def exibir(self):
for i in self.dados.keys():
if len(self.dados[i]) > 0: continue
else: return
defaults.width, defaults.height = 400, 300
# prepare some data
# input options
hist_pontuacao = Histogram(self.dados["pontos"],
title="Grades por pontuação",
xlabel="Pontuação",
ylabel="Número de grades",
responsive=True,
bins=30)
hist_tamanho = Histogram(self.dados["tamanhos"],
title="Grades por quantidade de disciplinas",
xlabel="Número de disciplinas",
ylabel="Número de grades",
responsive=True,
bins=8)
hist_pop = Histogram(self.dados["popularidade"],
title="Ocorrências da disciplina x",
xlabel="Disciplina",
ylabel="Ocorrências nas grades",
responsive=True,
bins=46)
output_file("html/histograms.html")
show(hplot(hist_pontuacao, hist_tamanho, hist_pop))
def exibir(self):
for i in self.dados.keys():
if len(self.dados[i]) > 0: continue
else: return
defaults.width, defaults.height = 400, 300
# prepare some data
# input options
hist_pontuacao = Histogram(self.dados["pontos"],
title="Grades por pontuação",
xlabel="Pontuação",
ylabel="Número de grades",
responsive=True,
bins=30)
hist_tamanho = Histogram(self.dados["tamanhos"],
title="Grades por quantidade de disciplinas",
xlabel="Número de disciplinas",
ylabel="Número de grades",
responsive=True,
bins=8)
hist_pop = Histogram(self.dados["popularidade"],
title="Ocorrências da disciplina x",
xlabel="Disciplina",
ylabel="Ocorrências nas grades",
responsive=True,
bins=46)
output_file("html/histograms.html")
show(hplot( hist_pontuacao,
hist_tamanho,
hist_pop
))
def getDistrib():
if request.method == 'GET':
return render_template('distrib.html')
else:
bronx=[[2009,'https://data.cityofnewyork.us/resource/en2c-j6tw.json'],[2010,'https://data.cityofnewyork.us/resource/n2s5-fumm.json'],[2011,'https://data.cityofnewyork.us/resource/bawj-6bgn.json'],[2012,'https://data.cityofnewyork.us/resource/3qfc-4tta.json']]
brooklyn=[[2009,'https://data.cityofnewyork.us/resource/rmv8-86p4.json'],[2010,'https://data.cityofnewyork.us/resource/w6yt-hctp.json'],[2011,'https://data.cityofnewyork.us/resource/5mw2-hzqx.json'],[2012,'https://data.cityofnewyork.us/resource/bss9-579f.json']]
manhattan=[[2009,'https://data.cityofnewyork.us/resource/956m-xy24.json'],[2010,'https://data.cityofnewyork.us/resource/ad4c-mphb.json'],[2011,'https://data.cityofnewyork.us/resource/ikqj-pyhc.json'],[2012,'https://data.cityofnewyork.us/resource/dvzp-h4k9.json']]
queens=[[2009,'https://data.cityofnewyork.us/resource/m59i-mqex.json'],[2010,'https://data.cityofnewyork.us/resource/crbs-vur7.json'],[2011,'https://data.cityofnewyork.us/resource/s3zn-tf7c.json'],[2012,'https://data.cityofnewyork.us/resource/jcih-dj9q.json']]
statenIsland=[[2009,'https://data.cityofnewyork.us/resource/cyfw-hfqk.json'],[2010,'https://data.cityofnewyork.us/resource/wv4q-e75v.json'],[2011,'https://data.cityofnewyork.us/resource/a5qt-5jpu.json'],[2012,'https://data.cityofnewyork.us/resource/tkdy-59zg.json']]
featureNames=[['comparable_rental_2_market_value_per_sqft', 'Market value per square foot'],['comparable_rental_2_full_market_value', 'Full market value'],['comparable_rental_2_year_built', 'Year Built'],['comparable_rental_2_gross_income_per_sqft', 'Gross income per square foot']]
#request was a POST (get the var from the form)
#... All Boroughs are selected by default
app_xplor.vars['feat'] = request.form['feat']
app_xplor.vars['year'] = request.form['year']
#Translating name of the feature into the name in the original database
dbFeatureName = convertField(app_xplor.vars['feat'],featureNames)
#Building the queries
queryA = buildQuery(int(app_xplor.vars['year']),bronx,dbFeatureName)
queryB = buildQuery(int(app_xplor.vars['year']),brooklyn,dbFeatureName)
queryC = buildQuery(int(app_xplor.vars['year']),manhattan,dbFeatureName)
queryD = buildQuery(int(app_xplor.vars['year']),queens,dbFeatureName)
