def gather_all_data(opt):
""" Main function for separate plots for ampdet terms """
LOG.info("Starting main evaluation loop.")
opt = tripcor.check_opt(opt)
# main loop
for beam, xing, error_types, error_loc in main_loop(opt):
_gather_data(opt.cwd, opt.machine, beam, xing, error_types, error_loc,
opt.optic_types, opt.seeds, opt.unused_stages)
def make_histogram_plots(opt):
alpha_mean = .2
alpha_hist = .6
title = ""
ps.set_style('standard')
opt = tripcor.check_opt(opt)
cwd = get_data_output_folder(opt.cwd)
output_folder = get_plot_output_folder(opt.cwd)
for beam, xing, error_types, error_loc, optic_type in hist_loop(opt):
fig, axs = plt.subplots(len(AMPDET_NAMES), 1)
for idx_data, output_id in _ordered_output_ids(opt.machine, beam,
opt.unused_stages):
title, filename = get_seed_data_title_and_filename(
beam, xing, error_types, error_loc, optic_type, output_id)
seed_df = tfs.read_tfs(os.path.join(cwd, filename), index="SEED")
y_max = len(seed_df.index)
for idx_ax, term in enumerate(AMPDET_NAMES):
ax = axs[idx_ax]
data = seed_df[term]
x_pos = data.mean()
# plot mean
stem_cont = ax.stem([x_pos], [y_max],
markerfmt="",
basefmt="",
label="_nolegend_")
plt.setp(stem_cont[1],
color=ps.get_mpl_color(idx_data),
alpha=alpha_mean)
# plot std
# error = data.std()
# ebar_cont = ax.errorbar(x_pos, y_max, xerr=error, color=ps.get_mpl_color(idx_data),
# label="_nolegend_", marker="")
# ps.change_ebar_alpha_for_line(ebar_cont, alpha_mean)
# plot histogram
data.hist(ax=ax,
alpha=alpha_hist,
color=ps.get_mpl_color(idx_data),
label=output_id)
for idx_ax, term in enumerate(AMPDET_NAMES):
axs[idx_ax].set_xlabel(term)
axs[idx_ax].set_ylabel("Seeds")
legend = axs[0].legend()
_reorder_legend(
legend, _get_all_output_ids(opt.machine, beam, opt.unused_stages))
ps.sync2d(axs)
title = " ".join(title.split(" ")[:-1])
figfilename = "{:s}.{:s}".format(title.replace(' ', '.'), "histogram")
fig.canvas.set_window_title(title)
make_bottom_text(axs[-1], title)
fig.savefig(os.path.join(output_folder, figfilename + ".pdf"))
fig.savefig(os.path.join(output_folder, figfilename + ".png"))
def main_body(opt):
""" Same stuff for both mains """
LOG.info("Starting main evaluation loop.")
opt = tripcor.check_opt(opt)
output_folder = get_plot_output_folder(opt.cwd)
# main loop
for beam, xing, error_types, error_loc in main_loop(opt):
current_params = dict(beam=beam,
xing=xing,
error_types=error_types,
error_loc=error_loc)
data = {}
for otype in opt.optic_types:
data[otype] = _load_and_average_data(opt.cwd, opt.machine, beam,
otype, xing, error_types,
error_loc, opt.unused_stages)
yield output_folder, data, current_params
def plot_crossing_oneplot(opt):
""" Main function to put all crossing into one plot. """
LOG.info("Starting crossing evaluation loop.")
opt = tripcor.check_opt(opt)
output_folder = get_plot_output_folder(opt.cwd)
# for beam in opt.beams:
# for otype in opt.optic_types:
# for error_types in opt.error_types:
# for error_loc in opt.error_locations:
# for xing in opt.xing:
# current_params = dict(beam=beam, optic_type=otype, error_types=error_types,
# error_loc=error_loc, xing=xing)
# _update_min_max(_load_and_average_data(opt.cwd, opt.machine,
# beam, otype, xing,
# error_types, error_loc,
# opt.unused_stages), current_params)
for beam in opt.beams:
for otype in opt.optic_types:
for error_types in opt.error_types:
for error_loc in opt.error_locations:
current_params = dict(beam=beam,
optic_type=otype,
error_types=error_types,
error_loc=error_loc)
data = {}
ordered = []
for xing in opt.xing:
id = tripcor.get_nameparts_from_parameters(
xing=xing)[0]
ordered.append(id)
data[id] = _load_and_average_data(
opt.cwd, opt.machine, beam, otype, xing,
error_types, error_loc, opt.unused_stages)
_plot_separate_ampdet(output_folder,
data,
current_params,
ordered,
id="all_xing")
def make_cta_histogram(opt):
alpha_mean = 1
alpha_hist = .4
mod_one = lambda x: np.mod(x, 1)
ps.set_style('standard', MANUAL_STYLE_CTA)
opt = tripcor.check_opt(opt)
cwd = get_data_output_folder(opt.cwd)
output_folder = get_cta_plot_output_folder(opt.cwd)
for beam, error_types, error_loc, optic_type in cta_loop(opt):
fig, ax = plt.subplots(1, 1)
color_cycle = get_colors()
for xing in opt.xing:
color = color_cycle.next()
_, filename = get_cta_seed_data_title_and_filename(
beam, xing, error_types, error_loc, optic_type)
xing_label = tripcor.get_nameparts_from_parameters(xing=xing)[0]
try:
xing_label = legend_map[xing_label]
except KeyError:
pass
seed_df = tfs.read_tfs(os.path.join(cwd, filename),
index="SEED").apply(mod_one)
diff = np.abs(seed_df.QX - seed_df.QY) * 1e4
y_max = len(seed_df.index) / 3
# plot mean
x_pos = diff.mean()
stem_cont = ax.stem([x_pos], [y_max],
markerfmt="",
basefmt="",
label="_nolegend_")
plt.setp(stem_cont[1], color=color, alpha=alpha_mean, ls="--")
# plot std
# error = data.std()
# ebar_cont = ax.errorbar(x_pos, y_max, xerr=error, color=ps.get_mpl_color(idx_data),
# label="_nolegend_", marker="")
# ps.change_ebar_alpha_for_line(ebar_cont, alpha_mean)
# plot histogram
if diff.std() > 0:
diff.hist(ax=ax,
alpha=alpha_hist,
color=color,
label="_nolegend_")
diff.hist(ax=ax,
histtype="step",
color=color,
label=xing_label)
ax.set_xlabel("|C$^{-}$| [$10^{-4}$]")
ax.set_ylabel("Count")
legend = ps.make_top_legend(ax, 2)
legend.remove()
figtitle, figfilename = get_cta_plot_title_and_filename(
beam, error_types, error_loc, optic_type)
fig.canvas.set_window_title(figtitle)
fig.tight_layout()
fig.savefig(os.path.join(output_folder, figfilename + ".hist.pdf"))
fig.savefig(os.path.join(output_folder, figfilename + ".hist.png"))