def run(cfg_dir):
"""Run shadow in ``cfg_dir``
Args:
cfg_dir (str): directory to run shadow in
"""
with pkio.save_chdir(cfg_dir):
beam = _run_shadow()
data = simulation_db.read_json(template_common.INPUT_BASE_NAME)
model = data['models'][data['report']]
column_values = _SCHEMA['enum']['ColumnValue']
if 'y' in model:
x_range = None
y_range = None
if model['overrideSize'] == '1':
x_range = (np.array([
model['horizontalOffset'] - model['horizontalSize'] / 2,
model['horizontalOffset'] + model['horizontalSize'] / 2,
]) * _MM_TO_CM).tolist()
y_range = (np.array([
model['verticalOffset'] - model['verticalSize'] / 2,
model['verticalOffset'] + model['verticalSize'] / 2,
]) * _MM_TO_CM).tolist()
ticket = beam.histo2(int(model['x']), int(model['y']), nbins=template_common.histogram_bins(model['histogramBins']), ref=int(model['weight']), nolost=1, calculate_widths=0, xrange=x_range, yrange=y_range)
_scale_ticket(ticket)
res = {
'x_range': [ticket['xrange'][0], ticket['xrange'][1], ticket['nbins_h']],
'y_range': [ticket['yrange'][0], ticket['yrange'][1], ticket['nbins_v']],
'x_label': _label_with_units(model['x'], column_values),
'y_label': _label_with_units(model['y'], column_values),
'z_label': 'Frequency',
'title': u'{}, {}'.format(_label(model['x'], column_values), _label(model['y'], column_values)),
'z_matrix': ticket['histogram'].T.tolist(),
'frameCount': 1,
}
else:
weight = int(model['weight'])
ticket = beam.histo1(int(model['column']), nbins=template_common.histogram_bins(model['histogramBins']), ref=weight, nolost=1, calculate_widths=0)
_scale_ticket(ticket)
res = {
'title': _label(model['column'], column_values),
'x_range': [ticket['xrange'][0], ticket['xrange'][1], ticket['nbins']],
'y_label': u'{}{}'.format(
'Number of Rays',
u' weighted by {}'.format(_label_for_weight(model['weight'], column_values)) if weight else ''),
'x_label': _label_with_units(model['column'], column_values),
'points': ticket['histogram'].T.tolist(),
'frameCount': 1,
}
#pkdp('range amount: {}', res['x_range'][1] - res['x_range'][0])
#1.55431223448e-15
dist = res['x_range'][1] - res['x_range'][0]
#TODO(pjm): only rebalance range if outside of 0
if dist < 1e-14:
#TODO(pjm): include offset range for client
res['x_range'][0] = 0
res['x_range'][1] = dist
simulation_db.write_result(res)
def _run_bunch_report(data, bunch, twiss):
twiss0 = twiss[0]
report = data.models[data['report']]
particles = bunch.get_local_particles()
x = particles[:, getattr(bunch, report['x'])]
y = particles[:, getattr(bunch, report['y'])]
hist, edges = np.histogramdd([x, y],
template_common.histogram_bins(
report['histogramBins']))
return {
'x_range': [float(edges[0][0]),
float(edges[0][-1]),
len(hist)],
'y_range': [float(edges[1][0]),
float(edges[1][-1]),
len(hist[0])],
'x_label': template.label(report['x']),
'y_label': template.label(report['y']),
'title': '{}-{}'.format(report['x'], report['y']),
'z_matrix': hist.T.tolist(),
'summaryData': {
'bunchTwiss': {
'alpha_x': template.format_float(twiss0['alpha_x']),
'alpha_y': template.format_float(twiss0['alpha_y']),
'beta_x': template.format_float(twiss0['beta_x']),
'beta_y': template.format_float(twiss0['beta_y']),
},
},
}
def extract_report_data(xFilename, data, page_index, page_count=0):
xfield = data['x'] if 'x' in data else data[_X_FIELD]
# x, column_names, x_def, err
x_col = sdds_util.extract_sdds_column(xFilename, xfield, page_index)
if x_col['err']:
return x_col['err']
x = x_col['values']
if not _is_histogram_file(xFilename, x_col['column_names']):
# parameter plot
plots = []
filename = {
'y1': xFilename,
#TODO(pjm): y2Filename, y3Filename are not currently used. Would require rescaling x value across files.
