ax=ax,
linewidth=lw,
scheme=scheme,
k=k,
legend=True)
pl.legend(loc=2)
ax.axis('off')
leg = ax.get_legend()
#pl.legend(bbox_to_anchor=(2, 2), loc=2, borderaxespad=0)
leg.set_bbox_to_anchor((0.35, 0.95, 0, 0))
fig = ax.get_figure()
return None, ax, leg
else:
if kind == 'continuous' and not isinstance(df[column].values[0],
(int, float)):
try:
df[column] = df[column].astype(float)
df[column].replace(np.inf, np.nan, inplace=True)
except ValueError:
kind = 'discrete'
ax = df.dropna(subset=[column]).plot(column=column,
edgecolor=edgecolor,
cmap=cmap,
alpha=alpha,
ax=ax,
linewidth=lw)
vmin, vmax = min(df[column].values), max(df[column].values)
ax.axis('off')
fig = ax.get_figure()
if column == None:
return fig, ax
#if discrete variable you want steps cb
if kind is 'discrete':
nc = df[column].unique()
cmap = discrete_cmap(len(nc), base_cmap=cmap)
# location of colorbar is tuned to the shape of NYC: sits above SI, west of Manhattan
if cb:
if not side:
x0 = 0.2
elif isinstance(side, float):
x0 = side
else:
x0 = 0.9
if not width:
width = 0.03
cax = fig.add_axes([x0, 0.41, width, 0.44])
if kind is 'discrete':
sm = mpl.colorbar.ColorbarBase(
ax=cax,
cmap=cmap,
norm=pl.Normalize(vmin=vmin - .5, vmax=vmax + .5),
#spacing='uniform',
orientation='vertical')
else:
sm = mpl.colorbar.ColorbarBase(ax=cax,
cmap=cmap,
norm=pl.Normalize(vmin=vmin,
vmax=vmax),
ticks=range(spacing + 1),
spacing='uniform',
orientation='vertical')
sm._A = []
if kind is 'discrete':
cb = fig.colorbar(sm,
cax=cax,
ticks=np.linspace(vmin, vmax, len(nc)))
cb.ax.set_yticklabels(['%s' % (c) for c in np.sort(nc)])
else:
cb = fig.colorbar(sm, cax=cax)
return fig, ax, cb
def plot_regions_interest(self, maze_id=0, report=None):
fig, ax = plt.subplots()
interests = self.compute_all_interests()
interest_lims = (min(interests), max(interests))
normal = pylab.Normalize(*interest_lims)
colors = pylab.cm.YlOrRd(normal(interests))
for region, color in zip(self.regions, colors):
lengths = region.max_border - region.min_border
ax.add_patch(
patches.Rectangle(region.min_border,
*lengths,
fill=True,
edgecolor='k',
facecolor=color))
cax, _ = cbar.make_axes(ax)
print("the interest lims are: ", interest_lims)
cb2 = cbar.ColorbarBase(cax, cmap=pylab.cm.YlOrRd, norm=normal)
ax.set_xlim(self.state_bounds[0][0], self.state_bounds[1][0])
ax.set_ylim(self.state_bounds[0][1], self.state_bounds[1][1])
if maze_id == 0:
ax.add_patch(
patches.Rectangle((-3, -3),
10,
2,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((-3, -1),
2,
6,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((-3, 5),
10,
2,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((5, -1),
2,
6,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((-1, 1),
4,
2,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
elif maze_id == 11:
ax.add_patch(
patches.Rectangle((-7, 5),
14,
2,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((5, -5),
2,
10,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((-7, -7),
14,
2,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((-7, -5),
2,
10,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((-1, 1),
6,
2,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((-3, -3),
2,
6,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
ax.add_patch(
patches.Rectangle((-1, -3),
4,
2,
fill=True,
edgecolor="none",
facecolor='0.4',
alpha=0.3))
regions_fig = save_image(fig)
if report is None:
return regions_fig
else:
report.add_image(
regions_fig,
'Interest per region:\nthe number of regions is: {}'.format(
len(self.regions)))
def plot_W_DBZ_T_WZ(w, dbz, t, pp, xx, yy, height, time, member, \
glat=None, glon=None, sfc=False, \
out_prefix=None, vector=False, noshow=False, zoom=None):
filename = "%s_%2.2imin_%2.2dkm" % (out_prefix, int(time),
int(height / 1000.))