queryE = buildQuery(int(app_xplor.vars['year']),statenIsland,dbFeatureName)
#executing the queries on the tables
rawA = pd.read_json(queryA)
rawB = pd.read_json(queryB)
rawC = pd.read_json(queryC)
rawD = pd.read_json(queryD)
rawE = pd.read_json(queryE)
#Managind the data to be input for a boxplot
rawA['Borough']='Bronx'
rawB['Borough']='Brooklyn'
rawC['Borough']='Manhattan'
rawD['Borough']='Queens'
rawE['Borough']='Staten Island'
allData = pd.concat([rawA, rawB, rawC, rawD, rawE])
cleanData= allData.dropna()
cleanData.columns=[app_xplor.vars['feat'],'Borough']
#plot
defaults.width = 450
defaults.height = 350
box_plot = BoxPlot(cleanData, label='Borough',title=str(app_xplor.vars['year']))
#box_plot = BoxPlot(cleanData, label='Borough',title='Year')
output_file("templates/results.html")
show(
vplot(
hplot(box_plot)
)
)
return redirect('/goDistrib')
def analyze(self):
h5_filename = self.output_filename +'.h5'
with tb.open_file(h5_filename, 'r+') as in_file_h5:
raw_data = in_file_h5.root.raw_data[:]
meta_data = in_file_h5.root.meta_data[:]
hit_data = self.dut.interpret_raw_data(raw_data, meta_data)
in_file_h5.createTable(in_file_h5.root, 'hit_data', hit_data, filters=self.filter_tables)
status_plot = plotting.plot_status(h5_filename)
occ_plot, H = plotting.plot_occupancy(h5_filename)
tot_plot,_ = plotting.plot_tot_dist(h5_filename)
lv1id_plot, _ = plotting.plot_lv1id_dist(h5_filename)
scan_pix_hist, _ = plotting.scan_pix_hist(h5_filename)
output_file(self.output_filename + '.html', title=self.run_name)
save(vplot(hplot(occ_plot, tot_plot, lv1id_plot), scan_pix_hist, status_plot))
from bokeh.charts import Histogram, defaults, vplot, hplot, show, output_file
from bokeh.sampledata.autompg import autompg as df
defaults.width = 450
defaults.height = 350
# input options
hist = Histogram(df["mpg"], title="df['mpg']")
hist2 = Histogram(df, "displ", title="df, 'displ'")
hist3 = Histogram(df, values="hp", title="df, values='hp'")
hist4 = Histogram(df, values="hp", color="cyl", title="df, values='hp', color='cyl'", legend="top_right")
hist5 = Histogram(df, values="mpg", bins=50, title="df, values='mpg', bins=50")
output_file("histogram_multi.html")
show(vplot(hplot(hist, hist2, hist3), hplot(hist4, hist5)))
cat=categories,
title="All negative input | Grouped",
ylabel="Random Number",
width=width,
height=height)
np_custom = Bar(mixed,
cat=categories,
title="Custom range (start=-3, end=0.4)",
ylabel="Random Number",
width=width,
height=height,
continuous_range=Range1d(start=-3, end=0.4))
np_mixed_grouped = Bar(mixed,
cat=categories,
title="Mixed-sign input | Grouped",
ylabel="Random Number",
width=width,
height=height)
# collect and display
output_file("bar.html")
show(
vplot(
hplot(dict_stacked, df_grouped),
hplot(np_stacked, np_negative_grouped),
hplot(np_mixed_grouped, np_custom),
))
from bokeh.charts import Histogram, defaults, vplot, hplot, show, output_file
from bokeh.sampledata.autompg import autompg as df
defaults.plot_width = 400
defaults.plot_height = 350
# input options
hist = Histogram(df['mpg'], title="df['mpg']")
hist2 = Histogram(df, 'displ', title="df, 'displ'")
hist3 = Histogram(df, values='hp', title="df, values='hp'")
hist4 = Histogram(df, values='hp', color='cyl',
title="df, values='hp', color='cyl'", legend='top_right')
hist5 = Histogram(df, values='mpg', bins=50,
title="df, values='mpg', bins=50")
output_file("histogram_multi.html", title="histogram_multi.py example")
show(vplot(
hplot(hist, hist2),
hplot(hist3, hist4),
hplot(hist5)
))
def tdc_table(self, scanrange):
h5_filename = self.output_filename + '.h5'
with tb.open_file(h5_filename, 'r+') as in_file_h5:
raw_data = in_file_h5.root.raw_data[:]
meta_data = in_file_h5.root.meta_data[:]
if (meta_data.shape[0] == 0): return
repeat_command = in_file_h5.root.meta_data.attrs.kwargs
a = repeat_command.rfind("repeat_command: ")
