def add_table(length, quality, views, pages, dates, lang, ax: Axes) -> Table:
"""Add a table to figure aunder the plot to display values from all input files"""
table_height = 4 if lang == 'sv' else 3
table_width = len(length)
table_text = [[f"{x:,}".replace(',', ' ')
for x in length[:-3]] + length[-3:],
[f"{x:,}".replace(',', ' ')
for x in views[:-3]] + views[-3:],
[f"{x:,}".replace(',', ' ')
for x in pages[:-3]] + pages[-3:]]
row_labels = ['Length', 'Views', 'Pages']
if lang == 'sv':
table_text.insert(1,
[f"{x:,}".replace(',', ' ')
for x in quality[:-3]] + quality[-3:])
row_labels.insert(1, 'Quality')
data_table = table(ax,
cellText=table_text,
rowLabels=row_labels,
colLabels=[d.strftime('%-d %b %Y') for d in dates] +
[' Change ', 'Change in %', 'Average / page'],
bbox=[0, -0.7, 0.9, 0.5],
loc='bottom')
data_table[table_height, len(length) - 1].get_text().set_text('-')
for col in range(table_width):
data_table[0, col].set_lw(3.0)
for row in range(1, table_height + 1):
data_table[row, -1].set_lw(3.0)
data_table.auto_set_column_width(range(table_width))
for row in range(1, table_height + 1):
col_nr = table_width - 3
cell_text = data_table[row, col_nr].get_text()
if '+' in cell_text.get_text():
data_table[row, col_nr].set_facecolor('lightgreen')
data_table[row, col_nr + 1].set_facecolor('lightgreen')
data_table[row, col_nr].set_fill(True)
data_table[row, col_nr + 1].set_fill(True)
elif '-' in cell_text.get_text():
data_table[row, col_nr].set_facecolor('lightcoral')
data_table[row, col_nr + 1].set_facecolor('lightcoral')
data_table[row, col_nr].set_fill(True)
data_table[row, col_nr + 1].set_fill(True)
return data_table
def plot_aux_data(soln, config, log, scale):
"""
Plot auxiliary data such as energy distribution and receiver functions.
:param soln: Solution container
:type soln: Customized scipy.optimize.OptimizeResult
:param config: Solution configuration
:type config: dict
:param log: Logging instance
:type log: logging.Logger
:param scale: Overall image scaling factor
:type scale: float
:return: Matplotlib figure containing the plotted data
:rtype: matplotlib.figure.Figure
"""
f = plt.figure(constrained_layout=False,
figsize=(6.4 * scale, 6.4 * scale))
f.suptitle(config["station_id"], y=0.96, fontsize=16)
gs = f.add_gridspec(2,
1,
left=0.1,
right=0.9,
bottom=0.1,
top=0.87,
hspace=0.3,
wspace=0.3,
height_ratios=[1, 2])
gs_top = gs[0].subgridspec(1, 2)
ax0 = f.add_subplot(gs_top[0, 0])
ax1 = f.add_subplot(gs_top[0, 1])
hist_alpha = 0.5
soln_alpha = 0.3
axis_font_size = 6 * scale
title_font_size = 6 * scale
nbins = 100
# Plot energy distribution of samples and solution clusters
energy_hist, bins = np.histogram(soln.sample_funvals, bins=nbins)
energy_hist = energy_hist.astype(float) / np.max(energy_hist)
ax0.bar(bins[:-1],
energy_hist,
width=np.diff(bins),
align='edge',
color='#808080',
alpha=hist_alpha)
for i, cluster_energies in enumerate(soln.cluster_funvals):
color = 'C' + str(i)
cluster_hist, _ = np.histogram(cluster_energies, bins)
cluster_hist = cluster_hist.astype(float) / np.max(cluster_hist)
ax0.bar(bins[:-1],
cluster_hist,
width=np.diff(bins),
align='edge',
color=color,
alpha=soln_alpha)
# end for
ax0.set_title(
'Energy distribution of random samples and solution clusters',
fontsize=title_font_size)
ax0.set_xlabel('$E_{SU}$ energy (arb. units)')
ax0.set_ylabel('Normalized counts')
ax0.tick_params(labelsize=axis_font_size)
ax0.xaxis.label.set_size(axis_font_size)
ax0.yaxis.label.set_size(axis_font_size)
