def run(self):
self.parent.clear_plots()
if self.data is None:
length, tMin, tMax, fMin, fMax, lMin, lMax, peakF, peakL, peakT = ("",) * 10
else:
length = len(self.data)
tMin = format_time(self.extent.tMin, True)
tMax = format_time(self.extent.tMax, True)
fMin = format_precision(self.settings, freq=self.extent.fMin, fancyUnits=True)
fMax = format_precision(self.settings, freq=self.extent.fMax, fancyUnits=True)
lMin = format_precision(self.settings, level=self.extent.lMin, fancyUnits=True)
lMax = format_precision(self.settings, level=self.extent.lMax, fancyUnits=True)
peak = self.extent.get_peak_flt()
peakF = format_precision(self.settings, freq=peak[0], fancyUnits=True)
peakL = format_precision(self.settings, level=peak[1], fancyUnits=True)
peakT = format_time(peak[2], True)
text = [
["Sweeps", "", length],
["Extents", "", ""],
["", "Start", tMin],
["", "End", tMax],
["", "Min frequency", fMin],
["", "Max frequency", fMax],
["", "Min level", lMin],
["", "Max level", lMax],
["Peak", "", ""],
["", "Level", peakL],
["", "Frequency", peakF],
["", "Time", peakT],
]
table = Table(self.axes, loc="center")
table.set_gid("table")
rows = len(text)
cols = len(text[0])
for row in xrange(rows):
for col in xrange(cols):
table.add_cell(row, col, text=text[row][col], width=1.0 / cols, height=1.0 / rows)
if self.settings.grid:
colour = "LightGray"
else:
colour = "w"
set_table_colour(table, colour)
for i in range(3):
table.auto_set_column_width(i)
self.axes.add_table(table)
self.parent.redraw_plot()
self.parent.threadPlot = None
def main():
a, b, dot_num, additional_length_multiplier_x, additional_length_multiplier_y, plot_style, borders_style, \
solution_style, figsize, methods_number, solution_exact, data, target, epsilon, iter_lim \
= -0.5, 2.0, 1000, 0.0, 0.0, 'k-', 'k--', 'ro', (15.0, 7.5), 5, 0.0, [], "min", 1e-6, 1000
optimization_methods = (nlopt.dichotomy, nlopt.gsection, nlopt.fibonacci, nlopt.tangent, nlopt.parabolic)
optimization_methods_names = ("Метод дихотомии", "Метод золотого сечения", "Метод Фибоначчи", "Метод касательных",
"Метод парабол")
table_head = ("Название\nметода", "Численное\nрешение", "Значение\nцелевой\nфункции",
"Абсолютная\nпогрешность\n(аргумент)", "Абсолютная\nпогрешность\n(функция)")
fmt_a, fmt_b, fmt_solution_exact, fmt_target_value = r"%.", r"%.", r"%.", r"%."
fmt_a += r"%sf" % str(main_digits_num(a, int(-np.log10(epsilon))))
fmt_b += r"%sf" % str(main_digits_num(b, int(-np.log10(epsilon))))
fmt_solution_exact += r"%sf" % str(main_digits_num(solution_exact, int(-np.log10(epsilon))))
fmt_target_value += r"%sf" % str(main_digits_num(f(solution_exact), int(-np.log10(epsilon))))
x = np.linspace(a - additional_length_multiplier_x * (b - a), b + additional_length_multiplier_x * (b - a), dot_num)
y = f(x)
ncols, nrows = len(table_head), methods_number + 1
for i in range(methods_number):
solution_numerical = optimization_methods[i](f, a, b, target=target, epsilon=epsilon, iter_lim=iter_lim)
plt.figure(num=i + 1, figsize=figsize)
plt.title(optimization_methods_names[i])
plt.plot(x, y, plot_style, solution_numerical, f(solution_numerical), solution_style,
[a, a], [np.min(y) - additional_length_multiplier_y * (np.max(y) - np.min(y)),
np.max(y) + additional_length_multiplier_y * (np.max(y) - np.min(y))], borders_style,
[b, b], [np.min(y) - additional_length_multiplier_y * (np.max(y) - np.min(y)),
np.max(y) + additional_length_multiplier_y * (np.max(y) - np.min(y))], borders_style)
plt.grid(True)
data.append([optimization_methods_names[i], solution_numerical, f(solution_numerical),
np.abs(solution_numerical - solution_exact), np.abs(f(solution_numerical) - f(solution_exact))])