fig, (ax1, ax2, ax3, ax4) = plt.subplots(1,
4,
sharey=True,
figsize=figsize)
#----
if (dbz.size > 0):
clevels = N.arange(_min_dbz, _max_dbz + 2.5, 2.5)
plot = ax1.contourf(xx, yy, N.clip(dbz,_min_dbz,_max_dbz), \
clevels, cmap=_ref_cmap)
divider = make_axes_locatable(ax1)
cax = divider.append_axes('right', size='5%', pad=0.05)
fig.colorbar(plot, cax=cax, orientation='vertical', label='dBZ')
plot = ax1.contour(xx,
yy,
dbz,
_dbz_clevels[::2],
colors='k',
linewidths=0.5)
title = ("Reflectivity")
ax1.set_aspect('equal', 'datalim')
ax1.set_title(title, fontsize=10)
if zoom:
ax1.set_xlim(1000 * zoom[0], 1000 * zoom[1])
ax1.set_ylim(1000 * zoom[2], 1000 * zoom[3])
at = AnchoredText(
"Max dBZ: %4.1f" % (dbz.max()),
loc=4,
prop=dict(size=6),
frameon=True,
)
at.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
ax1.add_artist(at)
#----
scale_w_clevels = min(max(N.int(height / 1000.), 1.0), 6.0)
clevels = scale_w_clevels * N.arange(-10., 11., 1.)
wmask = np.ma.masked_array(w, mask=[N.abs(w) <= scale_w_clevels * _min_w])
if (wmask.size > 0):
plot = ax2.contourf(xx, yy, wmask, clevels, cmap='bwr')
divider = make_axes_locatable(ax2)
cax = divider.append_axes('right', size='5%', pad=0.05)
fig.colorbar(plot,
cax=cax,
orientation=_cbar_orien,
label=('W %s' % ("$m s^{-1}$")))
plot = ax2.contour(xx,
yy,
wmask,
clevels[::2],
colors='k',
linewidths=0.5)
title = ("Vertical Velocity")
ax2.set_title(title, fontsize=10)
if zoom:
ax2.set_xlim(1000 * zoom[0], 1000 * zoom[1])
ax2.set_ylim(1000 * zoom[2], 1000 * zoom[3])
at = AnchoredText("Max W: %4.1f \n Min W: %4.1f" % (w.max(),w.min()), \
loc=4, prop=dict(size=6), frameon=True,)
at.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
ax2.add_artist(at)
#--
clevels = N.arange(-20., 21., 1.)
if (pp.size > 0):
plot = ax3.contourf(xx, yy, pp, clevels, cmap='bwr')
divider = make_axes_locatable(ax3)
cax = divider.append_axes('right', size='5%', pad=0.05)
fig.colorbar(plot,
cax=cax,
orientation=_cbar_orien,
label='pertP (mb)')
plot = ax3.contour(xx,
yy,
pp,
clevels[::2],
colors='k',
linewidths=0.5)
title = ("Pert. Pressure in mb)")
ax3.set_title(title, fontsize=10)
if zoom:
ax3.set_xlim(1000 * zoom[0], 1000 * zoom[1])
ax3.set_ylim(1000 * zoom[2], 1000 * zoom[3])
at = AnchoredText("Max pertP: %4.1f \n Min pertP: %4.1f" % (pp.max(),pp.min()), \
loc=4, prop=dict(size=6), frameon=True,)
at.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
ax3.add_artist(at)
#---
if (not vector):
clevels = N.arange(-12., 13., 1.)