# Plot sorted per-event upwards S-wave energy at top of mantle per solution.
# Collect event IDs of worst fit traces and present as table of waveform IDs.
event_ids = config["event_ids"]
events_best3 = []
events_worst3 = []
for i, esu in enumerate(soln.esu):
assert len(esu) == len(event_ids)
color = 'C' + str(i)
esu_sorted = sorted(zip(esu, event_ids))
events_best3.extend(esu_sorted[:3])
events_worst3.extend(esu_sorted[-3:])
esu_sorted = [e[0] for e in esu_sorted]
ax1.plot(esu_sorted, color=color, alpha=soln_alpha)
# end for
events_best3 = sorted(events_best3)
events_worst3 = sorted(events_worst3, reverse=True)
best_events_set = set()
worst_events_set = set()
for _, evid in events_best3:
best_events_set.add(evid)
if len(best_events_set) >= 3:
break
# end if
# end for
for _, evid in events_worst3:
worst_events_set.add(evid)
if len(worst_events_set) >= 3:
break
# end if
# end for
_tab1 = table(ax1,
cellText=[[e] for e in best_events_set],
colLabels=['BEST'],
cellLoc='left',
colWidths=[0.35],
loc='upper left',
edges='horizontal',
fontsize=8,
alpha=0.6) # alpha broken in matplotlib.table!
_tab2 = table(ax1,
cellText=[[e] for e in worst_events_set],
colLabels=['WORST'],
cellLoc='left',
colWidths=[0.35],
loc='upper right',
edges='horizontal',
fontsize=8,
alpha=0.6)
ax1.set_title('Ranked per-event energy for each solution point',
fontsize=title_font_size)
ax1.set_xlabel('Rank (out of # source events)')
ax1.set_ylabel('Event $E_{SU}$ energy (arb. units)')
ax1.tick_params(labelsize=axis_font_size)
ax1.xaxis.label.set_size(axis_font_size)
ax1.yaxis.label.set_size(axis_font_size)
# Plot receiver function at base of selected layers
axis_font_size = 6 * scale
max_solutions = config["solver"].get("max_solutions", 3)
for layer in config["layers"]:
lname = layer["name"]
if soln.subsurface and lname in soln.subsurface:
base_seismogms = soln.subsurface[lname]
# Generate RF and plot.
gs_bot = gs[1].subgridspec(max_solutions, 1, hspace=0.4)
for i, seismogm in enumerate(base_seismogms):
soln_rf = _compute_rf(seismogm, config, log)
assert isinstance(soln_rf, rf.RFStream)