'y2': xFilename,
'y3': xFilename,
}
for f in ('y1', 'y2', 'y3'):
if re.search(r'^none$', data[f], re.IGNORECASE) or data[f] == ' ':
continue
yfield = data[f]
y_col = sdds_util.extract_sdds_column(filename[f], yfield,
page_index)
if y_col['err']:
return y_col['err']
y = y_col['values']
plots.append({
'points': y,
'label': _field_label(yfield, y_col['column_def'][1]),
})
title = ''
if page_count > 1:
title = 'Plot {} of {}'.format(page_index + 1, page_count)
return {
'title': title,
'x_range': [min(x), max(x)],
'y_label': '',
'x_label': _field_label(xfield, x_col['column_def'][1]),
'x_points': x,
'plots': plots,
'y_range': template_common.compute_plot_color_and_range(plots),
}
yfield = data['y1'] if 'y1' in data else data['y']
y_col = sdds_util.extract_sdds_column(xFilename, yfield, page_index)
if y_col['err']:
return y_col['err']
y = y_col['values']
bins = data['histogramBins']
hist, edges = np.histogramdd([x, y], template_common.histogram_bins(bins))
return {
'x_range': [float(edges[0][0]),
float(edges[0][-1]),
len(hist)],
'y_range': [float(edges[1][0]),
float(edges[1][-1]),
len(hist[0])],
'x_label': _field_label(xfield, x_col['column_def'][1]),
'y_label': _field_label(yfield, y_col['column_def'][1]),
'title': _plot_title(xfield, yfield, page_index, page_count),
'z_matrix': hist.T.tolist(),
}
def _extract_bunch_plot(report, frame_index, run_dir):
filename = _particle_file_list(run_dir)[frame_index]
with h5py.File(str(filename), 'r') as f:
x = f['particles'][:, _COORD6.index(report['x'])].tolist()
y = f['particles'][:, _COORD6.index(report['y'])].tolist()
data = simulation_db.read_json(run_dir.join(template_common.INPUT_BASE_NAME))
if 'bunchAnimation' not in data.models:
# In case the simulation was run before the bunchAnimation was added
return {
'error': 'Report not generated',
}
bunch_animation = data.models.bunchAnimation
range = None
if report['plotRangeType'] == 'fixed':
range = [_plot_range(report, 'horizontal'), _plot_range(report, 'vertical')]
elif report['plotRangeType'] == 'fit' and 'fieldRange' in bunch_animation:
range = [bunch_animation.fieldRange[report['x']], bunch_animation.fieldRange[report['y']]]
hist, edges = np.histogramdd([x, y], template_common.histogram_bins(report['histogramBins']), range=range)
tlen = f['tlen'][()]
s_n = f['s_n'][()]
rep = 0 if s_n == 0 else int(round(tlen / s_n))
return {
'x_range': [float(edges[0][0]), float(edges[0][-1]), len(hist)],
'y_range': [float(edges[1][0]), float(edges[1][-1]), len(hist[0])],
'x_label': label(report['x']),
'y_label': label(report['y']),
'title': '{}-{} at {:.1f}m, turn {}'.format(report['x'], report['y'], tlen, rep),
'z_matrix': hist.T.tolist(),
}
def _extract_particle_plot(report, run_dir, page_index):
xfield = _map_field_name(report['x'])
yfield = _map_field_name(report['y'])
bins = report['histogramBins']
filename = _ion_files(run_dir)[page_index]
data = simulation_db.read_json(run_dir.join(template_common.INPUT_BASE_NAME))
settings = data.models.simulationSettings
time = settings.time / settings.step_number * settings.save_particle_interval * page_index
if time > settings.time:
time = settings.time
x_col = sdds_util.extract_sdds_column(filename, xfield, 0)
if x_col['err']:
return x_col['err']
x = x_col['values']