repeat_command = repeat_command[a + len("repeat_command: "):a + len("repeat_command: ") + 7]
a = repeat_command.rfind("\n")
repeat_command = int(repeat_command[0:a])
param, index = np.unique(meta_data['scan_param_id'], return_index=True)
pxl_list = []
for p in param:
pix_no = int(p) / int(len(self.inj_charge))
pxl_list.append(self.pixel_list[pix_no][0] * 64 + self.pixel_list[pix_no][1])
index = index[1:]
index = np.append(index, meta_data.shape[0])
index = index - 1
stops = meta_data['index_stop'][index]
split = np.split(raw_data, stops)
avg_tdc = []
avg_tdc_err = []
avg_del = []
avg_del_err = []
hits = []
deletelist = ()
for i in range(len(split[:-1])): # loop on pulses
rwa_data_param = split[i]
tdc_data = rwa_data_param & 0xFFF # take last 12 bit
tdc_delay = (rwa_data_param & 0x0FF00000) >> 20
counter = 0.0
TOT_sum = 0.0
DEL_sum = 0.0
if (tdc_data.shape[0] == 0 or tdc_data.shape[0] == 1):
counter = 1.0
for j in range(tdc_data.shape[0]): # loop on repeats
if (j > 0):
counter += 1
TOT_sum += tdc_data[j]
DEL_sum += tdc_delay[j]
if (counter > 1):
hits.append(counter)
avg_tdc.append((float(TOT_sum) / float(counter)) * 1.5625)
avg_tdc_err.append(1.5625 / (np.sqrt(12.0 * counter)))
avg_del.append((float(DEL_sum) / float(counter)) * 1.5625)
avg_del_err.append(1.5625 / (np.sqrt(12.0 * counter)))
else:
deletelist = np.append(deletelist, i)
pxl_list = np.delete(pxl_list, deletelist)
newpix = [0]
pix_no_old = pxl_list[0]
runparam = 0
for p in pxl_list:
if p != pix_no_old:
newpix = np.append(newpix, runparam)
pix_no_old = p
runparam = runparam + 1
addedvalues = 0
for pixels in range(len(newpix)):
missingvalues = 0
if newpix[pixels] == newpix[-1]:
missingvalues = scanrange - abs(newpix[pixels] + addedvalues - len(hits))
else:
if abs(newpix[pixels] - newpix[pixels + 1]) < scanrange:
missingvalues = scanrange - abs(newpix[pixels] - newpix[pixels + 1])
if missingvalues != 0:
hits = np.insert(hits, newpix[pixels] + addedvalues, np.zeros(missingvalues))
avg_tdc = np.insert(avg_tdc, newpix[pixels] + addedvalues, np.zeros(missingvalues))
avg_tdc_err = np.insert(avg_tdc_err, newpix[pixels] + addedvalues, np.zeros(missingvalues))
avg_del = np.insert(avg_del, newpix[pixels] + addedvalues, np.zeros(missingvalues))
avg_del_err = np.insert(avg_del_err, newpix[pixels] + addedvalues, np.zeros(missingvalues))
pxl_list = np.insert(pxl_list, newpix[pixels] + addedvalues,
(pxl_list[newpix[pixels] + addedvalues]) * np.ones(missingvalues))
addedvalues = addedvalues + missingvalues
injections = []
for pixels in range(int(len(pxl_list) / len(self.inj_charge))):
for i in range(len(self.inj_charge)):
injections = np.append(injections, self.inj_charge[i])
pix, stop = np.unique(pxl_list, return_index=True)
stop = np.sort(stop)
stop = list(stop)
stop.append(len(avg_tdc))
repeat_command_dic={}
repeat_command_dic['repeat_command']=repeat_command
avg_tab = np.rec.fromarrays([injections, pxl_list, hits, avg_tdc, avg_tdc_err, avg_del, avg_del_err],
dtype=[('charge', float), ('pixel_no', int), ('hits', int),
('tot_ns', float), ('err_tot_ns', float), ('delay_ns', float),
('err_delay_ns', float)])
tdc_table=in_file_h5.createTable(in_file_h5.root, 'tdc_data', avg_tab, filters=self.filter_tables)
tdc_table.attrs.repeat_command = repeat_command_dic
thresholds = ()
expfit0 = ()
expfit1 = ()
expfit2 = ()
expfit3 = ()
pixels = ()
for i in range(len(stop) - 1):
s1 = int(stop[i])
s2 = int(stop[i + 1])
A, mu, sigma = analysis.fit_scurve(hits[s1:s2], injections[s1:s2],repeat_command)
if np.max(hits[s1:s2]) > (repeat_command + 200): # or mu > 3000:
thresholds = np.append(thresholds, 0)
expfit0 = np.append(expfit0, 0)
expfit1 = np.append(expfit1, 0)
expfit2 = np.append(expfit2, 0)
expfit3 = np.append(expfit3, 0)
pixels = np.append(pixels, pxl_list[s1])
continue
for values in range(s1, s2):
if injections[values] >= 5 / 4 * mu:
s1 = values
break
numberer = 0
hitvaluesold = hits[-1]
for hitvalues in hits[s1:s2]:
if abs(hitvalues - hitvaluesold) <= 1 and hitvalues != 0:
break
numberer = numberer + 1
hitvaluesold = hitvalues
if numberer == len(avg_del[s1:s2]):
numberer = 0
expfit = analysis.fit_exp(injections[s1:s2], avg_del[s1:s2], mu, abs(numberer))
startexp = -expfit[0] * np.log((25.0 + np.min(avg_del[s1:s2]) - expfit[3]) / expfit[2]) - expfit[1]
if np.isnan(startexp) or startexp >= 2000:
startexp = 0
thresholds = np.append(thresholds, startexp)
expfit0 = np.append(expfit0, expfit[0])
expfit1 = np.append(expfit1, expfit[1])
expfit2 = np.append(expfit2, expfit[2])
expfit3 = np.append(expfit3, expfit[3])
pixels = np.append(pixels, pxl_list[s1])
thresh = np.rec.fromarrays([pixels, thresholds, expfit0, expfit1, expfit2, expfit3],
dtype=[('pixel_no', int), ('td_threshold', float),
('expfit0', float), ('expfit1', float), ('expfit2', float),
('expfit3', float)])
in_file_h5.createTable(in_file_h5.root, 'td_threshold', thresh, filters=self.filter_tables)
p1, p2, single_scan = plotting.plot_timewalk(h5_filename)
output_file(self.output_filename + '.html', title=self.run_name)
status = plotting.plot_status(h5_filename)
save(hplot(vplot(p1, p2, status), single_scan))
values='displ',
agg='mean',
group='origin',
title="label='cyl' values='displ' agg='mean' group='origin'",
legend='top_right')
bar_plot8 = Bar(df,
label='cyl',
values='neg_mpg',
agg='mean',
group='origin',
color='origin',
legend='top_right',
title="label='cyl' values='neg_mpg' agg='mean' group='origin'")
# infer labels from index
df = df.set_index('cyl')
bar_plot9 = Bar(df,
values='mpg',
agg='mean',
legend='top_right',
title='inferred labels')
# collect and display
output_file("bar_multi.html")
show(
vplot(hplot(bar_plot, bar_plot2, bar_plot3),
hplot(bar_plot4, bar_plot5, bar_plot6),
hplot(bar_plot7, bar_plot8, bar_plot9)))
title="label='cyl' color='blue'",
color='blue')
# color by one dimension and label by two dimensions
box_plot5 = BoxPlot(df, label=['cyl', 'origin'], values='mpg',
title="label=['cyl', 'origin'] color='cyl'",
color='cyl')
# specify custom marker for outliers
box_plot6 = BoxPlot(df, label='cyl', values='mpg', marker='cross',
title="label='cyl', values='mpg', marker='cross'")
# color whisker by cylinder
box_plot7 = BoxPlot(df, label='cyl', values='mpg', whisker_color='cyl',
title="label='cyl', values='mpg', whisker_color='cyl'")
# remove outliers
box_plot8 = BoxPlot(df, label='cyl', values='mpg', outliers=False,
title="label='cyl', values='mpg', outliers=False")
# collect and display
output_file("boxplot_multi.html")
show(
vplot(
hplot(box_plot, box_plot2, box_plot3),
hplot(box_plot4, box_plot5, box_plot6),
hplot(box_plot7, box_plot8)
)
)
ch1TotalSubscribers = channel1_data["total_subscribers"]
ch2TotalSubscribers = channel2_data["total_subscribers"]
# Data to be used for graphone ordered into groups
data = {
'Numbers': ['Dislikes', 'Likes', 'Subscribers', 'Total Views', 'Dislikes', 'Likes', 'Subscribers', 'Total Views'],
'Channels': [ch1, ch1, ch1, ch1, ch2, ch2, ch2, ch2],
'Total': [ch1TotalDislikes, ch1TotalLikes, ch1TotalSubscribers, ch1TotalViews, ch2TotalDislikes, ch2TotalLikes, ch2TotalSubscribers, ch2TotalViews]
}
# Puts all the data into a format which may be graphed with correct parameters:
bar = Bar(data, values='Total', label=['Channels', 'Numbers'],agg = 'sum',
title="Comparing two Youtube Channels", width=500, height = 1000,
group = 'Numbers', tools=['hover', 'resize', 'box_zoom', 'wheel_zoom', 'pan'])
# Allows the hover tool to function:
hover = bar.select(dict(type=HoverTool))
hover.tooltips = [('Value of Channel',' $x'),('Value of Total',' @height')]
# outputs a file with the data for the graph:
output_file("stacked_bar.html")
# Shows the graph:
show(hplot(bar))
#
#End of graphing functions code!