fig = plt.figure(num=methods_number + 1, figsize=figsize)
ax = plt.subplot()
ax.axis('off')
tab = Table(ax, bbox=[0, 0, 1, 1])
tab.auto_set_column_width(False)
tab.auto_set_font_size(False)
for j in range(ncols):
tab.add_cell(0, j, figsize[0] / ncols, 0.1, text=table_head[j], loc="center")
for i in range(1, nrows):
for j in range(ncols):
tab.add_cell(i, j, figsize[0] / ncols, 0.1, text=str(data[i - 1][j]), loc="center")
tab.set_fontsize(9.0)
ax.add_table(tab)
plt.title(r"$Задача: f(x) = |x| + 2x^2 \rightarrow %s, x\in[$" % target + fmt_a % a + r"; " + fmt_b % b
+ r"]%sТочное решение:" % "\n\n" + r"$x_{%s}$ = " % target + fmt_solution_exact % solution_exact +
r"; $f(x_{%s})$ = " % target + fmt_target_value % f(solution_exact))
plt.show()
plt.close()
def run(self):
self.parent.clear_plots()
if self.data is None:
length, tMin, tMax, fMin, fMax, lMin, lMax, peakF, peakL, peakT = ('-',) * 10
else:
length = len(self.data)
tMin = format_time(self.extent.tMin, True)
tMax = format_time(self.extent.tMax, True)
fMin = format_precision(self.settings, freq=self.extent.fMin,
fancyUnits=True)
fMax = format_precision(self.settings, freq=self.extent.fMax,
fancyUnits=True)
lMin = format_precision(self.settings, level=self.extent.lMin,
fancyUnits=True)
lMax = format_precision(self.settings, level=self.extent.lMax,
fancyUnits=True)
peak = self.extent.get_peak_flt()
peakF = format_precision(self.settings, freq=peak[0],
fancyUnits=True)
peakL = format_precision(self.settings, level=peak[1],
fancyUnits=True)
peakT = format_time(peak[2], True)
text = [['Sweeps', '', length],
['Extent', '', ''],
['', 'Start', tMin],
['', 'End', tMax],
['', 'Min frequency', fMin],
['', 'Max frequency', fMax],
['', 'Min level', lMin],
['', 'Max level', lMax],
['Peak', '', ''],
['', 'Level', peakL],
['', 'Frequency', peakF],
['', 'Time', peakT],
]
table = Table(self.axes, loc='center')
table.set_gid('table')
rows = len(text)
cols = len(text[0])
fontProperties = FontProperties()
fontProperties.set_weight('semibold')
for row in xrange(rows):
for col in xrange(cols):
fp = fontProperties if col == 0 else None
table.add_cell(row, col,
text=text[row][col],
fontproperties=fp,
width=1.0 / cols, height=1.0 / rows)
if self.settings.grid:
colour = 'LightGray'
else:
colour = 'w'
set_table_colour(table, colour)
for i in range(2):
table.auto_set_column_width(i)
self.axes.add_table(table)
self.parent.redraw_plot()
self.parent.threadPlot = None
def plot(self, experiment, plot_name = None, **kwargs):
"""Plot a table"""
if experiment is None:
raise util.CytoflowViewError('experiment', "No experiment specified")
if self.statistic not in experiment.statistics:
raise util.CytoflowViewError('statistic',
"Can't find the statistic {} in the experiment"
.format(self.statistic))
else:
stat = experiment.statistics[self.statistic]
data = pd.DataFrame(index = stat.index)
data[stat.name] = stat
if self.subset:
try:
data = data.query(self.subset)
except Exception as e:
raise util.CytoflowViewError('subset',
"Subset string '{0}' isn't valid"
.format(self.subset)) from e
if len(data) == 0:
raise util.CytoflowViewError('subset',
"Subset string '{0}' returned no values"
.format(self.subset))
names = list(data.index.names)
for name in names:
unique_values = data.index.get_level_values(name).unique()
if len(unique_values) == 1:
warn("Only one value for level {}; dropping it.".format(name),
util.CytoflowViewWarning)
try:
data.index = data.index.droplevel(name)
except AttributeError as e:
raise util.CytoflowViewError(None,
"Must have more than one "
"value to plot.") from e
if not (self.row_facet or self.column_facet):
raise util.CytoflowViewError('row_facet',