y_col = sdds_util.extract_sdds_column(filename, yfield, 0)
if y_col['err']:
return y_col['err']
y = y_col['values']
particle_animation = data.models.particleAnimation
range = None
if report['plotRangeType'] == 'fixed':
range = [_plot_range(report, 'horizontal'), _plot_range(report, 'vertical')]
elif report['plotRangeType'] == 'fit' and 'fieldRange' in particle_animation:
range = [particle_animation.fieldRange[xfield], particle_animation.fieldRange[yfield]]
hist, edges = np.histogramdd([x, y], template_common.histogram_bins(bins), range=range)
return {
'x_range': [float(edges[0][0]), float(edges[0][-1]), len(hist)],
'y_range': [float(edges[1][0]), float(edges[1][-1]), len(hist[0])],
'x_label': _field_label(xfield, x_col['column_def']),
'y_label': _field_label(yfield, y_col['column_def']),
'title': 'Ions at time {:.2f} [s]'.format(time),
'z_matrix': hist.T.tolist(),
}
def save_report_data(data, run_dir):
if 'bunchReport' in data['report']:
import synergia.bunch
with h5py.File(str(run_dir.join(OUTPUT_FILE['twissReport'])), 'r') as f:
twiss0 = dict(map(
lambda k: (k, format_float(f[k][0])),
('alpha_x', 'alpha_y', 'beta_x', 'beta_y'),
))
report = data.models[data['report']]
bunch = data.models.bunch
if bunch.distribution == 'file':
bunch_file = template_common.lib_file_name('bunch', 'particleFile', bunch.particleFile)
else:
bunch_file = OUTPUT_FILE['bunchReport']
if not run_dir.join(bunch_file).exists():
return
with h5py.File(str(run_dir.join(bunch_file)), 'r') as f:
x = f['particles'][:, getattr(synergia.bunch.Bunch, report['x'])]
y = f['particles'][:, getattr(synergia.bunch.Bunch, report['y'])]
hist, edges = np.histogramdd([x, y], template_common.histogram_bins(report['histogramBins']))
res = {
'title': '',
'x_range': [float(edges[0][0]), float(edges[0][-1]), len(hist)],
'y_range': [float(edges[1][0]), float(edges[1][-1]), len(hist[0])],
'x_label': label(report['x'], _SCHEMA['enum']['PhaseSpaceCoordinate8']),
'y_label': label(report['y'], _SCHEMA['enum']['PhaseSpaceCoordinate8']),
'z_matrix': hist.T.tolist(),
'summaryData': {
'bunchTwiss': twiss0,
},
}
else:
report_name = data['report']
x = None
plots = []
report = data['models'][report_name]
with h5py.File(str(run_dir.join(OUTPUT_FILE[report_name])), 'r') as f:
x = f['s'][:].tolist()
for yfield in ('y1', 'y2', 'y3'):
if report[yfield] == 'none':
continue
name = report[yfield]
plots.append({
'name': name,
'label': label(report[yfield], _SCHEMA['enum']['TwissParameter']),
'points': f[name][:].tolist(),
})
res = {
'title': '',
'x_range': [min(x), max(x)],
'y_range': template_common.compute_plot_color_and_range(plots),
'x_label': 's [m]',
'y_label': '',
'x_points': x,
'plots': plots,
}
simulation_db.write_result(res, run_dir=run_dir)
def extract_report_data(xFilename, data, page_index, page_count=0):
xfield = data['x'] if 'x' in data else data[_X_FIELD]
# x, column_names, x_def, err
x_col = sdds_util.extract_sdds_column(xFilename, xfield, page_index)
if x_col['err']:
return x_col['err']
x = x_col['values']
if not _is_histogram_file(xFilename, x_col['column_names']):
# parameter plot
plots = []
filename = {
'y1': xFilename,
#TODO(pjm): y2Filename, y3Filename are not currently used. Would require rescaling x value across files.