# Remove any traces for which deconvolution failed.
# First, find their unique ID. Then remove all traces with that ID.
exclude_ids = set(
[tr.stats.event_id for tr in soln_rf if len(tr) == 0])
soln_rf = rf.RFStream([
tr for tr in soln_rf
if tr.stats.event_id not in exclude_ids
])
axn = f.add_subplot(gs_bot[i])
if soln_rf:
color = 'C' + str(i)
rf_R = soln_rf.select(component='R').trim2(
RF_TRIM_WINDOW[0], RF_TRIM_WINDOW[1], reftime='onset')
num_RFs = len(rf_R)
times = rf_R[0].times() + RF_TRIM_WINDOW[0]
data = rf_R.stack()[0].data
axn.plot(times,
data,
color=color,
alpha=soln_alpha,
linewidth=2)
axn.text(0.95,
0.95,
'N = {}'.format(num_RFs),
fontsize=10,
ha='right',
va='top',
transform=axn.transAxes)
axn.set_xlabel('Time (sec)')
axn.grid(color='#80808080', linestyle=':')
else:
axn.annotate('Empty RF plot', (0.5, 0.5),
xycoords='axes fraction',
ha='center')
# end if
axn.set_title(' '.join([
config["station_id"], lname, 'base RF',
'(soln {})'.format(i)
]),
fontsize=title_font_size,
y=0.92,
va='top')
axn.tick_params(labelsize=axis_font_size)
axn.xaxis.label.set_size(axis_font_size)
axn.yaxis.label.set_size(axis_font_size)
# end for
break # TODO: Figure out how to add more layers if needed
# end if
# end for
return f
"""
Ref:
https://stackoverflow.com/questions/35634238/how-to-save-a-pandas-dataframe-table-as-a-png/35715029
https://stackoverflow.com/questions/26678467/export-a-pandas-dataframe-as-a-table-image/26681726
https://stackoverflow.com/questions/35634238/how-to-save-a-pandas-dataframe-table-as-a-png
"""
import matplotlib
matplotlib.use('Agg')
from matplotlib.pyplot import figure
from matplotlib.table import table
from pylab import *
fig = figure()
colLabels = ('Freeze', 'Wind', 'Flood', 'Quake', 'Hail')
rowLabels = ['%d year' % x for x in (100, 50, 20, 10, 5)]
cellText = [['66.4', '174.3', '75.1', '577.9', '32.0'], ['124.6', '555.4', '153.2', '677.2', '192.5'], ['213.8', '636.0', '305.7', '1175.2', '796.0'], ['292.2', '717.8', '456.4', '1368.5', '865.6'], ['431.5', '1049.4', '799.6', '2149.8', '917.9']]
#table(cellText=cellText, colLabels=colLabels)
ax = subplot(111, frame_on=False)
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
table(cellText=cellText, colLabels=colLabels)
fig.savefig('test12.png')
def plot_tables_planning_time_curve(self, res, res_1, simple, horizon, x,
y, hue, execs):
print("----- ", y)
data = res[['id', x, y, hue]]
if not self.compare_3T:
if len(data[x].unique()) < 8:
fig = plt.figure(figsize=(max(len(data[x].unique()), 8) - 3,
7))
else:
fig = plt.figure(figsize=(max(len(data[x].unique()), 8) - 4,
7))
else:
fig = plt.figure(figsize=(max(len(data[x].unique()), 8), 8))
ax = plt.gca()
# sns.set_context("paper", rc={"font.size":16,"axes.titlesize":16,"axes.labelsize":16})
if horizon == min(self.horizons) or self.compare_3T:
ax = sns.lineplot(x=x,
y=y,
hue=hue,
style="algorithm",
markers=True,
data=data)
plt.setp(ax.get_legend().get_texts(),
fontsize='22') # for legend text
plt.setp(ax.get_legend().get_title(),
fontsize='22') # for legend title
else:
ax = sns.lineplot(x=x,
y=y,
hue=hue,
style="algorithm",
markers=True,
data=data,
