def _show_plot(self, data):
cd = dict(padding=5, stack_order='top_to_bottom')
csnames = self.column_names
xmin = np.Inf
xmax = -np.Inf
if self.as_series:
g = RegressionGraph(container_dict=cd)
p = g.new_plot(padding=[50, 5, 5, 50],
xtitle=''
)
p.value_range.tight_bounds = False
p.value_range.margin = 0.1
else:
g = StackedRegressionGraph(container_dict=cd)
regressable = False
# metadata = None
for i, csi in enumerate(self.data_selectors):
if not self.as_series:
p = g.new_plot(padding=[50, 5, 5, 50])
p.value_range.tight_bounds = False
p.value_range.margin = 0.1
plotid = i
else:
plotid = 0
try:
x = data[csnames.index(csi.index)]
y = data[csnames.index(csi.value)]
xmin = min(xmin, min(x))
xmax = max(xmax, max(x))
fit = csi.fit if csi.fit != NULL_STR else None
g.new_series(x, y, fit=fit,
filter_outliers=csi.use_filter,
type=csi.plot_type,
plotid=plotid)
g.set_x_title(csi.index, plotid=plotid)
g.set_y_title(csi.value, plotid=plotid)
if fit:
regressable = True
except IndexError:
pass
g.set_x_limits(xmin, xmax, pad='0.1')
self._graph_count += 1
if regressable:
gg = StatsGraph(graph=g)
gii = gg
else:
gii = g
g._update_graph()
def show(gi):
gi.window_title = '{} Graph {}'.format(self.short_name, self._graph_count)
gi.window_x = self._graph_count * 20 + 400
gi.window_y = self._graph_count * 20 + 20
gi.edit_traits()
show(gii)
def _graph_linear_j(self, x, y, r, reg, refresh):
g = self.graph
if not isinstance(g, RegressionGraph):
g = RegressionGraph(
container_dict={'bgcolor': self.plotter_options.bgcolor})
self.graph = g
po = self.plotter_options
g.clear()
plot = g.new_plot(padding=po.get_paddings())
if po.model_kind == WEIGHTED_MEAN_1D:
fit = 'weighted mean'
else:
fit = po.least_squares_fit
_, scatter, line = g.new_series(x=reg.xs,
y=reg.ys,
yerror=reg.yserr,
fit=fit)
ebo = ErrorBarOverlay(component=scatter, orientation='y')
scatter.underlays.append(ebo)
scatter.error_bars = ebo
add_inspector(scatter, self._additional_info)
add_axes_tools(g, plot)
g.set_x_title(po.one_d_axis)
g.set_y_title('J')
g.add_statistics()
miy = 100
may = -1
if self._individual_analyses_enabled:
sel = [
i for i, (a, x, y, e) in enumerate(zip(*self._analyses))
if a.is_omitted()
]
# plot the individual analyses
iscatter, iys = self._graph_individual_analyses(fit=None,
add_tools=False)
miy = min(iys)
may = max(iys)
# set metadata last because it will trigger a refresh
self.suppress_metadata_change = True
iscatter.index.metadata['selections'] = sel
self.suppress_metadata_change = False
g.set_y_limits(min_=miy, max_=may, pad='0.1')
g.set_x_limits(pad='0.1')
g.refresh()
fys = line.value.get_data()
self.max_j = fys.max()
self.min_j = fys.min()
def _graph_grid(self, x, y, z, ze, r, reg, refresh):
self.min_j = min(z)
self.max_j = max(z)
g = self.graph
layout = FigureLayout(fixed='filled_grid')
nrows, ncols = layout.calculate(len(x))
if not isinstance(g, Graph):
g = RegressionGraph(container_dict={
'bgcolor': 'gray',
'kind': 'g',
'shape': (nrows, ncols)
})
self.graph = g
def get_ip(xi, yi):
return next((ip for ip in self.monitor_positions
if ((ip.x - xi)**2 + (ip.y - yi)**2)**0.5 < 0.01),
None)
opt = self.plotter_options
monage = opt.monitor_age * 1e6
lk = opt.lambda_k
ans = self._analyses[0]
scale = opt.flux_scalar
for r in range(nrows):
for c in range(ncols):
idx = c + ncols * r
if refresh:
try:
yy = z[idx] * scale
ye = ze[idx] * scale
except IndexError:
continue
# if hasattr(g, 'rules'):
# if idx in g.rules:
# l1, l2, l3 = g.rules[idx]
# l1.value = yy
# l2.value = yy + ye
# l3.value = yy - ye
else:
plot = g.new_plot(padding_left=65,
padding_right=5,
padding_top=30,
