def draw(fig, old, new, leglabels, legpointers):
ax = fig.gca()
assert old.nsensors == new.nsensors
for i in range(0, old.nsensors):
old.sensors[i].drawNominalPosition(
"nominal position" if i == 0 else "", ax, leglabels, legpointers)
#no name skips label for i>1
#draw fitted old position
old.sensors[i].drawSensor("fitted w/o B-field" if i == 0 else "", "r",
old.x, old.y, ax, leglabels, legpointers)
#draw fitted new position
new.sensors[i].drawSensor("fitted w/ B-field" if i == 0 else "", "g",
new.x, new.y, ax, leglabels, legpointers)
#arrows and text
"""
ax.annotate("",
xy=(posnew[0]+shiftnew[0],posnew[1]+shiftnew[1]), xycoords='data',
xytext=(pos[0]+shift[0],pos[1]+shift[1]), textcoords='data',
arrowprops=dict(arrowstyle="fancy", #linestyle="dashed",
color="0.1",
shrinkA=0, shrinkB=0,
patchA=None,
patchB=None,
connectionstyle="arc3,rad=0.2",
),
) #arrow for shift due to magnetic field
"""
text = (
"Far" if new.isFarHalf else "Near"
) + " (without B field)\nx={0:.2f}+-{1:.2f}\ny={2:.3f}+-{3:.2f}".format(
old.x, (old.xeu - old.xel) / 2, old.y, (old.yeu - old.yel) / 2)
an1 = ax.annotate(text,
xy=(0.02 if new.isFarHalf else 0.59, 0.97),
xycoords="axes fraction",
va="top",
ha="left" if new.isFarHalf else "right",
bbox=dict(boxstyle="round",
fc="w")) #box with values 1
from matplotlib.text import OffsetFrom
offset_from = OffsetFrom(an1, (0.5, 0))
text = (
"Far" if new.isFarHalf else "Near"
) + " (with B field)\nx={0:.2f}+-{1:.2f}\ny={2:.3f}+-{3:.2f}".format(
new.x, (new.xeu - new.xel) / 2, new.y, (new.yeu - new.yel) / 2)
an2 = ax.annotate(
text,
xy=(0.1, 0.1),
xycoords="data",
xytext=(0, -10),
textcoords=offset_from,
# xytext is offset points from "xy=(0.5, 0), xycoords=at"
va="top",
ha="center",
bbox=dict(boxstyle="round", fc="w")) #box with values 2
def init_plots(model_names, all_model_state):
"""Return the figure and annotations for the animation.
"""
fig, axs = plt.subplots(1,
len(MODEL_NAMES),
figsize=(5 * len(MODEL_NAMES), 5))
min_value, max_value = -2, 2
images, model_data, annotes = [], [], []
for i, ax in enumerate(fig.axes):
image, info = ALL_MODEL_STATES[i][0]
images.append(
ax.imshow(image,
vmin=min_value,
vmax=max_value,
cmap="RdYlGn",
interpolation=None))
ax.axis("off")
ax.set_title(MODEL_NAMES[i])
model_data.append(info)
helper = ax.annotate("",
xy=(0.5, 0),
xycoords="axes fraction",
va="bottom",
ha="center")
offset_from = OffsetFrom(helper, (0.5, 0))
a = ax.annotate("seat",
xy=(0, 0),
xycoords="data",
xytext=(0, -10),
textcoords=offset_from,
va="top",
ha="center",
bbox=dict(boxstyle="round", fc="w"),
arrowprops=dict(arrowstyle="->"),
alpha=1)
a.set_visible(False)
annotes.append(a)
return fig, images, annotes, model_data
xycoords=("data", 'axes fraction'),
xytext=(0, -20),
textcoords='offset points',
ha="center",
va="top",
bbox=bbox_args,
arrowprops=arrow_args)