plot = ax4.contourf(xx, yy, t, clevels, cmap='bwr')
divider = make_axes_locatable(ax4)
cax = divider.append_axes('right', size='5%', pad=0.05)
fig.colorbar(plot, cax=cax, orientation=_cbar_orien, label='pertT (K)')
plot = ax4.contour(xx, yy, t, clevels[::2], colors='k', linewidths=0.5)
title = ("Pert. Pot. Temperature")
ax4.set_title(title, fontsize=10)
if zoom:
ax4.set_xlim(1000 * zoom[0], 1000 * zoom[1])
ax4.set_ylim(1000 * zoom[2], 1000 * zoom[3])
at = AnchoredText(
"Max pertT: %4.1f \n Min pertT: %4.1f" % (t.max(), t.min()),
loc=4,
prop=dict(size=6),
frameon=True,
)
at.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
ax4.add_artist(at)
else: # Plot wind vectors
ax4.set_title("Vert Velocity & Horizontal Wind", fontsize=10)
scale_w_clevels = min(max(N.int(height / 1000.), 1.0), 6.0)
clevels = scale_w_clevels * N.arange(-10., 11., 1.)
wmask = np.ma.masked_array(w,
mask=[N.abs(w) <= scale_w_clevels * _min_w])
# ax4.contourf(xx, yy, wmask, clevels, cmap='bwr')
ax4.contour(xx, yy, w, clevels[::2], colors='k', linewidths=1.0)
spd = np.sqrt(t[0, :, :]**2 + t[1, :, :]**2)
plot = ax4.quiver(xx[::2,::2], yy[::2,::2], t[0,::2,::2], t[1,::2,::2], spd[::2,::2],
pivot='middle', color='black', width=_vec_w, \
cmap=plt.get_cmap('YlOrRd'), norm=plt.Normalize(vmin=5.0, vmax=60.), \
angles='xy', scale_units='xy', scale=_vector_scale)
divider = make_axes_locatable(ax4)
cax = divider.append_axes('right', size='5%', pad=0.05)
fig.colorbar(plot,
cax=cax,
orientation=_cbar_orien,
label=('W %s' % ("$m s^{-1}$")))
#------- finish
title = ("\n Time: %s min Height: %4.2f km" %
(time, height / 1000.))
fig.suptitle(title, fontsize=12)
if output_format != None:
new_filename = "%s.%s" % (filename, output_format)
print("\n Saving file %s" % (new_filename))
fig.savefig(new_filename, format=output_format, dpi=300)
if interactive and not noshow:
print(filename)
os.system("open %s" % new_filename)
return filename
contour_sets = []
for i in range(h):
for j in range(l):
print("i,j", i, j)
x, y = np.meshgrid(np.arange(0, l1, 1), np.arange(0, l2, 1))
z = Alpha[(i) * l + j].reshape((l2, l1))
levels = MaxNLocator(nbins=15).tick_values(z.min(), z.max())
im = axarr[i, j].contourf(x,
y,
z,
50,
cmap=plt.cm.rainbow,
levels=levels,
vmax=z_max,
vmin=z_min)
f.subplots_adjust(bottom=0.1,
top=0.9,
left=0.1,
right=0.8,
wspace=0.3,
hspace=0.3)
cb_ax = f.add_axes([0.83, 0.1, 0.02, 0.8])
sm = plt.cm.ScalarMappable(cmap=plt.cm.rainbow,
norm=plt.Normalize(vmin=z_min, vmax=z_max))
sm._A = []
plt.colorbar(sm, cax=cb_ax)
plt.suptitle("Modélisation de l'APS pour {} images de tailles {}x{}".format(
N, l1, l2))
plt.show()
ax.errorbar(row['RA'],
row['Dec'],
linestyle='none',
color=pl.cm.jet((row['Velocity']-50)/20.),
marker=marker,
xerr=row['eRA'],
yerr=row['eDec'],
transform=ax.get_transform('fk5'),
)
ax.axis([pmask2.bbox.ixmin-pmask.bbox.ixmin,
pmask2.bbox.ixmax-pmask.bbox.ixmin,
pmask2.bbox.iymin-pmask.bbox.iymin,
pmask2.bbox.iymax-pmask.bbox.iymin])
ax.set_aspect('equal')