padding_bottom=5)
try:
ip = get_ip(x[idx], y[idx])
except IndexError:
continue
add_axes_tools(g, plot)
yy = z[idx] * scale
ye = ze[idx] * scale
plot.title = 'Identifier={} Position={}'.format(
ip.identifier, ip.hole_id)
plot.x_axis.visible = False
if c == 0 and r == nrows // 2:
plot.y_axis.title = 'J x{}'.format(scale)
if not ip.use:
continue
# get ip via x,y
ais = [
a for a in ans if a.irradiation_position == ip.hole_id
]
n = len(ais)
# plot mean value
# l1 = g.add_horizontal_rule(yy, color='black', line_style='solid', plotid=idx)
# l2 = g.add_horizontal_rule(yy + ye, plotid=idx)
# l3 = g.add_horizontal_rule(yy - ye, plotid=idx)
# rs = (l1, l2, l3)
# d = {idx: rs}
# if hasattr(g, 'rules'):
# g.rules.update(d)
# else:
# g.rules = d
# plot individual analyses
fs = [a.model_j(monage, lk) * scale for a in ais]
fs = sorted(fs)
iys = array([nominal_value(fi) for fi in fs])
ies = array([std_dev(fi) for fi in fs])
if self.plotter_options.use_weighted_fit:
fit = 'weighted mean'
else:
fit = 'average'
ek = self.plotter_options.error_kind
if ek == MSEM:
ek = 'msem'
fit = '{}_{}'.format(fit, ek)
p_, s, l_ = g.new_series(linspace(0, n - 1, n),
iys,
yerror=ies,
type='scatter',
fit=fit,
add_point_inspector=False,
add_inspector=False,
marker='circle',
marker_size=3)
g.set_x_limits(0, n - 1, pad='0.1', plotid=idx)
g.set_y_limits(min(iys - ies),
max(iys + ies),
pad='0.1',
plotid=idx)
g.add_statistics(plotid=idx)
ebo = ErrorBarOverlay(component=s, orientation='y')
s.underlays.append(ebo)
s.error_bars = ebo
add_analysis_inspector(s, ais)
s.index.on_trait_change(
self._grid_update_graph_metadata(ais),
'metadata_changed')
self.suppress_metadata_change = True
sel = [i for i, a in enumerate(ais) if a.is_omitted()]
s.index.metadata['selections'] = sel
self.suppress_metadata_change = False
g.refresh()
#===============================================================================
# Copyright 2011 Jake Ross
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#===============================================================================
from traits.etsconfig.etsconfig import ETSConfig
ETSConfig.toolkit = 'qt4'
from pychron.graph.regression_graph import RegressionGraph
import numpy as np
if __name__ == '__main__':
reg = RegressionGraph()
reg.new_plot()
# xs = np.linspace(0, 100, 100)
# ys = xs * 0.02 + np.random.random(100)
xs = [0, 1, 2, 3, 4, 5]
ys = [0, 3, 5, 6, 7, 5]
reg.new_series(xs, ys, fit='linear')
reg.configure_traits()
def _show_plot(self, data):
cd = dict(padding=5, stack_order='top_to_bottom')
csnames = self.column_names
xmin = np.Inf
xmax = -np.Inf
if self.as_series:
g = RegressionGraph(container_dict=cd)
p = g.new_plot(padding=[50, 5, 5, 50],
xtitle=''
)
p.value_range.tight_bounds = False
p.value_range.margin = 0.1
else:
g = StackedRegressionGraph(container_dict=cd)
regressable = False
# metadata = None
for i, csi in enumerate(self.data_selectors):
if not self.as_series:
p = g.new_plot(padding=[50, 5, 5, 50])
p.value_range.tight_bounds = False
p.value_range.margin = 0.1
plotid = i
else:
plotid = 0
try:
x = data[csnames.index(csi.index)]
y = data[csnames.index(csi.value)]
xmin = min(xmin, min(x))
xmax = max(xmax, max(x))
fit = csi.fit if csi.fit != NULL_STR else None
g.new_series(x, y, fit=fit,
filter_outliers=csi.use_filter,
type=csi.plot_type,
plotid=plotid)
g.set_x_title(csi.index, plotid=plotid)
g.set_y_title(csi.value, plotid=plotid)
if fit:
regressable = True
except IndexError:
pass
g.set_x_limits(xmin, xmax, pad='0.1')
self._graph_count += 1
if regressable:
gg = StatsGraph(graph=g)
gii = gg
else:
gii = g
g._update_graph()