ax2.annotate('xy=(0.5, 0)\nxycoords=artist',
xy=(0.5, 0.),
xycoords=text,
xytext=(0, -20),
textcoords='offset points',
ha="center",
va="top",
bbox=bbox_args,
arrowprops=arrow_args)
ax2.annotate('xy=(0.8, 0.5)\nxycoords=ax1.transData',
xy=(0.8, 0.5),
xycoords=ax1.transData,
xytext=(10, 10),
textcoords=OffsetFrom(ax2.bbox, (0, 0), "points"),
ha="left",
va="bottom",
bbox=bbox_args,
arrowprops=arrow_args)
ax2.set(xlim=[-2, 2], ylim=[-2, 2])
plt.show()
plt.savefig("Annotate_Simple_Coord_b.png")
plt.show()
# source 2 : https://matplotlib.org/devdocs/gallery/userdemo/annotate_simple_coord02.html#sphx-glr-gallery-userdemo-annotate-simple-coord02-py
## =====================================
import matplotlib.pyplot as plt
from matplotlib.text import OffsetFrom
fig, ax = plt.subplots(figsize = (3, 2))
an1 = ax.annotate("Test 1", xy = (0.5, 0.5), xycoords = "data",
va = "center", ha = "center",
bbox = dict(boxstyle = "round", fc = "w"))
offset_from = OffsetFrom(an1, (0.5, 0))
an2 = ax.annotate("Test 2", xy = (0.1, 0.1), xycoords = "data",
xytext = (0, -10), textcoords = offset_from,
# xytext is offset points from "xy = (0.5, 0), xycoords = an1"
va = "top", ha = "center",
bbox = dict(boxstyle = "round", fc = "w"),
arrowprops = dict(arrowstyle = "->"))
plt.savefig("Annotate_Simple_Coord_c.png")
plt.show()
# source 3 : https://matplotlib.org/devdocs/gallery/userdemo/annotate_simple_coord03.html#sphx-glr-gallery-userdemo-annotate-simple-coord03-py
shrinkA=5,shrinkB=5,
relpos=(1, 1)),
horizontalalignment='right', verticalalignment='top', multialignment='left',
fontfamily='Futura LT', fontsize=28, fontweight='bold', color=next_color)
if args.logyscale:
helpusY=cosur_estimate_arrow_text_y / 2
else:
helpusY=cosur_estimate_arrow_text_y - (ax.get_ylim()[1]-ax.get_ylim()[0])/10
#ax.annotate(_('help us get more data'),
# xy=(cosur_estimate_arrow_text_x, helpusY ), xycoords='data',
# horizontalalignment='left', verticalalignment='top', multialignment='left',
# fontfamily='Futura LT', fontsize=20, fontweight='normal', color=next_color)
offset_from = OffsetFrom(ancosur, (0, 0))
yoff= args.helpusoff
#print ("yoff=",yoff)
ax.annotate(_('help us get more data'),
xy=(0, yoff), xycoords=offset_from,
horizontalalignment='left', verticalalignment='top', multialignment='left',
fontfamily='Futura LT', fontsize=20, fontweight='normal', color=next_color)
#sns.lineplot(data=df, x='date', y='prop_cases', ax=ax, color=next(new_palette)) #, err_style="bars")
#sns.lineplot(data=df, x='date', y='dunbar_cases', ax=ax) #, err_style="bars")
## Date Limits
ha="center", va="top",
bbox=bbox_args,
arrowprops=arrow_args
)
from matplotlib.text import OffsetFrom
ax2.annotate('xy=(0.5, 0)\nxycoords=artist',
xy=(0.5, 0.), xycoords=t,
xytext=(0, -20), textcoords='offset points',
ha="center", va="top",
bbox=bbox_args,
arrowprops=arrow_args
)