"Must set at least one of row_facet "
"or column_facet")
if self.subrow_facet and not self.row_facet:
raise util.CytoflowViewError('subrow_facet',
"Must set row_facet before using "
"subrow_facet")
if self.subcolumn_facet and not self.column_facet:
raise util.CytoflowViewError('subcolumn_facet',
"Must set column_facet before using "
"subcolumn_facet")
if self.row_facet and self.row_facet not in experiment.conditions:
raise util.CytoflowViewError('row_facet',
"Row facet {} not in the experiment, "
"must be one of {}"
.format(self.row_facet, experiment.conditions))
if self.row_facet and self.row_facet not in data.index.names:
raise util.CytoflowViewError('row_facet',
"Row facet {} not a statistic index; "
"must be one of {}"
.format(self.row_facet, data.index.names))
if self.subrow_facet and self.subrow_facet not in experiment.conditions:
raise util.CytoflowViewError('subrow_facet',
"Subrow facet {} not in the experiment, "
"must be one of {}"
.format(self.subrow_facet, experiment.conditions))
if self.subrow_facet and self.subrow_facet not in data.index.names:
raise util.CytoflowViewError('subrow_facet',
"Subrow facet {} not a statistic index; "
"must be one of {}"
.format(self.subrow_facet, data.index.names))
if self.column_facet and self.column_facet not in experiment.conditions:
raise util.CytoflowViewError('column_facet',
"Column facet {} not in the experiment, "
"must be one of {}"
.format(self.column_facet, experiment.conditions))
if self.column_facet and self.column_facet not in data.index.names:
raise util.CytoflowViewError('column_facet',
"Column facet {} not a statistic index; "
"must be one of {}"
.format(self.column_facet, data.index.names))
if self.subcolumn_facet and self.subcolumn_facet not in experiment.conditions:
raise util.CytoflowViewError('subcolumn_facet',
"Subcolumn facet {} not in the experiment, "
"must be one of {}"
.format(self.subcolumn_facet, experiment.conditions))
if self.subcolumn_facet and self.subcolumn_facet not in data.index.names:
raise util.CytoflowViewError('subcolumn_facet',
"Subcolumn facet {} not a statistic index; "
"must be one of {}"
.format(self.subcolumn_facet, data.index.names))
facets = [x for x in [self.row_facet, self.subrow_facet,
self.column_facet, self.subcolumn_facet] if x]
if len(facets) != len(set(facets)):
raise util.CytoflowViewError(None,
"Can't reuse facets")
if set(facets) != set(data.index.names):
raise util.CytoflowViewError(None,
"Must use all the statistic indices as variables or facets: {}"
.format(data.index.names))
row_groups = data.index.get_level_values(self.row_facet).unique() \
if self.row_facet else [None]
subrow_groups = data.index.get_level_values(self.subrow_facet).unique() \
if self.subrow_facet else [None]
col_groups = data.index.get_level_values(self.column_facet).unique() \
if self.column_facet else [None]
subcol_groups = data.index.get_level_values(self.subcolumn_facet).unique() \
if self.subcolumn_facet else [None]
row_offset = (self.column_facet != "") + (self.subcolumn_facet != "")
col_offset = (self.row_facet != "") + (self.subrow_facet != "")
num_cols = len(col_groups) * len(subcol_groups) + col_offset
fig = plt.figure()
ax = fig.add_subplot(111)
# hide the plot axes that matplotlib tries to make
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
for sp in ax.spines.values():
sp.set_color('w')
sp.set_zorder(0)
loc = 'upper left'
bbox = None
t = Table(ax, loc, bbox, **kwargs)
t.auto_set_font_size(False)
for c in range(num_cols):
t.auto_set_column_width(c)
width = [0.2] * num_cols
height = t._approx_text_height() * 1.8
# make the main table
for (ri, r) in enumerate(row_groups):
for (rri, rr) in enumerate(subrow_groups):