legend=False) #, dashes=False
# if horizon != 2:
# ax._legend.remove()
# plt.annotate('actual group', xy=(x+0.2,y), xytext=(x+0.3, 300), arrowprops=dict(facecolor='black', shrink=0.05, headwidth=20, width=7))
# tab_names = data[x].unique()
# alg_names = data[hue].unique()
# for alg in range(len(alg_names)):
# dat_temp = data.loc[data[hue] == alg_names[alg]]
# mean_x = np.mean(dat_temp.loc[dat_temp[x] == tab_names[len(tab_names)-alg-1]])
# pos = (tab_names[len(tab_names)-alg-1],mean_x[y])
# set_trace()
# plt.annotate(alg_names[alg][0], xy=(pos[0], pos[1]), xytext=(pos[0]+0.5, pos[1]+0.5), arrowprops=dict(facecolor='black', shrink=0.05),)
if y != "final_total_belief_reward" and res_1 is not None and not res_1.empty:
data_1 = res_1[[hue, x, y]]
# table(ax, data_1, loc='upper left')
# set_trace()
# set_trace()
tab_names = data_1[x].unique()
alg_names = data_1[hue].unique()
table_arr = np.full((len(tab_names) + 1, len(alg_names) + 1),
"",
dtype=object)
# print ("algorithms: ", alg_names)
# print ("tables: ", tab_names)
for t in range(len(tab_names)):
table_arr[t + 1, 0] = "tables = " + str(tab_names[t])
for alg in range(len(alg_names)):
table_arr[0, alg + 1] = alg_names[alg][0]
sample = data_1.loc[data_1[hue] == alg_names[alg]]
sample = sample.loc[sample[x] == tab_names[t]]
# print(sample)
if not sample.empty:
table_arr[t + 1, alg +
1] = str(np.round(float(sample[y]), 1)) + "s"
if not self.compare_3T or simple == 1:
if horizon == 2 or (simple == 1 and horizon == 5):
tab = table(ax,
cellText=table_arr,
cellLoc='center',
rowLoc='center',
loc='upper left',
bbox=[0.1, .2, .7,
.3]) ## left, bottom, width, height
else:
if len(data[x].unique()) < 10:
tab = table(ax,
cellText=table_arr,
cellLoc='center',
rowLoc='center',
loc='upper left',
bbox=[0.0, .7, 1.0,
.3]) ## left, bottom, width, height
else:
tab = table(ax,
cellText=table_arr,
cellLoc='center',
rowLoc='center',
loc='upper left',
bbox=[0.0, .7, 1.0,
.3]) ## left, bottom, width, height
else:
tab = table(ax,
cellText=table_arr,
cellLoc='center',
rowLoc='center',
loc='upper left',
bbox=[0.1, .4, .9,
.2]) ## left, bottom, width, height
tab.auto_set_font_size(False)
tab.set_fontsize(24)
# tab.scale(5, 5)
ax.set_title("horizon: " + str(horizon), fontsize=26)
# set_trace()
matplt.xticks(
np.arange(self.tables[0], max(max(data[x].unique()) + 1, 9)))
if "time" in y:
ylab = "planning time (s)"
if "reward" in y:
ylab = "reward"
ax.set_xlabel(x, fontsize=26)
ax.set_ylabel(ylab, fontsize=26)
ax.tick_params(labelsize=22)
agent_pomdp_max_table = data.loc[data[hue] == "B:Agent POMDP"]
ax.get_xticklabels()[len(agent_pomdp_max_table[x].unique()) -
1].set_color("orange")
# ax.tick_params(direction='out', length=6, width=2, colors='r', grid_color='r', grid_alpha=0.5)
plt.tight_layout()
# set_trace()
matplt.savefig(self.POMDPTasks.test_folder + '/results_' + str(execs) + '/' + "3T_" + str(self.compare_3T) + "_" + x + "_" + hue + "_" + y + '_simple-' \
+ str(simple) + '_horizon-' + str(horizon) + "_execs-" + str(execs) + '.png')
r'\# model runs'
]
rowLoc = 'right'
# cell_text = list(cols.flatten().astype(str))
cols = np.append(cols, cols.sum(axis=1).reshape((-1, 1)), axis=1)