ax2.annotate('xy=(0.8, 0.5)\nxycoords=ax1.transData',
xy=(0.8, 0.5), xycoords=ax1.transData,
xytext=(10, 10), textcoords=OffsetFrom(ax2.bbox, (0, 0), "points"),
ha="left", va="bottom",
bbox=bbox_args,
arrowprops=arrow_args
)
ax2.set_xlim(-2, 2)
ax2.set_ylim(-2, 2)
an1.draggable()
an2.draggable()
plt.show()
def plotmBB(df, df_cut):
bins = np.linspace(0, 500, 51)
# Initialise plot stuff
plt.ion()
plt.clf()
plot_range = (0, 500)
plot_data = []
plot_weights = []
plot_colors = []
plt.rc('font', weight='bold')
plt.rc('xtick.major', size=5, pad=7)
plt.rc('xtick', labelsize=10)
plt.rcParams["font.weight"] = "bold"
plt.rcParams["axes.labelweight"] = "bold"
plt.rcParams["mathtext.default"] = "regular"
df_signal = df.loc[df['Class'] == 1.0]
df_background = df.loc[df['Class'] == 0.0]
df_signal_cut = df_cut.loc[df_cut['Class'] == 1.0]
df_background_cut = df_cut.loc[df_cut['Class'] == 0.0]
plot_weights_signal = df_signal['post_fit_weight']
plot_weights_signal = (1 / sum(plot_weights_signal)) * plot_weights_signal
plot_weights_signal_cut = df_signal_cut['post_fit_weight']
plot_weights_signal_cut = (
1 / sum(plot_weights_signal_cut)) * plot_weights_signal_cut
plot_weights_background = df_background['post_fit_weight']
plot_weights_background = (
1 / sum(plot_weights_background)) * plot_weights_background
plot_weights_background_cut = df_background_cut['post_fit_weight']
plot_weights_background_cut = (
1 / sum(plot_weights_background_cut)) * plot_weights_background_cut
mbb_vals = df['mBB'].tolist()
for i in range(len(df)):
if mbb_vals[i] >= 500000:
mbb_vals[i] = 499500
df['mBB'] = mbb_vals
mbb_vals_cut = df_cut['mBB'].tolist()
for i in range(len(df_cut)):
if mbb_vals_cut[i] >= 500000:
mbb_vals_cut[i] = 499500
df_cut['mBB'] = mbb_vals_cut
variable_values_signal = df_signal['mBB'] / 1000
variable_values_background = df_background['mBB'] / 1000
variable_values_signal_cut = df_signal_cut['mBB'] / 1000
variable_values_background_cut = df_background_cut['mBB'] / 1000
if True == False:
count_sig_cut = plt.hist(
variable_values_signal_cut,
bins=bins,
weights=plot_weights_signal_cut,
range=plot_range,
rwidth=1,
color='#b32400', #dark red
label='Signal after cut',
stacked=False,
alpha=1,
#hatch=4*'\\'
)
if True != False:
count_back_cut = plt.hist(
variable_values_background_cut,
bins=bins,
weights=plot_weights_background_cut,
range=plot_range,
rwidth=1,
linewidth=3,
color='#0066CC', #dark blue
label='Background after cut',
alpha=1,
#hatch=4*'//'
)
if True == False:
# Plot.
count_sig = plt.hist(
variable_values_signal,
bins=bins,
weights=plot_weights_signal,
range=plot_range,
rwidth=1,
histtype='step',
linewidth=3,
edgecolor='#FF0000',
#edgecolor='red',
label='Signal',
stacked=False,
alpha=1,
#hatch=4*'\\'
)
if True != False:
count_back = plt.hist(
variable_values_background,
bins=bins,
weights=plot_weights_background,
range=plot_range,
rwidth=1,