#**************************************************************************************
print(channel1_data)
print(channel2_data)
def robots():
def choose_channel():
channel_name = raw_input('Enter a channel name: ')
return channel_name
def retrieve_links(name_of_channel):
root_url = "https://www.youtube.com"
url = urllib2.urlopen("https://www.youtube.com/user/" + name_of_channel + "/videos")
content = url.read()
soup = BeautifulSoup(content, "html.parser")
list_of_links = []
#searches for all 'a' tags that have "/watch" in them.
for elem in soup.find_all('a', href=re.compile('/watch')):
list_of_links.append(root_url + elem['href'])
#This loop gets rid of duplicate links
i = 0
while i < len(list_of_links):
if list_of_links[i] == list_of_links[i+1]:
list_of_links.remove(list_of_links[i])
i += 1
#print("Links for the %d most recent videos:" % len(list_of_links))
#for x in range(len(list_of_links)):
#print(list_of_links[x])
return list_of_links
#This function will go to each link and scrape data of subscribers, views, likes and dislikes
def get_data(list_of_links = []):
data = {}
data['total_views'] = 0
data['total_likes'] = 0
data['total_dislikes'] = 0
data['total_subscribers'] = 0
for x in range(len(list_of_links)):
url = urllib2.urlopen(list_of_links[x])
link_content = url.read()
link_soup = BeautifulSoup(link_content, "html.parser")
#print("Video number: %d" % (x+1))
views = link_soup.find('div', {'class' : 'watch-view-count'})
views = views.get_text()
views = views.replace(",", "")
views = views.replace(" views", "")
data['total_views'] += int(views)
#print("%s views" % views)
likes = link_soup.find('button', {'class' : "yt-uix-button yt-uix-button-size-default yt-uix-button-opacity yt-uix-button-has-icon no-icon-markup like-button-renderer-like-button like-button-renderer-like-button-unclicked yt-uix-clickcard-target yt-uix-tooltip"})
likes = likes.get_text()
likes = likes.replace(",", "")
data['total_likes'] += int(likes)
#print("%s likes" % likes)
dislikes = link_soup.find('button', {'class' : "yt-uix-button yt-uix-button-size-default yt-uix-button-opacity yt-uix-button-has-icon no-icon-markup like-button-renderer-dislike-button like-button-renderer-dislike-button-unclicked yt-uix-clickcard-target yt-uix-tooltip"})
dislikes = dislikes.get_text()
dislikes = dislikes.replace(",", "")
data['total_dislikes'] += int(dislikes)
#print("%s dislikes" % dislikes)
#print(" ")
subscribers = link_soup.find('span', {'class' : "yt-subscription-button-subscriber-count-branded-horizontal yt-subscriber-count"})
subscribers = subscribers.get_text()
subscribers = subscribers.replace(",", "")
data['total_subscribers'] = int(subscribers)
return data
name1 = request.args['example']
name2 = request.args['example2']
ch1 = name1
ch2 = name2
channel1 = retrieve_links(ch1)
channel2 = retrieve_links(ch2)
channel1_data = get_data(channel1)
channel2_data = get_data(channel2)
#*************************************************************************************************
#Graphing functions added here:
# Puts integer values into variables to be used in bokeh graphing:
ch1TotalViews = channel1_data["total_views"]
ch2TotalViews = channel2_data["total_views"]
ch1TotalLikes = channel1_data["total_likes"]
ch2TotalLikes = channel2_data["total_likes"]
ch1TotalDislikes = channel1_data["total_dislikes"]
ch2TotalDislikes = channel2_data["total_dislikes"]
ch1TotalSubscribers = channel1_data["total_subscribers"]
ch2TotalSubscribers = channel2_data["total_subscribers"]
# Data to be used for graphone ordered into groups
data = {
'Numbers': ['Dislikes', 'Likes', 'Subscribers', 'Total Views', 'Dislikes', 'Likes', 'Subscribers', 'Total Views'],
'Channels': [ch1, ch1, ch1, ch1, ch2, ch2, ch2, ch2],
'Total': [ch1TotalDislikes, ch1TotalLikes, ch1TotalSubscribers, ch1TotalViews, ch2TotalDislikes, ch2TotalLikes, ch2TotalSubscribers, ch2TotalViews]
}
# Puts all the data into a format which may be graphed with correct parameters:
bar = Bar(data, values='Total', label=['Channels', 'Numbers'],agg = 'sum',
title="Comparing two Youtube Channels", width=500, height = 1000,
group = 'Numbers', tools=['hover', 'resize', 'box_zoom', 'wheel_zoom', 'pan'])
# Allows the hover tool to function:
hover = bar.select(dict(type=HoverTool))
hover.tooltips = [('Value of Channel',' $x'),('Value of Total',' @height')]
# outputs a file with the data for the graph:
output_file("stacked_bar.html")
# Shows the graph:
show(hplot(bar))
#
#End of graphing functions code!