'y2': xFilename,
'y3': xFilename,
}
for f in ('y1', 'y2', 'y3'):
if re.search(r'^none$', data[f], re.IGNORECASE) or data[f] == ' ':
continue
yfield = data[f]
y_col = sdds_util.extract_sdds_column(filename[f], yfield, page_index)
if y_col['err']:
return y_col['err']
y = y_col['values']
plots.append({
'points': y,
'label': _field_label(yfield, y_col['column_def'][1]),
})
title = ''
if page_count > 1:
title = 'Plot {} of {}'.format(page_index + 1, page_count)
return {
'title': title,
'x_range': [min(x), max(x)],
'y_label': '',
'x_label': _field_label(xfield, x_col['column_def'][1]),
'x_points': x,
'plots': plots,
'y_range': template_common.compute_plot_color_and_range(plots),
}
yfield = data['y1'] if 'y1' in data else data['y']
y_col = sdds_util.extract_sdds_column(xFilename, yfield, page_index)
if y_col['err']:
return y_col['err']
y = y_col['values']
bins = data['histogramBins']
hist, edges = np.histogramdd([x, y], template_common.histogram_bins(bins))
return {
'x_range': [float(edges[0][0]), float(edges[0][-1]), len(hist)],
'y_range': [float(edges[1][0]), float(edges[1][-1]), len(hist[0])],
'x_label': _field_label(xfield, x_col['column_def'][1]),
'y_label': _field_label(yfield, y_col['column_def'][1]),
'title': _plot_title(xfield, yfield, page_index, page_count),
'z_matrix': hist.T.tolist(),
}
def extract_beam_histrogram(report, run_dir, frame):
beam_info = hellweg_dump_reader.beam_info(_dump_file(run_dir), frame)
points = hellweg_dump_reader.get_points(beam_info, report.reportType)
hist, edges = np.histogram(points, template_common.histogram_bins(report.histogramBins))
return {
'title': _report_title(report.reportType, 'BeamHistogramReportType', beam_info),
'x_range': [edges[0], edges[-1]],
'y_label': 'Number of Particles',
'x_label': hellweg_dump_reader.get_label(report.reportType),
'points': hist.T.tolist(),
}
def extract_particle_report(frame_args, particle_type):
data_file = open_data_file(frame_args.run_dir, frame_args.frameIndex)
xarg = frame_args.x
yarg = frame_args.y
nbins = frame_args.histogramBins
opmd = _opmd_time_series(data_file)
data_list = opmd.get_particle(
var_list=[xarg, yarg],
species=particle_type,
iteration=numpy.array([data_file.iteration]),
select=None,
output=True,
plot=False,
)
with h5py.File(data_file.filename) as f:
data_list.append(main.read_species_data(f, particle_type, 'w', ()))
select = _particle_selection_args(frame_args)
if select:
with h5py.File(data_file.filename) as f:
main.apply_selection(f, data_list, select, particle_type, ())
xunits = ' [m]' if len(xarg) == 1 else ''
yunits = ' [m]' if len(yarg) == 1 else ''
if len(xarg) == 1:
data_list[0] /= 1e6
if len(yarg) == 1:
data_list[1] /= 1e6
if xarg == 'z':
data_list = _adjust_z_width(data_list, data_file)
hist, edges = numpy.histogramdd(
[data_list[0], data_list[1]],
template_common.histogram_bins(nbins),
weights=data_list[2],
range=[
_select_range(data_list[0], xarg, select),
_select_range(data_list[1], yarg, select)
],
)
return PKDict(
x_range=[float(edges[0][0]),
float(edges[0][-1]),
len(hist)],
y_range=[float(edges[1][0]),
float(edges[1][-1]),
len(hist[0])],