histtype='step',
linewidth=3,
edgecolor='#0000b3',
#edgecolor='blue',
label='Background',
alpha=1,
#hatch=4*'//'
)
x1, x2, y1, y2 = plt.axis()
#plt.yscale('log', nonposy='clip')
plt.axis((x1, x2, y1, y2 * 1.2))
axes = plt.gca()
axes.set_ylim([0, 0.12])
axes.set_xlim(plot_range)
x = [-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1]
#plt.xticks(x, x,fontweight = 'normal',fontsize = 16)
y = ["10", r"10$^{2}$", r"10$^{3}$", r"10$^{4}$", r"10$^{5}$"]
yi = [10, 100, 1000, 10000, 100000]
#plt.yticks(yi, y,fontweight = 'normal',fontsize = 16)
axes.yaxis.set_ticks_position('both')
axes.yaxis.set_tick_params(which='both', direction='in')
axes.xaxis.set_ticks_position('both')
axes.xaxis.set_tick_params(which='both', direction='in')
axes.xaxis.set_minor_locator(AutoMinorLocator(4))
axes.yaxis.set_minor_locator(AutoMinorLocator(4))
handles, labels = axes.get_legend_handles_labels()
plt.legend(loc='upper right',
ncol=1,
prop={'size': 12},
frameon=False,
handles=handles[::-1])
plt.ylabel('Normalised Number of Events', fontsize=16, fontweight='normal')
axes.yaxis.set_label_coords(-0.1, 0.5)
plt.xlabel("$m_{bb}$ [GeV]", fontsize=16, fontweight='normal')
axes.xaxis.set_label_coords(0.89, -0.07)
an1 = axes.annotate("ATLAS",
xy=(0.05, 0.91),
xycoords=axes.transAxes,
fontstyle='italic',
fontsize=16)
offset_from = OffsetFrom(an1, (0, -1.4))
an2 = axes.annotate(r'$\sqrt{s}$' + " = 13 TeV , 36.1 fb$^{-1}$",
xy=(0.05, 0.91),
xycoords=axes.transAxes,
textcoords=offset_from,
fontweight='normal',
fontsize=12)
offset_from = OffsetFrom(an2, (0, -1.4))
an3 = axes.annotate("1 lepton, " + str(nJets) + " jets, 2 b-tags",
xy=(0.05, 0.91),
xycoords=axes.transAxes,
textcoords=offset_from,
fontstyle='italic',
fontsize=12)
offset_from = OffsetFrom(an3, (0, -1.6))
an4 = axes.annotate("p$^V_T \geq$ 150 GeV",
xy=(0.05, 0.91),
xycoords=axes.transAxes,
textcoords=offset_from,
fontstyle='italic',
fontsize=12)
plt.show(block=True)
def bdt_plot(df,z_s = 10,z_b = 10,show=False, block=False, trafoD_bins = False, bin_number = 20):
"""Plots histogram decision score output of classifier"""
nJets = df['nJ'].tolist()[1]
df['decision_value'] = ((list(df['decision_value'])-0.5)*2)
if trafoD_bins == True:
bins, arg2, arg3 = trafoD_with_error(df)
print(len(bins))
else:
bins = np.linspace(-1,1,bin_number+1)
# Initialise plot stuff
plt.ion()
plt.close("all")
fig = plt.figure(figsize=(8.5,7))
plot_range = (-1, 1)
plot_data = []
plot_weights = []
plot_colors = []
plt.rc('font', weight='bold')
plt.rc('xtick.major', size=5, pad=7)
plt.rc('xtick', labelsize=10)
plt.rcParams["font.weight"] = "bold"
plt.rcParams["axes.labelweight"] = "bold"
plt.rcParams["mathtext.default"] = "regular"
df = setBinCategory(df,bins)
decision_value_list = df['bin_scaled'].tolist()
post_fit_weight_list = df['post_fit_weight'].tolist()
sample_list = df['sample'].tolist()