for (ci, c) in enumerate(col_groups):
for (cci, cc) in enumerate(subcol_groups):
row_idx = ri * len(subrow_groups) + rri + row_offset
col_idx = ci * len(subcol_groups) + cci + col_offset
# this is not pythonic, but i'm tired
agg_idx = []
for data_idx in data.index.names:
if data_idx == self.row_facet:
agg_idx.append(r)
elif data_idx == self.subrow_facet:
agg_idx.append(rr)
elif data_idx == self.column_facet:
agg_idx.append(c)
elif data_idx == self.subcolumn_facet:
agg_idx.append(cc)
agg_idx = tuple(agg_idx)
if len(agg_idx) == 1:
agg_idx = agg_idx[0]
try:
text = "{:g}".format(data.loc[agg_idx][stat.name])
except ValueError:
text = data.loc[agg_idx][stat.name]
t.add_cell(row_idx,
col_idx,
width = width[col_idx],
height = height,
text = text)
# row headers
if self.row_facet:
for (ri, r) in enumerate(row_groups):
row_idx = ri * len(subrow_groups) + row_offset
try:
text = "{0} = {1:g}".format(self.row_facet, r)
except ValueError:
text = "{0} = {1}".format(self.row_facet, r)
t.add_cell(row_idx,
0,
width = width[0],
height = height,
text = text)
# subrow headers
if self.subrow_facet:
for (ri, r) in enumerate(row_groups):
for (rri, rr) in enumerate(subrow_groups):
row_idx = ri * len(subrow_groups) + rri + row_offset
try:
text = "{0} = {1:g}".format(self.subrow_facet, rr)
except ValueError:
text = "{0} = {1}".format(self.subrow_facet, rr)
t.add_cell(row_idx,
1,
width = width[1],
height = height,
text = text)
# column headers
if self.column_facet:
for (ci, c) in enumerate(col_groups):
col_idx = ci * len(subcol_groups) + col_offset
try:
text = "{0} = {1:g}".format(self.column_facet, c)
except ValueError:
text = "{0} = {1}".format(self.column_facet, c)
t.add_cell(0,
col_idx,
width = width[col_idx],
height = height,
text = text)
# subcolumn headers
if self.subcolumn_facet:
for (ci, c) in enumerate(col_groups):
for (cci, cc) in enumerate(subcol_groups):
col_idx = ci * len(subcol_groups) + cci + col_offset
try:
text = "{0} = {1:g}".format(self.subcolumn_facet, cc)
except ValueError:
text = "{0} = {1}".format(self.subcolumn_facet, cc)
t.add_cell(1,
col_idx,
width = width[col_idx],
height = height,
text = text)
ax.add_table(t)
def run(self):
self.parent.clear_plots()
if self.data is None:
length, tMin, tMax, fMin, fMax, lMin, lMax, peakF, peakL, peakT = (
'', ) * 10
else:
length = len(self.data)
tMin = format_time(self.extent.tMin, True)
tMax = format_time(self.extent.tMax, True)
fMin = format_precision(self.settings,
freq=self.extent.fMin,
fancyUnits=True)
fMax = format_precision(self.settings,
freq=self.extent.fMax,
fancyUnits=True)
lMin = format_precision(self.settings,
level=self.extent.lMin,
fancyUnits=True)
lMax = format_precision(self.settings,
level=self.extent.lMax,
fancyUnits=True)
peak = self.extent.get_peak_flt()
peakF = format_precision(self.settings,
freq=peak[0],
fancyUnits=True)
peakL = format_precision(self.settings,
level=peak[1],
fancyUnits=True)
peakT = format_time(peak[2], True)
text = [
['Sweeps', '', length],
['Extents', '', ''],
['', 'Start', tMin],
['', 'End', tMax],
['', 'Min frequency', fMin],
['', 'Max frequency', fMax],
['', 'Min level', lMin],
['', 'Max level', lMax],
['Peak', '', ''],
['', 'Level', peakL],
['', 'Frequency', peakF],
['', 'Time', peakT],
]
table = Table(self.axes, loc='center')
table.set_gid('table')
rows = len(text)
cols = len(text[0])
for row in xrange(rows):
for col in xrange(cols):
table.add_cell(row,
col,
text=text[row][col],
width=1.0 / cols,
height=1.0 / rows)
if self.settings.grid:
colour = 'LightGray'
else:
colour = 'w'
set_table_colour(table, colour)
for i in range(3):
table.auto_set_column_width(i)
self.axes.add_table(table)
self.parent.redraw_plot()