x_label='{}{}'.format(xarg, xunits),
y_label='{}{}'.format(yarg, yunits),
title='t = {}'.format(_iteration_title(opmd, data_file)),
z_matrix=hist.T.tolist(),
frameCount=data_file.num_frames,
)
def _extract_bunch_data(report, col_names, rows, title):
x_info = _PHASE_SPACE_FIELD_INFO[report['x']]
y_info = _PHASE_SPACE_FIELD_INFO[report['y']]
x = np.array(column_data(report['x'], col_names, rows)) * x_info[1]
y = np.array(column_data(report['y'], col_names, rows)) * y_info[1]
hist, edges = np.histogramdd([x, y], template_common.histogram_bins(report.histogramBins))
return {
'x_range': [float(edges[0][0]), float(edges[0][-1]), len(hist)],
'y_range': [float(edges[1][0]), float(edges[1][-1]), len(hist[0])],
'x_label': x_info[0],
'y_label': y_info[0],
'title': title,
'z_matrix': hist.T.tolist(),
'z_label': 'Number of Particles',
}
def sim_frame_beamHistogramAnimation(frame_args):
beam_info = hellweg_dump_reader.beam_info(_dump_file(run_dir))
points = hellweg_dump_reader.get_points(beam_info, frame_args.reportType)
hist, edges = np.histogram(
points, template_common.histogram_bins(frame_args.histogramBins))
return {
'title':
_report_title(frame_args.reportType, 'BeamHistogramReportType',
beam_info),
'x_range': [edges[0], edges[-1]],
'y_label':
'Number of Particles',
'x_label':
hellweg_dump_reader.get_label(frame_args.reportType),
'points':
hist.T.tolist(),
}
def extract_particle_report(args, particle_type, run_dir, data_file):
xarg = args.x
yarg = args.y
nbins = args.histogramBins
opmd = _opmd_time_series(data_file)
data_list = opmd.get_particle(
var_list=[xarg, yarg],
species=particle_type,
iteration=numpy.array([data_file.iteration]),
select=None,
output=True,
plot=False,
)
with h5py.File(data_file.filename) as f:
data_list.append(main.read_species_data(f, particle_type, 'w', ()))
select = _particle_selection_args(args)
if select:
with h5py.File(data_file.filename) as f:
main.apply_selection(f, data_list, select, particle_type, ())
xunits = ' [m]' if len(xarg) == 1 else ''
yunits = ' [m]' if len(yarg) == 1 else ''
if len(xarg) == 1:
data_list[0] /= 1e6
if len(yarg) == 1:
data_list[1] /= 1e6
if xarg == 'z':
data_list = _adjust_z_width(data_list, data_file)
hist, edges = numpy.histogramdd(
[data_list[0], data_list[1]],
template_common.histogram_bins(nbins),
weights=data_list[2],
range=[_select_range(data_list[0], xarg, select), _select_range(data_list[1], yarg, select)],
)
return {
'x_range': [float(edges[0][0]), float(edges[0][-1]), len(hist)],
'y_range': [float(edges[1][0]), float(edges[1][-1]), len(hist[0])],
'x_label': '{}{}'.format(xarg, xunits),
'y_label': '{}{}'.format(yarg, yunits),
'title': 't = {}'.format(_iteration_title(opmd, data_file)),
'z_matrix': hist.T.tolist(),
'frameCount': data_file.num_frames,
}
def _extract_bunch_data(report, col_names, rows, title):
x_info = _PHASE_SPACE_FIELD_INFO[report['x']]
y_info = _PHASE_SPACE_FIELD_INFO[report['y']]
x = np.array(column_data(report['x'], col_names, rows)) * x_info[1]
y = np.array(column_data(report['y'], col_names, rows)) * y_info[1]
hist, edges = np.histogramdd([x, y],
template_common.histogram_bins(
report.histogramBins))