# Get list of hists.
for t in class_names_grouped[::-1]:
class_names = class_names_map[t]
class_decision_vals = []
plot_weight_vals = []
for c in class_names:
for x in range(0,len(decision_value_list)):
if sample_list[x] == c:
class_decision_vals.append(decision_value_list[x])
plot_weight_vals.append(post_fit_weight_list[x])
plot_data.append(class_decision_vals)
plot_weights.append(plot_weight_vals)
plot_colors.append(colour_map[t])
#Plots the filled in histogram parts
plt.hist(plot_data,bins=bins,weights=plot_weights,range=plot_range,rwidth=1,color=plot_colors,label=legend_names[::-1],stacked=True,edgecolor='none')
#Plots the additional line over the top for signal events
if nJets == 2:
multiplier = 20
elif nJets == 3:
multiplier = 100
plt.plot([],[],color='#FF0000',label=r'VH $\rightarrow$ Vbb x '+str(multiplier))
df_sig = df.loc[df['Class']==1]
plt.hist(df_sig['bin_scaled'].tolist(),bins=bins,weights=(df_sig['post_fit_weight']*multiplier).tolist(),range=plot_range,rwidth=1,histtype = 'step',linewidth=2,color='#FF0000',edgecolor='#FF0000')
#sets axis limits and labels
x1, x2, y1, y2 = plt.axis()
plt.yscale('log', nonposy='clip') # can comment out this line if log error stops plotting
plt.axis((x1, x2, y1, y2 * 1.2))
axes = plt.gca()
axes.set_ylim([5,135000])
axes.set_xlim([-1,1])
x = [-1,-0.8,-0.6,-0.4,-0.2,0,0.2,0.4,0.6,0.8,1]
plt.xticks(x, x,fontweight = 'normal',fontsize = 20)
y = [r"10",r"10$^{2}$",r"10$^{3}$",r"10$^{4}$",r"10$^{5}$"]
yi = [10,100,1000,10000,100000]
plt.yticks(yi, y,fontweight = 'normal',fontsize = 20) # can comment out this line if log error stops plotting
#sets axis ticks
axes.yaxis.set_ticks_position('both')
axes.yaxis.set_tick_params(which='major', direction='in', length=10, width=1)
axes.yaxis.set_tick_params(which='minor', direction='in', length=5, width=1)
axes.xaxis.set_ticks_position('both')
axes.xaxis.set_tick_params(which='major', direction='in', length=10, width=1)
axes.xaxis.set_tick_params(which='minor', direction='in', length=5, width=1)
axes.xaxis.set_minor_locator(AutoMinorLocator(4))
handles, labels = axes.get_legend_handles_labels()
#hack thing to get legend entries in correct order
handles = handles[::-1]
handles = handles+handles
handles = handles[1:12]
plt.legend(loc='upper right', ncol=1, prop={'size': 12},frameon=False,handles=handles)
#axis titles and lables
plt.ylabel("Events",fontsize = 20,fontweight='normal')
axes.yaxis.set_label_coords(-0.07,0.93)
plt.xlabel(r"BDT$_{VH}$ output",fontsize = 20,fontweight='normal')
axes.xaxis.set_label_coords(0.89, -0.07)
an1 = axes.annotate("ATLAS Internal", xy=(0.05, 0.91), xycoords=axes.transAxes,fontstyle = 'italic',fontsize = 16)
offset_from = OffsetFrom(an1, (0, -1.4))
an2 = axes.annotate(r'$\sqrt{s}$' + " = 13 TeV , 36.1 fb$^{-1}$", xy=(0.05,0.91), xycoords=axes.transAxes, textcoords=offset_from, fontweight='normal',fontsize = 12)
offset_from = OffsetFrom(an2, (0, -1.4))
an3 = axes.annotate("1 lepton, "+str(nJets)+" jets, 2 b-tags", xy=(0.05,0.91), xycoords=axes.transAxes, textcoords=offset_from,fontstyle = 'italic',fontsize = 12)
offset_from = OffsetFrom(an3, (0, -1.6))
an4 = axes.annotate("p$^V_T \geq$ 150 GeV", xy=(0.05,0.91), xycoords=axes.transAxes, textcoords=offset_from,fontstyle = 'italic',fontsize = 12)
plt.show(block=block)
return fig,axes
def plot(self, fig, ax, interactive=False, state=-1):
"""Using matplotlib to draw the current state of the model.
Args:
fig: the pyplot figure.
ax: the axes to draw the plot.
interactive: whether to handle mouse events.
state: which model state to use. -1 can be used to show last state.