def show(gi):
gi.window_title = '{} Graph {}'.format(self.short_name, self._graph_count)
gi.window_x = self._graph_count * 20 + 400
gi.window_y = self._graph_count * 20 + 20
gi.edit_traits()
show(gii)
def cup_deflection_calibration(self, mass):
self.info('{} deflection calibration'.format(self.reference_detector))
rgraph = RegressionGraph(window_x=100,
window_y=50)
rgraph.new_plot()
rgraph.new_series(yer=[])
root_dir = unique_dir(os.path.join(paths.data_dir, 'magfield'), '{}_def_calibration'.format(self.reference_detector))
# if not os.path.exists(root_dir):
# os.mkdir(root_dir)
dm = self.data_manager
p = os.path.join(root_dir, 'defl_vs_dac.csv')
deflection_frame_key = dm.new_frame(path=p)
dm.write_to_frame(['Deflection (V)', '40{} DAC'.format(self.reference_detector)],
frame_key=deflection_frame_key)
start = self.dc_start
stop = self.dc_stop
width = self.dc_step
nstep = (stop - start) / width + 1
npeak_centers = self.dc_npeak_centers
self.info('Deflection scan parameters start={}, stop={}, stepwidth={}, nstep={}'.format(start, stop, width, nstep))
self.info('Reference detector {}'.format(self.reference_detector))
self.info('Peak centers per step n={}'.format(npeak_centers))
for i, ni in enumerate(np.linspace(start, stop, nstep)):
if not self.isAlive():
break
self.info('Deflection step {} {} (V)'.format(i + 1, ni))
self._detectors[self.reference_detector].deflection = ni
ds = []
for n in range(npeak_centers):
if not self.isAlive():
break
self.info('Peak center ni = {}'.format(n + 1))
p = os.path.join(root_dir, 'peak_scan_{:02d}_{:02d}.csv'.format(int(ni), n))
dm.new_frame(path=p)
dm.write_to_frame(['DAC (V)', 'Intensity (fA)'])
graph = Graph(window_title='Peak Centering',
window_x=175 + i * 25 + n * 5,
window_y=25 + i * 25 + n * 5
)
self.peak_center(graph=graph,
update_mftable=True,
update_pos=False,
center_pos=mass
)
if self.isAlive():
# write scan to file
dm.write_to_frame(list(zip(graph.get_data(), graph.get_data(axis=1))))
if npeak_centers > 1:
if not self.simulation:
time.sleep(1)
if self.peak_center_results:
d = (ni, self.peak_center_results[0][1])
ds.append(self.peak_center_results[0][1])
dm.write_to_frame(list(d), frame_key=deflection_frame_key)
# write the centering results to the centering file
dm.write_to_frame([('#{}'.format(x), y) for x, y in zip(graph.get_data(series=1), graph.get_data(series=1, axis=1))])
if self.peak_center_results:
rgraph.add_datum((ni, np.mean(ds), np.std(ds)))
if i == 2:
invoke_in_main_thread(rgraph.edit_traits)
# delay so we can view graph momonetarily
if not self.simulation and self.isAlive():
time.sleep(2)
self.info('deflection calibration finished')
# ===============================================================================
# Copyright 2011 Jake Ross
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ===============================================================================
from traits.etsconfig.etsconfig import ETSConfig
ETSConfig.toolkit = 'qt4'
from pychron.graph.regression_graph import RegressionGraph
if __name__ == '__main__':
reg = RegressionGraph()
reg.new_plot()
# xs = np.linspace(0, 100, 100)
# ys = xs * 0.02 + np.random.random(100)
xs = [0, 1, 2, 3, 4, 5]
ys = [0, 3, 5, 6, 7, 5]
reg.new_series(xs, ys, fit='linear')
reg.configure_traits()
def _make_correlation(self, idx, ytitle):
fi = self.figures[0]
plots = list(fi.options.get_plotable_aux_plots())
tag = plots[idx].plot_name
n = len(self.figures)
r, c = filled_grid(n)
g = RegressionGraph(container_dict={
'kind': 'g',
'shape': (r, c)
},
window_title='Correlation')
for i, fi in enumerate(self.figures):