sm = pl.cm.ScalarMappable(cmap=pl.cm.jet, norm=pl.Normalize(50,70))
sm._A = []
cb = fig.colorbar(sm,
fig.add_axes([0.90, 0.1, 0.05, 0.8]))
cb.set_label("Velocity (km/s)")
fig.savefig("../figures/masers_on_continuum_velocities_epoch{0}_{1}.pdf".format(epoch, line),
bbox_inches='tight')
epochs = ['02-Oct-2016', '26-Dec-2016', '30-Dec-2016', '07-Jan-2017']
epochs = [datetime.datetime.strptime(x, '%d-%b-%Y') for x in epochs]
tbl.add_column(Column(name='Date', data=[epochs[row['Epoch']] for row in tbl], dtype='datetime64[s]'))
times = [datetime.datetime.utcfromtimestamp((dt64 - np.datetime64('1970-01-01T00:00:00Z')) / np.timedelta64(1, 's'))
def plot_hist(deltas,
concs,
pointToRemove=[-1],
minConc=8,
maxConc=11,
binCount=23,
plotTitle='NIHAO'):
all_delta_percentages = np.array(deltas)
all_delta_percentages = all_delta_percentages[np.logical_not(
np.isnan(all_delta_percentages))]
n, bins, patches = plt.hist(all_delta_percentages,
bins=binCount) #15 is good, as is 23
A = np.vstack((np.digitize(all_delta_percentages, bins), concs)).T
res_lst = np.array([np.mean(A[A[:, 0] == i, 1]) for i in range(len(bins))])
res_lst[np.isnan(res_lst)] = min(concentrations)
res_lst = np.array(res_lst)
maxC = np.average(res_lst)
minC = np.average(res_lst)
for i, j in zip(n, res_lst):
if i < 5:
continue
if j < minC:
minC = j
if j > maxC:
maxC = j
print(maxC)
maxs.append(maxC)
print(minC)
mins.append(minC)
'''
if plotTitle == 'Galaxies with AGN':
minC = 6.3
maxC = 8.3
'''
if minC < 5.5:
minC = 5.5
print("n", n)
print('bins', bins)
print("A", A)
print("res_lst", res_lst)
#minC = 6.5
#maxC = 12.1
cmap = plt.get_cmap('plasma')
#sm = plt.cm.ScalarMappable(cmap='plasma', norm=plt.Normalize(minConc, maxConc))
sm = plt.cm.ScalarMappable(cmap='plasma', norm=plt.Normalize(minC, maxC))
#sm = plt.cm.ScalarMappable(cmap='plasma', norm=plt.Normalize(min(res_lst), max(res_lst)))
sm._A = []
for i in range(len(patches)):
if i in pointToRemove:
patches[i].set_edgecolor('white')
patches[i].set_facecolor('white')
else:
#patches[i].set_facecolor(cmap(norm(res_lst[i],minConc, maxConc) ))
patches[i].set_facecolor(cmap(norm(res_lst[i], minC, maxC)))
#patches[i].set_facecolor(cmap(norm(res_lst[i],min(res_lst), max(res_lst) )))
cbar = plt.colorbar(sm, )
cbar.set_label('c', rotation=270, fontsize=18)
plt.ylabel("number of galaxy datapoints", fontsize=18)
plt.xlabel("$\Delta$ log SFR [M$_\odot$/ year]", fontsize=18)
plt.xlim(-3, 3)
plt.yscale('log')
plt.title(plotTitle)
#plt.tight_layout()
plt.savefig(str("plots/10_hist_" + plotTitle.replace(" ", "") + ".png"),
dpi=300)
plt.show()
sfrEndAtTime = []
sfrEndConc = []
concDict = pickle.load(
open(fileBaseFalcon + '/2018/pickles/concentrations.pkl', 'rb'))
concentrations = concDict.values()
allConc = []
allMstar = []
cmap = plt.get_cmap('plasma')
#colors = [cmap(i) for i in np.linspace(0, 1, number)]
#hacky code for getting a colorbar
sm = plt.cm.ScalarMappable(cmap='plasma',
norm=plt.Normalize(vmin=min(concentrations),
vmax=max(concentrations)))