self.parent.threadPlot = None
def run(self):
self.parent.clear_plots()
if self.data is None:
self.parent.threadPlot = None
return
tMin = format_time(self.extent.tMin, True)
tMax = format_time(self.extent.tMax, True)
fMin = format_precision(self.settings, freq=self.extent.fMin,
fancyUnits=True)
fMax = format_precision(self.settings, freq=self.extent.fMax,
fancyUnits=True)
lMin = format_precision(self.settings, level=self.extent.lMin,
fancyUnits=True)
lMax = format_precision(self.settings, level=self.extent.lMax,
fancyUnits=True)
peak = self.extent.get_peak_flt()
peakF = format_precision(self.settings, freq=peak[0],
fancyUnits=True)
peakL = format_precision(self.settings, level=peak[1],
fancyUnits=True)
peakT = format_time(peak[2], True)
text = [['Sweeps', '', len(self.data)],
['Extents', '', ''],
['', 'Start', tMin],
['', 'End', tMax],
['', 'Min frequency', fMin],
['', 'Max frequency', fMax],
['', 'Min level', lMin],
['', 'Max level', lMax],
['Peak', '', ''],
['', 'Level', peakL],
['', 'Frequency', peakF],
['', 'Time', peakT],
]
table = Table(self.axes, loc='center', gid='table')
rows = len(text)
cols = len(text[0])
for row in xrange(rows):
for col in xrange(cols):
table.add_cell(row, col,
text=text[row][col],
width=1.0 / cols, height=1.0 / rows)
if self.settings.grid:
colour = 'LightGray'
else:
colour = 'w'
set_table_colour(table, colour)
for i in range(3):
table.auto_set_column_width(i)
self.axes.add_table(table)
noData = find_artists(self.axes, 'noData')
noData[0].set_alpha(0)
self.parent.redraw_plot()
self.parent.threadPlot = None
def main():
a, b, a_lst, b_lst, dot_num, additional_length_multiplier_x, additional_length_multiplier_y, plot_style, \
borders_style, solution_style, a_style, b_style, figsize, methods_number, solution_exact, data, target, epsilon, iter_lim \
= -1.5, 0.0, [], [], 1000, 0.0, 0.0, 'k-', 'k--', 'ko', '<k', 'k>', (15.0, 7.5), 3, -1.0, [], "min", 1e-3, 1000
optimization_methods = (nlopt.dichotomy, nlopt.gsection, nlopt.fibonacci,
nlopt.tangent, nlopt.parabolic)
optimization_methods_names = (u"Метод дихотомії",
u"Метод золотого перерізу",
u"Метод Фібоначчи", u"Метод дотичних",
u"Метод парабол")
table_head = (u"Назва\nметоду", u"Чисельний\nрозв'язок",
u"Значення\nцільової\nфункції",
u"Абсолютна\nпохибка\n(аргумент)",
u"Абсолютна\nпохибка\n(функція)")
fmt_a, fmt_b, fmt_solution_exact, fmt_target_value = r"%.", r"%.", r"%.", r"%."
fmt_a += r"%sf" % str(main_digits_num(a, int(-np.log10(epsilon))))
fmt_b += r"%sf" % str(main_digits_num(b, int(-np.log10(epsilon))))
fmt_solution_exact += r"%sf" % str(
main_digits_num(solution_exact, int(-np.log10(epsilon))))
fmt_target_value += r"%sf" % str(
main_digits_num(f(solution_exact), int(-np.log10(epsilon))))
x = np.linspace(a - additional_length_multiplier_x * (b - a),
b + additional_length_multiplier_x * (b - a), dot_num)
y = f(x)
ncols, nrows = len(table_head), methods_number + 1
for i in range(methods_number):
a_lst.append([])
b_lst.append([])
solution_numerical = optimization_methods[i](f,
a,
b,
a_lst[i],
b_lst[i],
target=target,
epsilon=epsilon,
iter_lim=iter_lim)
plt.figure(num=i + 1, figsize=figsize)
plt.title(optimization_methods_names[i])
plt.plot(x, y, plot_style, [a, a], [
np.min(y) - additional_length_multiplier_y *
(np.max(y) - np.min(y)),
np.max(y) + additional_length_multiplier_y *
(np.max(y) - np.min(y))
], borders_style, [b, b], [
np.min(y) - additional_length_multiplier_y *
(np.max(y) - np.min(y)),
np.max(y) + additional_length_multiplier_y *
(np.max(y) - np.min(y))
], borders_style)