cell_text = cols.astype(str)
cell_text[0, -1] = 'Total'
cell_text = cell_text[[0, 2, 3], :]
est1_ms.model_runs = int(cell_text[-1, -1])
table = tbl.table(ax[1],
cellText=cell_text,
cellLoc=cellLoc,
cellColours=colors,
colWidths=colWidths,
rowLabels=rowLabels,
rowLoc=rowLoc,
fontsize=(config.TICK_FONTSIZE - 2) * config.SCALE,
loc='center')
table.scale(1, 3)
for cell in table._cells:
table._cells[cell].set_alpha(0.1)
ax[1].add_table(table)
plt.subplots_adjust(hspace=0.4)
# print(cols)
ax[0] = fp.add_title(ax[0],
def plt_figure(directory, trace, peaks_vals, spacer, pos_in_call, filename,
pos_list, base_pos_in_spacer, strand):
# Plot the trace and table showing % of each base along the spacer
fig, (ax1, ax2) = plt.subplots(2, figsize=(4, 2.5))
fig.patch.set_visible(False)
ax1 = plt.subplot2grid((2, 1), (0, 0))
ax2 = plt.subplot2grid((2, 1), (1, 0))
Arialfont = {'fontname': 'Arial'}
ax1.plot(trace['G'][peaks_vals[pos_in_call] -
8:peaks_vals[pos_in_call + len(spacer) - 1] + 8],
color='black',
label='G',
linewidth=0.6)
ax1.plot(trace['A'][peaks_vals[pos_in_call] -
8:peaks_vals[pos_in_call + len(spacer) - 1] + 8],
color='g',
label='A',
linewidth=0.6) # A
ax1.plot(trace['T'][peaks_vals[pos_in_call] -
8:peaks_vals[pos_in_call + len(spacer) - 1] + 8],
color='r',
label='T',
linewidth=0.6) # T
ax1.plot(trace['C'][peaks_vals[pos_in_call] -
8:peaks_vals[pos_in_call + len(spacer) - 1] + 8],
color='b',
label='C',
linewidth=0.6) # C
ax1.annotate('G',
xy=(10, 60),
xycoords='axes points',
size=5,
color='black',
weight='bold',
ha='right',
va='top')
ax1.annotate('A',
xy=(14, 60),
xycoords='axes points',
size=5,
color='g',
weight='bold',
ha='right',
va='top')
ax1.annotate('T',
xy=(18, 60),
xycoords='axes points',
size=5,
color='r',
weight='bold',
ha='right',
va='top')
ax1.annotate('C',
xy=(22, 60),
xycoords='axes points',
size=5,
color='b',
weight='bold',
ha='right',
va='top')
ax1.axis('off')
ax1.axis('tight')
ax1.set_title(filename.split(".")[0], fontsize=8, **Arialfont)
# Put a legend to the right of the current axis
# plt.legend([a_plot, c_plot, g_plot, t_plot], ['A', 'C', 'G', 'T'], bbox_to_anchor=(0.98,0.5), loc="center left", prop={'size': 6}, borderaxespad=0., ncol=1)
ax2.axis('off')
ax2.axis('tight')
df = pd.DataFrame(pos_list, columns=list(spacer))
xx = (df.values * 2.56).astype(int)
c = mcolors.ColorConverter().to_rgb
mycm = make_colormap([
c('white'),
c('yellow'), 0.3,
c('yellow'),
c('red'), 0.60,
c('red'),
c('green'), 0.8,
c('green')
])
# colours=plt.cm.CMRmap(xx)
colours = plt.cm.get_cmap(mycm)(xx)
stats_table = table(ax2,
cellText=df.values,
rowLabels='GATC',
colLabels=df.columns,
loc='center',
cellColours=colours)
stats_table._cells[(0, base_pos_in_spacer - 1)]._text.set_color('red')
stats_table._cells[(2, -1)]._text.set_color('green')
stats_table._cells[(3, -1)]._text.set_color('red')
stats_table._cells[(4, -1)]._text.set_color('blue')
# stats_table._cells[(1,base_pos_in_spacer-1)].set_facecolor('red')
for (row, col), cell in stats_table.get_celld().items():
cell.set_linewidth(0)
if (col == -1):
cell.set_text_props(fontproperties=FontProperties(