return {
'x_range': [float(edges[0][0]),
float(edges[0][-1]),
len(hist)],
'y_range': [float(edges[1][0]),
float(edges[1][-1]),
len(hist[0])],
'x_label': x_info[0],
'y_label': y_info[0],
'title': title,
'z_matrix': hist.T.tolist(),
'z_label': 'Number of Particles',
}
def extract_beam_report(report, run_dir, frame):
beam_info = hellweg_dump_reader.beam_info(_dump_file(run_dir), frame)
x, y = report.reportType.split('-')
data_list = [
hellweg_dump_reader.get_points(beam_info, x),
hellweg_dump_reader.get_points(beam_info, y),
]
hist, edges = numpy.histogramdd(
data_list, template_common.histogram_bins(report.histogramBins))
return {
'x_range': [float(edges[0][0]),
float(edges[0][-1]),
len(hist)],
'y_range': [float(edges[1][0]),
float(edges[1][-1]),
len(hist[0])],
'x_label': hellweg_dump_reader.get_label(x),
'y_label': hellweg_dump_reader.get_label(y),
'title': _report_title(report.reportType, 'BeamReportType', beam_info),
'z_matrix': hist.T.tolist(),
'z_label': 'Number of Particles',
'summaryData': _summary_text(run_dir),
}
def _run_bunch_report(data):
import synergia.bunch
with h5py.File(template.OUTPUT_FILE['twissReport'], 'r') as f:
twiss0 = dict(
map(
lambda k: (k, template.format_float(f[k][0])),
('alpha_x', 'alpha_y', 'beta_x', 'beta_y'),
))
report = data.models[data['report']]
bunch = data.models.bunch
if bunch.distribution == 'file':
bunch_file = template_common.lib_file_name('bunch', 'particleFile',
bunch.particleFile)
else:
bunch_file = template.OUTPUT_FILE['bunchReport']
with h5py.File(bunch_file, 'r') as f:
x = f['particles'][:, getattr(synergia.bunch.Bunch, report['x'])]
y = f['particles'][:, getattr(synergia.bunch.Bunch, report['y'])]
hist, edges = np.histogramdd([x, y],
template_common.histogram_bins(
report['histogramBins']))
return {
'title': '{}-{}'.format(report['x'], report['y']),
'x_range': [float(edges[0][0]),
float(edges[0][-1]),
len(hist)],
'y_range': [float(edges[1][0]),
float(edges[1][-1]),
len(hist[0])],
'x_label': template.label(report['x']),
'y_label': template.label(report['y']),
'z_matrix': hist.T.tolist(),
'summaryData': {
'bunchTwiss': twiss0,
},
}
def extract_report_data(xFilename, yFilename, data, page_index):
xfield = data['x'] if 'x' in data else data[_X_FIELD]
# x, column_names, x_def, err
x_col = sdds_util.extract_sdds_column(xFilename, xfield, page_index)
if x_col['err']:
return x_col['err']
x = x_col['values']
if 'y1' in data:
# parameter plot
y_range = None
plots = []
for f in ('y1', 'y2', 'y3'):
if data[f] == 'none':
continue
yfield = data[f]
y_col = sdds_util.extract_sdds_column(yFilename, yfield,
page_index)
if y_col['err']:
return y_col['err']
y = y_col['values']
if y_range:
y_range[0] = min(y_range[0], min(y))
y_range[1] = max(y_range[1], max(y))
else:
y_range = [min(y), max(y)]
plots.append({
'points': y,
'label': _field_label(yfield, y_col['column_def'][1]),
#TODO(pjm): refactor with template_common.compute_plot_color_and_range()
'color': _PLOT_LINE_COLOR[f],
})
return {
'title': '',
'x_range': [min(x), max(x)],
'y_label': '',
'x_label': _field_label(_X_FIELD, x_col['column_def'][1]),
'x_points': x,
'plots': plots,