gi = fi.analysis_group
p = g.new_plot(xtitle='age',
ytitle=ytitle,
title='{}({})'.format(gi.sample, gi.identifier))
xs = [nominal_value(a.uage) for a in gi.clean_analyses()]
ys = [nominal_value(a.get_value(tag)) for a in gi.clean_analyses()]
g.new_series(xs,
ys,
fit='linear',
use_error_envelope=False,
plotid=i)
g.add_correlation_statistics(plotid=i)
g.set_x_limits(pad='0.1', plotid=i)
g.set_y_limits(pad='0.1', plotid=i)
g.refresh()
open_view(g)
def cup_deflection_calibration(self, mass):
self.info('{} deflection calibration'.format(self.reference_detector))
rgraph = RegressionGraph(window_x=100, window_y=50)
rgraph.new_plot()
rgraph.new_series(yer=[])
root_dir = unique_dir(
os.path.join(paths.data_dir, 'magfield'),
'{}_def_calibration'.format(self.reference_detector))
# if not os.path.exists(root_dir):
# os.mkdir(root_dir)
dm = self.data_manager
p = os.path.join(root_dir, 'defl_vs_dac.csv')
deflection_frame_key = dm.new_frame(path=p)
dm.write_to_frame(
['Deflection (V)', '40{} DAC'.format(self.reference_detector)],
frame_key=deflection_frame_key)
start = self.dc_start
stop = self.dc_stop
width = self.dc_step
nstep = (stop - start) / width + 1
npeak_centers = self.dc_npeak_centers
self.info(
'Deflection scan parameters start={}, stop={}, stepwidth={}, nstep={}'
.format(start, stop, width, nstep))
self.info('Reference detector {}'.format(self.reference_detector))
self.info('Peak centers per step n={}'.format(npeak_centers))
for i, ni in enumerate(np.linspace(start, stop, nstep)):
if not self.isAlive():
break
self.info('Deflection step {} {} (V)'.format(i + 1, ni))
self._detectors[self.reference_detector].deflection = ni
ds = []
for n in range(npeak_centers):
if not self.isAlive():
break
self.info('Peak center ni = {}'.format(n + 1))
p = os.path.join(
root_dir, 'peak_scan_{:02d}_{:02d}.csv'.format(int(ni), n))
dm.new_frame(path=p)
dm.write_to_frame(['DAC (V)', 'Intensity (fA)'])
graph = Graph(window_title='Peak Centering',
window_x=175 + i * 25 + n * 5,
window_y=25 + i * 25 + n * 5)
self.peak_center(graph=graph,
update_mftable=True,
update_pos=False,
center_pos=mass)
if self.isAlive():
# write scan to file
dm.write_to_frame(
list(zip(graph.get_data(), graph.get_data(axis=1))))
if npeak_centers > 1:
if not self.simulation:
time.sleep(1)
if self.peak_center_results:
d = (ni, self.peak_center_results[0][1])
ds.append(self.peak_center_results[0][1])
dm.write_to_frame(list(d),
frame_key=deflection_frame_key)
# write the centering results to the centering file
dm.write_to_frame([
('#{}'.format(x), y)
for x, y in zip(graph.get_data(
series=1), graph.get_data(series=1, axis=1))
])
if self.peak_center_results:
rgraph.add_datum((ni, np.mean(ds), np.std(ds)))
if i == 2:
invoke_in_main_thread(rgraph.edit_traits)
# delay so we can view graph momonetarily
if not self.simulation and self.isAlive():
time.sleep(2)
self.info('deflection calibration finished')
def _make_correlation(self, refplot, xtitle):
fi = self.figures[0]
n = len(list(fi.options.get_plotable_aux_plots()))
plots = list(reversed(fi.graph.plots))
xs = refplot.data.get_data('y1')
r, c = filled_grid(n - 1)
g = RegressionGraph(container_dict={
'kind': 'g',
'shape': (r, c)
},
window_title='Correlation')
i = 0
for pp in plots:
ytitle = pp.y_axis.title
if ytitle == xtitle:
continue
g.new_plot(xtitle=xtitle, ytitle=ytitle, padding=[80, 10, 10, 40])
ys = pp.data.get_data('y1')
g.new_series(xs,
ys,
fit='linear',
use_error_envelope=False,
plotid=i)
g.add_correlation_statistics(plotid=i)
g.set_x_limits(pad='0.1', plotid=i)
g.set_y_limits(pad='0.1', plotid=i)
i += 1
g.refresh()
open_view(g)