family='Arial', size=8, weight='bold'))
elif (row == 0):
cell.set_text_props(
fontproperties=FontProperties(family='Arial', size=8))
else:
cell.set_text_props(
fontproperties=FontProperties(family='Arial', size=6))
# shrink current axis
box = ax2.get_position()
ax2.set_position(
[box.x0 + 0.038, box.y0 + 0.15, box.width * 0.9, box.height * 0.8])
# ax2.arrow(0, 20, 10, 47, head_width=1, head_length=2, fc='k', ec='k')
if (strand == "+"):
ax2.annotate("5'>",
xy=(0, 47),
xycoords='axes points',
size=5,
color='k',
weight='bold',
ha='right',
va='top')
else:
ax2.annotate("3'<",
xy=(0, 47),
xycoords='axes points',
size=5,
color='k',
weight='bold',
ha='right',
va='top')
figfile = filename.split('.')[0] + '.png'
fig.savefig(directory + '/result/' + figfile, dpi=300)
plt.close()
],
[
'Maximum Head above\nLand Surface (ft)',
str(round(abs(max_dtw), 2))
],
[
'Number of Locations\nwith Max Head',
str(num_max_values)
]]
cell_color = [['white', 'white'], ['white', 'white'],
['white', 'white']]
tbl = table(ax,
cellText=table_vals,
cellColours=cell_color,
bbox=[0.5, 0.7, 0.40, 0.2],
rasterized=True,
zorder=10)
tbl.auto_set_font_size(False)
tbl.set_fontsize(8)
tbl.auto_set_column_width(col=[0, 1])
# plot locations of max heads above ground surface
ax.scatter(max_values.X,
max_values.Y,
s=50,
facecolors='none',
edgecolors='r')
def plot_catalogue_performance(data_table,strategies,filename=None,figsize=(12,6),zbins=[(0.9,2.1),(2.1,None)],desi_nqso=[1.3*10**6,0.8*10**6],dv_max=6000.,show_correctwrongzbin=False,verbose=False,nydec=0,ymax=0.1,filter=None,add_bar_heights=True,extrarow=False,rotation=0.):
fig, axs = plt.subplots(1,len(zbins),figsize=figsize,sharey=True,squeeze=False)
if filter is None:
filt = np.ones(len(data_table)).astype(bool)
else:
filt = filter
# determine the true classifications
isqso_truth, isgal_truth, isstar_truth, isbad = get_truths(data_table)
for i,zbin in enumerate(zbins):
for s in strategies.keys():
# Make a filter to deal with masked arrays.
filt_s = filt & (strategies[s]['isqso']|True)
z_s = strategies[s]['z']
w_s = strategies[s]['isqso']
in_zbin_zvi = np.ones(data_table['Z_VI'].shape).astype(bool)
in_zbin_zs = np.ones(z_s.shape).astype(bool)
if zbin[0] is not None:
in_zbin_zvi &= (data_table['Z_VI']>=zbin[0])
in_zbin_zs &= (z_s>=zbin[0])
if zbin[1] is not None:
in_zbin_zvi &= (data_table['Z_VI']<zbin[1])
in_zbin_zs &= (z_s<zbin[1])
dv = strategy.get_dv(z_s,data_table['Z_VI'],data_table['Z_VI'],use_abs=True)
zgood = (dv <= dv_max)
strategies[s]['ncat'] = (w_s & in_zbin_zs & (~isbad) & filt_s).sum()
strategies[s]['nstar'] = (w_s & in_zbin_zs & isstar_truth & filt_s).sum()
strategies[s]['ngalwrongz'] = (w_s & ~zgood & in_zbin_zs & isgal_truth & filt_s).sum()
strategies[s]['nqsowrongz'] = (w_s & ~zgood & in_zbin_zs & isqso_truth & filt_s).sum()
strategies[s]['ncorrectwrongzbin'] = (w_s & zgood & in_zbin_zs & (isqso_truth | isgal_truth) & ~in_zbin_zvi & filt_s).sum()
strategies[s]['nwrong'] = (strategies[s]['nstar'] + strategies[s]['ngalwrongz']
+ strategies[s]['nqsowrongz'] + strategies[s]['ncorrectwrongzbin'])