'y_range': y_range,
}
yfield = data['y']
y_col = sdds_util.extract_sdds_column(yFilename, yfield, page_index)
if y_col['err']:
return y_col['err']
y = y_col['values']
if _is_2d_plot(x_col['column_names']):
# 2d plot
return {
'title': _plot_title(xfield, yfield, page_index),
'x_range': [np.min(x), np.max(x)],
'x_label': _field_label(xfield, x_col['column_def'][1]),
'y_label': _field_label(yfield, y_col['column_def'][1]),
'points': y,
'x_points': x,
}
bins = data['histogramBins']
hist, edges = np.histogramdd([x, y], template_common.histogram_bins(bins))
return {
'x_range': [float(edges[0][0]),
float(edges[0][-1]),
len(hist)],
'y_range': [float(edges[1][0]),
float(edges[1][-1]),
len(hist[0])],
'x_label': _field_label(xfield, x_col['column_def'][1]),
'y_label': _field_label(yfield, y_col['column_def'][1]),
'title': _plot_title(xfield, yfield, page_index),
'z_matrix': hist.T.tolist(),
}
def _run_shadow(cfg_dir, data):
beam = template_common.exec_parameters().beam
model = data['models'][data['report']]
column_values = _SCHEMA['enum']['ColumnValue']
if 'y' in model:
x_range = None
y_range = None
if model['overrideSize'] == '1':
x_range = (numpy.array([
model['horizontalOffset'] - model['horizontalSize'] / 2,
model['horizontalOffset'] + model['horizontalSize'] / 2,
]) * _MM_TO_CM).tolist()
y_range = (numpy.array([
model['verticalOffset'] - model['verticalSize'] / 2,
model['verticalOffset'] + model['verticalSize'] / 2,
]) * _MM_TO_CM).tolist()
ticket = beam.histo2(
int(model['x']),
int(model['y']),
nbins=template_common.histogram_bins(model['histogramBins']),
ref=int(model['weight']),
nolost=1,
calculate_widths=0,
xrange=x_range,
yrange=y_range,
)
_scale_ticket(ticket)
values = ticket['histogram'].T
assert not numpy.isnan(values).any(), 'nan values found'
res = PKDict(
x_range=[
ticket['xrange'][0], ticket['xrange'][1], ticket['nbins_h']
],
y_range=[
ticket['yrange'][0], ticket['yrange'][1], ticket['nbins_v']
],
x_label=_label_with_units(model['x'], column_values),
y_label=_label_with_units(model['y'], column_values),
z_label='Intensity' if int(model['weight']) else 'Rays',
title=u'{}, {}'.format(_label(model['x'], column_values),
_label(model['y'], column_values)),
z_matrix=values.tolist(),
frameCount=1,
)
else:
weight = int(model['weight'])
ticket = beam.histo1(
int(model['column']),
nbins=template_common.histogram_bins(model['histogramBins']),
ref=weight,
nolost=1,
calculate_widths=0,
)
_scale_ticket(ticket)
res = PKDict(
title=_label(model['column'], column_values),
x_range=[
ticket['xrange'][0], ticket['xrange'][1], ticket['nbins']
],
y_label=u'{}{}'.format(
'Number of Rays', u' weighted by {}'.format(
_label_for_weight(model['weight'], column_values))
if weight else ''),
x_label=_label_with_units(model['column'], column_values),
points=ticket['histogram'].T.tolist(),
frameCount=1,
)
#pkdlog('range amount: {}', res['x_range'][1] - res['x_range'][0])
#1.55431223448e-15
dist = res['x_range'][1] - res['x_range'][0]
#TODO(pjm): only rebalance range if outside of 0
if dist < 1e-14:
#TODO(pjm): include offset range for client
res['x_range'][0] = 0
res['x_range'][1] = dist
return res