#**************************************************************************************
return render_template("resultsPage.html")
# multiple columns
dot_plot5 = Dot(df, label=['cyl', 'origin'], values='mpg', agg='mean',
title="label=['cyl', 'origin'] values='mpg' agg='mean'")
dot_plot6 = Dot(df, label='origin', values='mpg', agg='mean', stack='cyl',
title="label='origin' values='mpg' agg='mean' stack='cyl'",
legend='top_right')
dot_plot7 = Dot(df, label='cyl', values='displ', agg='mean', group='origin',
title="label='cyl' values='displ' agg='mean' group='origin'",
legend='top_right')
dot_plot8 = Dot(df, label='cyl', values='neg_mpg', agg='mean', group='origin',
color='origin', legend='top_right',
title="label='cyl' values='neg_mpg' agg='mean' group='origin'")
dot_plot8.title_text_font_size = '11pt'
# infer labels from index
df = df.set_index('cyl')
dot_plot9 = Dot(df, values='mpg', agg='mean', legend='top_right', title='inferred labels')
output_file("dots_multi.html", title="dots_multi.py example")
show(vplot(
hplot(dot_plot, dot_plot2),
hplot(dot_plot3, dot_plot4),
hplot(dot_plot5, dot_plot6),
hplot(dot_plot7, dot_plot8),
hplot(dot_plot9)
))
def tdc_table(self, scanrange):
h5_filename = self.output_filename + '.h5'
with tb.open_file(h5_filename, 'r+') as in_file_h5:
raw_data = in_file_h5.root.raw_data[:]
meta_data = in_file_h5.root.meta_data[:]
if (meta_data.shape[0] == 0): return
repeat_command = in_file_h5.root.meta_data.attrs.kwargs
a = repeat_command.rfind("repeat_command: ")
repeat_command = repeat_command[a + len("repeat_command: "):a +
len("repeat_command: ") + 7]
a = repeat_command.rfind("\n")
repeat_command = int(repeat_command[0:a])
param, index = np.unique(meta_data['scan_param_id'],
return_index=True)
pxl_list = []
for p in param:
pix_no = int(p) / int(len(self.inj_charge))
pxl_list.append(self.pixel_list[pix_no][0] * 64 +
self.pixel_list[pix_no][1])
index = index[1:]
index = np.append(index, meta_data.shape[0])
index = index - 1
stops = meta_data['index_stop'][index]
split = np.split(raw_data, stops)
avg_tdc = []
avg_tdc_err = []
avg_del = []
avg_del_err = []
hits = []
deletelist = ()
for i in range(len(split[:-1])): # loop on pulses
rwa_data_param = split[i]
tdc_data = rwa_data_param & 0xFFF # take last 12 bit
tdc_delay = (rwa_data_param & 0x0FF00000) >> 20
counter = 0.0
TOT_sum = 0.0
DEL_sum = 0.0
if (tdc_data.shape[0] == 0 or tdc_data.shape[0] == 1):
counter = 1.0
for j in range(tdc_data.shape[0]): # loop on repeats
if (j > 0):
counter += 1
TOT_sum += tdc_data[j]
DEL_sum += tdc_delay[j]
if (counter > 1):
hits.append(counter)
avg_tdc.append((float(TOT_sum) / float(counter)) * 1.5625)
avg_tdc_err.append(1.5625 / (np.sqrt(12.0 * counter)))
avg_del.append((float(DEL_sum) / float(counter)) * 1.5625)
avg_del_err.append(1.5625 / (np.sqrt(12.0 * counter)))
else:
deletelist = np.append(deletelist, i)
pxl_list = np.delete(pxl_list, deletelist)
newpix = [0]
pix_no_old = pxl_list[0]
runparam = 0
for p in pxl_list:
if p != pix_no_old:
newpix = np.append(newpix, runparam)
pix_no_old = p
runparam = runparam + 1
addedvalues = 0
for pixels in range(len(newpix)):
missingvalues = 0
if newpix[pixels] == newpix[-1]:
missingvalues = scanrange - abs(newpix[pixels] +
addedvalues - len(hits))
else:
if abs(newpix[pixels] - newpix[pixels + 1]) < scanrange:
missingvalues = scanrange - abs(newpix[pixels] -
newpix[pixels + 1])
if missingvalues != 0:
hits = np.insert(hits, newpix[pixels] + addedvalues,
np.zeros(missingvalues))
avg_tdc = np.insert(avg_tdc, newpix[pixels] + addedvalues,
np.zeros(missingvalues))
avg_tdc_err = np.insert(avg_tdc_err,
newpix[pixels] + addedvalues,