com_num = (w_s & zgood & in_zbin_zs & isqso_truth & in_zbin_zvi & filt_s).sum()
com_denom = (isqso_truth & in_zbin_zvi & filt_s).sum()
strategies[s]['completeness'] = com_num/com_denom
if ('RR' in s) and verbose:
print(s)
for k in strategies[s]:
if (k[0]=='n') or (k=='completeness'):
print(k,strategies[s][k])
nlostqso = (~w_s & isqso_truth & in_zbin_zvi).sum()
print(nlostqso,com_denom,nlostqso/com_denom)
print('')
nwrong = np.array([strategies[s]['nwrong'] for s in strategies.keys()])
ncat = np.array([strategies[s]['ncat'] for s in strategies.keys()])
pstar = np.array([strategies[s]['nstar'] for s in strategies.keys()])/ncat
pgalwrongz = np.array([strategies[s]['ngalwrongz'] for s in strategies.keys()])/ncat
pqsowrongz = np.array([strategies[s]['nqsowrongz'] for s in strategies.keys()])/ncat
pcorrectwrongzbin = np.array([strategies[s]['ncorrectwrongzbin'] for s in strategies.keys()])/ncat
completeness = np.array([strategies[s]['completeness'] for s in strategies.keys()])
axs[0,i].bar(range(len(strategies)),pstar,color=utils.colours['C0'],label='star',width=0.5)
axs[0,i].bar(range(len(strategies)),pgalwrongz,bottom=pstar,color=utils.colours['C1'],label='galaxy w.\nwrong $z$',width=0.5)
bars = axs[0,i].bar(range(len(strategies)),pqsowrongz,bottom=pstar+pgalwrongz,color=utils.colours['C2'],label='QSO w.\nwrong $z$',width=0.5)
if show_correctwrongzbin:
axs[0,i].bar(range(len(strategies)),pcorrectwrongzbin,bottom=pstar+pgalwrongz+pqsowrongz,color=utils.colours['C3'],label='correct w.\nwrong $z$-bin',width=0.5)
if add_bar_heights:
bar_heights = pstar+pgalwrongz+pqsowrongz
if show_correctwrongzbin:
bar_heights += pcorrectwrongzbin
utils.autolabel_bars(axs[0,i],bars,numbers=bar_heights,heights=bar_heights,percentage=True,above=True)
DESI_ncat_presents = []
for j,c in enumerate(completeness):
pcon = nwrong[j]/ncat[j]
DESI_ncat = c * desi_nqso[i]/(1 - pcon)
DESI_ncat_present = (round(DESI_ncat * 10**-6,3))
DESI_ncat_presents.append(DESI_ncat_present)
axs[0,i].set_xlabel('classification strategy',labelpad=10)
axs[0,i].set_xticks(range(len(strategies)))
axs[0,i].set_xlim(-0.5,len(strategies)-0.5)
slabels = []
for s in strategies.keys():
try:
slabels += [strategies[s]['label']]
except KeyError:
slabels += [s]
if rotation>0:
ha = 'right'
else:
ha = 'center'
axs[0,i].set_xticklabels(slabels,rotation=rotation, ha=ha, rotation_mode="anchor")
axs[0,i].yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1.0,decimals=nydec))
axs[0,i].set_ylim(0,ymax)
zbin_label = get_label_from_zbin(zbin)
axs[0,i].text(0.5,1.05,zbin_label,ha='center',va='center',transform=axs[0,i].transAxes)
cell_text = []
for s in slabels:
if '\n' in s:
extrarow = True
if extrarow:
cell_text.append(['']*len(completeness))
cell_text.append(['{:2.1%}'.format(c) for c in completeness])
cell_text.append(['{:1.2f}'.format(c) for c in DESI_ncat_presents])
rowLabels = []
if extrarow:
rowLabels += ['']
if i==0:
rowLabels += ['completeness:','estimated DESI\ncatalogue size\n[million QSOs]:']
else:
rowLabels += ['','']
table = mp_table.table(cellText=cell_text,
rowLabels=rowLabels,
colLabels=['' for s in strategies.keys()],
loc='bottom',
ax=axs[0,i],