# fake up the array of the scalar mappable. Urgh...
sm._A = []
#plt.colorbar(sm)
#for a in ['g3.59e12', 'g1.05e13', 'g7.92e12']:
# galDict.pop(a)
'''
g1 = [g for g in galDict.keys()[0:10]]
g2 = [g for g in galDict.keys()[10:20]]
g3 = [g for g in galDict.keys()[20:30]]
g4 = [g for g in galDict.keys() if g not in g1+g2+g3]
'''
g1 = ['g8.26e11', 'g5.38e11']
g2 = ['g8.26e11', 'g3.21e11']
def fission_density_c(sp, case):
"""Generates a csv and png file with results of fission source
distribution by 1/5 stripes for phase 1a-c of the benchmark
in the analysis_output folder.
Parameters
----------
sp: openmc.statepoint.StatePoint
this statepoint.h5 file is created by running the openmc
executable on the xml files generated by the build_xml.py
files and when tallies_on toggle is on True.
case: str
case number for naming files
Returns
-------
This function generates a csv file with fission density results
and visualization of fission source distribution by 1/5 stripe
for phase 1a-c of the benchmark.
"""
name = 'analysis_output/' + case + '_c'
region = ['1', '2', '3', '4', '5']
fission_rates = []
num = 1
for x in range(1, 13):
mesh_tally = sp.get_tally(name='mesh tally c' + str(x))
a = mesh_tally.get_pandas_dataframe()
a = a.drop(columns=['mesh ' + str(x), 'nuclide', 'score'])
if (x % 2) == 0:
num = int(x / 2)
stripe = [str(num) + 'B'] * 5
else:
num = int((x + 1) / 2)
stripe = [str(num) + 'T'] * 5
a['Region'] = region
a['Stripe'] = stripe
if x == 1:
df = a
else:
df = df.append(a)
b = a['mean'].to_numpy()
fission_rates.append(b)
df = df.set_index(['Stripe', 'Region'])
ave = df['mean'].mean()
df['Fission Density'] = df['mean'] / ave
ave_sd = 1 / (len(df['mean'])) * np.sqrt(np.sum(df['std. dev.']**2))
df['FD std dev'] = df['Fission Density'] * \
np.sqrt((df['std. dev.'] / df['mean'])**2 + (ave_sd / ave)**2)
df['Relative unc.'] = df['FD std dev'] / df['Fission Density']
df.to_csv(name + '.csv')
xs = []
ys = []
ws = np.array([F_len / 5] * 60)
hs = np.array([F_width] * 60)
fission_rates /= np.mean(fission_rates)
vs = fission_rates.flatten()
for p in range(6):
for f in range(2):
x_trans = p * T['A1']['P']['x']
y_trans = p * T['A1']['P']['y']
if f == 1:
x_trans += T['A1']['F']['x']
y_trans += T['A1']['F']['y']
for s in range(5):
if s > 0:
x_trans += F_len / 5
xs.append(V['A1']['F']['L']['x'] + x_trans)
ys.append(V['A1']['F']['B']['y'] + y_trans)
normal = pl.Normalize(vs.min(), vs.max())
colors = pl.cm.YlOrRd(normal(vs))
fig, ax = plt.subplots()
for x, y, w, h, c in zip(xs, ys, ws, hs, colors):
rect = pl.Rectangle((x, y), w, h, color=c)
ax.add_patch(rect)
cax, _ = cbar.make_axes(ax)
cb2 = cbar.ColorbarBase(cax, cmap=pl.cm.YlOrRd, norm=normal)
ax.set_xlim(-25, 12)
ax.set_ylim(-25, 0)
ax.set_xlabel('x [cm]')
ax.set_ylabel('y [cm]')
ax.set_title('Case ' + case + ': Normalized Fission Source')
pl.savefig(name, bbox_inches='tight')
return