np.zeros(missingvalues))
avg_del = np.insert(avg_del, newpix[pixels] + addedvalues,
np.zeros(missingvalues))
avg_del_err = np.insert(avg_del_err,
newpix[pixels] + addedvalues,
np.zeros(missingvalues))
pxl_list = np.insert(
pxl_list, newpix[pixels] + addedvalues,
(pxl_list[newpix[pixels] + addedvalues]) *
np.ones(missingvalues))
addedvalues = addedvalues + missingvalues
injections = []
for pixels in range(int(len(pxl_list) / len(self.inj_charge))):
for i in range(len(self.inj_charge)):
injections = np.append(injections, self.inj_charge[i])
pix, stop = np.unique(pxl_list, return_index=True)
stop = np.sort(stop)
stop = list(stop)
stop.append(len(avg_tdc))
repeat_command_dic = {}
repeat_command_dic['repeat_command'] = repeat_command
avg_tab = np.rec.fromarrays([
injections, pxl_list, hits, avg_tdc, avg_tdc_err, avg_del,
avg_del_err
],
dtype=[('charge', float),
('pixel_no', int),
('hits', int),
('tot_ns', float),
('err_tot_ns', float),
('delay_ns', float),
('err_delay_ns', float)])
tdc_table = in_file_h5.createTable(in_file_h5.root,
'tdc_data',
avg_tab,
filters=self.filter_tables)
tdc_table.attrs.repeat_command = repeat_command_dic
thresholds = ()
expfit0 = ()
expfit1 = ()
expfit2 = ()
expfit3 = ()
pixels = ()
for i in range(len(stop) - 1):
s1 = int(stop[i])
s2 = int(stop[i + 1])
A, mu, sigma = analysis.fit_scurve(hits[s1:s2],
injections[s1:s2],
repeat_command)
if np.max(
hits[s1:s2]) > (repeat_command + 200): # or mu > 3000:
thresholds = np.append(thresholds, 0)
expfit0 = np.append(expfit0, 0)
expfit1 = np.append(expfit1, 0)
expfit2 = np.append(expfit2, 0)
expfit3 = np.append(expfit3, 0)
pixels = np.append(pixels, pxl_list[s1])
continue
for values in range(s1, s2):
if injections[values] >= 5 / 4 * mu:
s1 = values
break
numberer = 0
hitvaluesold = hits[-1]
for hitvalues in hits[s1:s2]:
if abs(hitvalues - hitvaluesold) <= 1 and hitvalues != 0:
break
numberer = numberer + 1
hitvaluesold = hitvalues
if numberer == len(avg_del[s1:s2]):
numberer = 0
expfit = analysis.fit_exp(injections[s1:s2], avg_del[s1:s2],
mu, abs(numberer))
startexp = -expfit[0] * np.log(
(25.0 + np.min(avg_del[s1:s2]) - expfit[3]) /
expfit[2]) - expfit[1]
if np.isnan(startexp) or startexp >= 2000:
startexp = 0
thresholds = np.append(thresholds, startexp)
expfit0 = np.append(expfit0, expfit[0])
expfit1 = np.append(expfit1, expfit[1])
expfit2 = np.append(expfit2, expfit[2])
expfit3 = np.append(expfit3, expfit[3])
pixels = np.append(pixels, pxl_list[s1])
thresh = np.rec.fromarrays(
[pixels, thresholds, expfit0, expfit1, expfit2, expfit3],
dtype=[('pixel_no', int), ('td_threshold', float),
('expfit0', float), ('expfit1', float),
('expfit2', float), ('expfit3', float)])
in_file_h5.createTable(in_file_h5.root,
'td_threshold',
thresh,
filters=self.filter_tables)
p1, p2, single_scan = plotting.plot_timewalk(h5_filename)
output_file(self.output_filename + '.html', title=self.run_name)
status = plotting.plot_status(h5_filename)
save(hplot(vplot(p1, p2, status), single_scan))
bar_plot4 = Bar(df, label='cyl', title="label='cyl' color='DimGray'", color='dimgray')
# multiple columns
bar_plot5 = Bar(df, label=['cyl', 'origin'], values='mpg', agg='mean',
title="label=['cyl', 'origin'] values='mpg' agg='mean'")
bar_plot6 = Bar(df, label='origin', values='mpg', agg='mean', stack='cyl',
title="label='origin' values='mpg' agg='mean' stack='cyl'",
legend='top_right')
bar_plot7 = Bar(df, label='cyl', values='displ', agg='mean', group='origin',
title="label='cyl' values='displ' agg='mean' group='origin'",
legend='top_right')
bar_plot8 = Bar(df, label='cyl', values='neg_mpg', agg='mean', group='origin',