edges='open',
cellLoc='center',
rowLoc='right',
in_layout=True)
table.scale(1,6)
offset_label = -0.1
axs[0,0].set_ylabel('contamination of\nQSO catalogue')
axs[0,1].legend()
rect = (0.07,0.2,1.0,1.0)
plt.tight_layout(rect=rect)
if filename is not None:
plt.savefig(filename)
return fig, axs
def __call__(self, data):
t, y = data
if self.model._last_visualized_ts is None or self.vis is None:
self.vis = t
elif self.vis < self.model._last_visualized_ts:
# This is to avoid cases with repeated past measurements. Should
# perhaps be handled in livehart.py
self.vis = self.model._last_visualized_ts
if self.start_day is None:
self.start_day = t.day
try:
self.model.update(y)
except ValueError as err:
logging.error('ERROR: %s' % (err))
return None
# Update lines
bufidx = self.model._buffer.index
tmpdata = self.model._buffer.loc[bufidx > self.vis]
day = tmpdata.index.day - self.start_day
self.xdata.extend(
(day * 3200 * 24 + tmpdata.index.hour * 3200 +
tmpdata.index.minute * 60 + tmpdata.index.second).values.tolist())
self.ydata.extend(tmpdata['active'].values.tolist())
lim = min(len(self.xdata), self.time_window)
self.line_active.set_data(self.xdata[-lim:], self.ydata[-lim:])
lim1 = (bufidx[-1] - self.model._last_visualized_ts).seconds
ydisp = self.model._yest[-(lim - lim1):].tolist()
if lim1 > 0:
self.line_est.set_data(self.xdata[-lim:-lim1], ydisp)
ymatchdisp = self.model._ymatch['active'].values[
-lim:-lim1].tolist()
self.line_match.set_data(self.xdata[-lim:-lim1], ymatchdisp)
else:
self.line_est.set_data(self.xdata[-lim:], ydisp)
ymatchdisp = self.model._ymatch['active'].values[-lim:].tolist()
self.line_match.set_data(self.xdata[-lim:], ymatchdisp)
# Update axis limits
tidx = t.hour * 3200 + t.minute * 60 + t.second
xmin, xmax = self.ax.get_xlim()
xmin = max(0, tidx + self.step - self.time_window + 1)
xmax = max(self.time_window, tidx + self.step)
ymin = min(self.ydata[-self.time_window:] + [0]) # List concatenation
ymax = max(self.ydata[-self.time_window:] + [0]) # List concatenation
self.ax.set_xlim(xmin - 100, xmax + 100)
self.ax.set_ylim(ymin - 50, ymax + 100)
self.ax.figure.canvas.draw()
# Add table
if not self.model.live:
cell_text = [['None', '-', '-']]
else:
cell_text = [[m.name, m.signature[0, 0], m.signature[0, 1]]
for m in self.model.live]
cell_text.append(['Other', self.model.residual_live[0], '-'])
cell_text.append(['Background', self.model.background_active, '-'])
tab = table(self.axt,
cell_text,
colLabels=['Appliance', 'Active', 'Reactive'],
cellLoc='left',
colLoc='left',
edges='horizontal')
for (row, col), cell in tab.get_celld().items():
if (row == 0) or (col == -1):
cell.set_text_props(fontproperties=FontProperties(
weight='bold'))
self.axt.clear()
self.axt.add_table(tab)
self.axt.set_axis_off()
self.axt.figure.canvas.draw()
# TODO (for dates)
# self.xdata.extend(y.index.strftime('%Y-%m-%d %H:%M:%S').tolist())
# Save model
if self.model_path_w and \
(self.save_counter % self.MODEL_SAVE_STEP == 0):
with open(self.model_path_w, "wb") as fp:
dill.dump(self.model, fp)
self.save_counter += 1
return self.line_active, \
self.line_est, \
self.line_match