"""
try:
image, info = self.model_states[state]
except:
image, info = self.get_model_state()
ax.clear()
self.im = ax.imshow(image,
vmin=-2,
vmax=2,
cmap='RdYlGn',
interpolation=None)
ax.axis('off')
if state < 0:
num_students = len(self.students) + state + 1
else:
num_students = min(state, len(self.students))
ax.set_title('Classroom State ({} students)'.format(num_students))
helper = ax.annotate("",
xy=(0.5, 0),
xycoords="axes fraction",
va="bottom",
ha="center")
offset_from = OffsetFrom(helper, (0.5, 0))
a = ax.annotate("seat",
xy=(0, 0),
xycoords="data",
xytext=(0, -10),
textcoords=offset_from,
va="top",
ha="center",
bbox=dict(boxstyle="round", fc="w"),
arrowprops=dict(arrowstyle="->"),
alpha=1)
a.set_visible(False)
def hover(event):
if (event.inaxes):
x, y = int(np.round(event.xdata)), int(np.round(event.ydata))
value = self.im.get_array()[y][x]
seat_utility, student_id, sociability, initial_happiness = (
info[y, x])
text = ""
if value == -0.8:
text = "AISLE"
elif value == -3:
text = "DOOR"
elif value <= -1:
text = "EMPTY SEAT\nSeat attractivness: {:.2f}\nAccessibility: {:.2f}".format(
seat_utility, value + 2)
elif value >= 0:
text = "FILLED SEAT\n {} {:.2f} \n {} {:.2f} \n {} {:.2f} \n {} {:.2f}".format(
"Seat attractivness:", seat_utility,
"Student sociability:", sociability,
"Initial happiness (t = {}):".format(int(student_id)),
initial_happiness, "Current happiness:", value - 1)
a.set_text(text)
a.xy = (x, y)
a.set_visible(True)
fig.canvas.draw_idle()
else:
a.set_visible(False)
fig.canvas.draw_idle()
ax.set_xticks(np.arange(-0.5, image.shape[1]), minor=True)
ax.set_yticks(np.arange(-0.5, image.shape[0]), minor=True)
ax.grid(which='minor', color='k', linestyle='-', linewidth=2)
if interactive:
cid = fig.canvas.mpl_connect('motion_notify_event', hover)
fig.tight_layout(rect=[0, 0.2, 1, 1])
def crossquiverkey(QV, **kwargs):
u"""
鉛直ベクトルプロットのrefference arrowを描く
:Arguments:
**QV**
**Keyword**
独自キーワード
=============== =========== ======================================================
Value Default Description
=============== =========== ======================================================
data_scale
zscale QV.zscale 水平成分に対する鉛直成分スケール比
dot_scale 30
uvunit 水平成分のreferrence arrowにつける単位
wunit 鉛直成分のreferrence arrowにつける単位
=============== =========== ======================================================
デフォルトを独自に設定しているキーワード
=============== ======= ======================================================
Value Default Description
=============== ======= ======================================================
=============== ======= ======================================================
"""
from matplotlib.text import OffsetFrom
ax = kwargs.pop('ax', plt.gca())
uvunit = kwargs.pop('uvunit', '')
wunit = kwargs.pop('wunit', '')
dot_scale = kwargs.pop('dot_scale', 30)
## Refference Allowの長さ (データ単位)
data_scale = kwargs.pop('data_scale', None)
zscale = kwargs.pop('zscale', None)
if zscale == None: zscale = getattr(QV, 'zscale', 1)
if data_scale == None:
data_scale = getattr(
QV, 'data_scale',
tools.roundoff(np.hypot(QV.U, QV.V).max(), digit=1))
offset_from = OffsetFrom(ax, (0.95, 1))
uvlabel = '%.1f %s' % (data_scale, uvunit)
wlabel = '%.1f %s' % (data_scale / zscale, wunit)
ax.annotate('',
xy=(0, 5),
xycoords=offset_from,
xytext=(-dot_scale, dot_scale + 5),
textcoords=offset_from,
ha='right',
va='bottom',
arrowprops=dict(arrowstyle='<->',
connectionstyle='angle,rad=0'))
ax.annotate(uvlabel,
xy=(1, 1),
xycoords='axes fraction',
xytext=(3, 5),
textcoords='offset points',
ha='right',
va='bottom')
ax.annotate(wlabel,
xy=(0, 5),
xycoords=offset_from,
xytext=(-dot_scale * 0.8, dot_scale * 0.8),
textcoords=offset_from,
ha='left',
va='bottom')