# plt.plot(np.array(a_lst[i]), np.zeros_like(a_lst[i]) + np.min(y) - additional_length_multiplier_y *
# (np.max(y) - np.min(y)), a_style, alpha=0.5, label=r'$a_k$')
# plt.plot(np.array(b_lst[i]), np.zeros_like(b_lst[i]) + np.min(y) - additional_length_multiplier_y *
# (np.max(y) - np.min(y)), b_style, alpha=0.5, label=r'$b_k$')
plt.plot(solution_numerical,
f(solution_numerical),
solution_style,
label=u'Чисельний розв\'язок')
plt.legend(loc='best',
shadow=False,
fontsize='x-large',
facecolor='black',
framealpha=0.15)
plt.grid(True)
data.append([
optimization_methods_names[i], solution_numerical,
f(solution_numerical),
np.abs(solution_numerical - solution_exact),
np.abs(f(solution_numerical) - f(solution_exact))
])
plt.figure(num=methods_number + 1, figsize=figsize)
ax = plt.subplot()
ax.axis('off')
tab = Table(ax, bbox=[0, 0, 1, 1])
tab.auto_set_column_width(False)
tab.auto_set_font_size(False)
for j in range(ncols):
tab.add_cell(0,
j,
figsize[0] / ncols,
0.1,
text=table_head[j],
loc="center")
for i in range(1, nrows):
for j in range(ncols):
tab.add_cell(i,
j,
figsize[0] / ncols,
0.1,
text=str(data[i - 1][j]),
loc="center")
tab.set_fontsize(9.0)
ax.add_table(tab)
plt.title(r"$Задача: f(x) = \frac{x}{x^2 + 1} \rightarrow %s, x\in[$" %
target + fmt_a % a + r"; " + fmt_b % b +
r"]%sТочное решение:" % "\n\n" + r"$x_{%s}$ = " % target +
fmt_solution_exact % solution_exact +
r"; $f(x_{%s})$ = " % target +
fmt_target_value % f(solution_exact))
plt.show()
plt.close()
print("a:")
for arr in a_lst:
for a in arr:
print("%.4f" % a)
print("---------------")
print("b:")
for arr in b_lst:
for b in arr:
print("%.4f" % b)
print("---------------")
print("f(a):")
for arr in a_lst:
for a in arr:
print("%.4f" % f(a))
print("---------------")
print("f(b):")
for arr in b_lst:
for b in arr:
print("%.4f" % f(b))
print("---------------")
def plot(self, experiment, plot_name=None, **kwargs):
"""Plot a table of the conditions, filenames, and number of events"""
if experiment is None:
raise util.CytoflowViewError('experiment',
"No experiment specified")
if self.subset:
try:
experiment = experiment.query(self.subset)
except Exception as e:
raise util.CytoflowViewError(
'subset', "Subset string '{0}' isn't valid".format(
self.subset)) from e
if len(experiment) == 0:
raise util.CytoflowViewError(
'subset', "Subset string '{0}' returned no values".format(
self.subset))
# if path is set, actually do an export. this isn't terribly elegant,
# but this is the only place we have an experiment! ExportFCS.export()
# raises CytoflowViewErrors, so it should be compatible with the
# existing error reporting stuff.
if self.path:
self.export(experiment)
return
# otherwise, make a table showing what will be exported
num_cols = len(self.by) + 2
fig = plt.figure()
ax = fig.add_subplot(111)
# hide the plot axes that matplotlib tries to make
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
for sp in ax.spines.values():
sp.set_color('w')
sp.set_zorder(0)
loc = 'upper left'
bbox = None
t = Table(ax, loc, bbox)
t.auto_set_font_size(False)
for c in range(num_cols):
t.auto_set_column_width(c)
width = [0.2] * num_cols
height = t._approx_text_height() * 1.8
t.add_cell(0, 0, width=width[0], height=height, text="#")
for ci, c in enumerate(self.by):
ci = ci + 1
t.add_cell(0, ci, width=width[ci], height=height, text=c)
ci = len(self.by) + 1
t.add_cell(0, ci, width=width[ci], height=height, text="Filename")
# ci = len(self.by) + 2
# t.add_cell(0, ci, width = width[ci], height = height, text = "Events")
for ri, row in enumerate(self.enum_conditions_and_files(experiment)):
t.add_cell(ri + 1,
0,
width=width[0],
height=height,
text="{:g}".format(ri + 1))
for ci, col in enumerate(row):
t.add_cell(ri + 1,
ci + 1,
width=width[ci + 1],
height=height,
text=col)
ax.add_table(t)