color='origin', legend='top_right',
title="label='cyl' values='neg_mpg' agg='mean' group='origin'")
# infer labels from index
df = df.set_index('cyl')
bar_plot9 = Bar(df, values='mpg', agg='mean', legend='top_right', title='inferred labels')
output_file("bar_multi.html")
show(vplot(
hplot(bar_plot, bar_plot2, bar_plot3),
hplot(bar_plot4, bar_plot5, bar_plot6),
hplot(bar_plot7, bar_plot8, bar_plot9)
))
label='cyl',
values='displ',
agg='mean',
group='origin',
title="label='cyl' values='displ' agg='mean' group='origin'",
legend='top_right')
dot_plot8 = Dot(df,
label='cyl',
values='neg_mpg',
agg='mean',
group='origin',
color='origin',
legend='top_right',
title="label='cyl' values='neg_mpg' agg='mean' group='origin'")
# infer labels from index
df = df.set_index('cyl')
dot_plot9 = Dot(df,
values='mpg',
agg='mean',
legend='top_right',
title='inferred labels')
output_file("dots_multi.html")
show(
vplot(hplot(dot_plot, dot_plot2, dot_plot3),
hplot(dot_plot4, dot_plot5, dot_plot6),
hplot(dot_plot7, dot_plot8, dot_plot9)))
dot_plot4 = Dot(df, label='cyl', title="label='cyl' color='DimGray'", color='dimgray')
# multiple columns
dot_plot5 = Dot(df, label=['cyl', 'origin'], values='mpg', agg='mean',
title="label=['cyl', 'origin'] values='mpg' agg='mean'")
dot_plot6 = Dot(df, label='origin', values='mpg', agg='mean', stack='cyl',
title="label='origin' values='mpg' agg='mean' stack='cyl'",
legend='top_right')
dot_plot7 = Dot(df, label='cyl', values='displ', agg='mean', group='origin',
title="label='cyl' values='displ' agg='mean' group='origin'",
legend='top_right')
dot_plot8 = Dot(df, label='cyl', values='neg_mpg', agg='mean', group='origin',
color='origin', legend='top_right',
title="label='cyl' values='neg_mpg' agg='mean' group='origin'")
# infer labels from index
df = df.set_index('cyl')
dot_plot9 = Dot(df, values='mpg', agg='mean', legend='top_right', title='inferred labels')
output_file("dots_multi.html")
show(vplot(
hplot(dot_plot, dot_plot2, dot_plot3),
hplot(dot_plot4, dot_plot5, dot_plot6),
hplot(dot_plot7, dot_plot8, dot_plot9)
))
values='mpg',
agg='mean',
title="label=['cyl', 'origin'] values='mpg' agg='mean'")
bar_plot6 = Bar(df,
label='origin',
values='mpg',
agg='mean',
stack='cyl',
title="label='origin' values='mpg' agg='mean' stack='cyl'",
legend='top_right')
bar_plot7 = Bar(df,
label='cyl',
values='displ',
agg='mean',
group='origin',
title="label='cyl' values='displ' agg='mean' group='origin'",
legend='top_right')
# ToDo: negative values
# bar_plot8 = Bar(df, label='cyl', values='neg_displ', agg='mean', group='origin', color='origin',
# title="label='cyl' values='displ' agg='mean' group='origin'", legend='top_right')
# collect and display
output_file("bar.html")
show(
vplot(hplot(bar_plot, bar_plot2, bar_plot3),
hplot(bar_plot4, bar_plot5, bar_plot6), hplot(bar_plot7)))
group='origin',
title="label='cyl' values='displ' agg='mean' group='origin'",
legend='top_right')
bar_plot7.title_text_font_size = '10pt'
bar_plot8 = Bar(df,
label='cyl',
values='neg_mpg',
agg='mean',
group='origin',
color='origin',
legend='top_right',
title="label='cyl' values='neg_mpg' agg='mean' group='origin'")
bar_plot8.title_text_font_size = '9pt'
# infer labels from index
df = df.set_index('cyl')
bar_plot9 = Bar(df,
values='mpg',
agg='mean',
legend='top_right',
title='inferred labels')
bar_plot9.title_text_font_size = '10pt'
output_file("bar_multi.html", title="bar_multi.py example")
show(
vplot(hplot(bar_plot, bar_plot2), hplot(bar_plot3, bar_plot4),
hplot(bar_plot5, bar_plot6), hplot(bar_plot7, bar_plot8),
hplot(bar_plot9)))
# specify custom marker for outliers
box_plot6 = BoxPlot(df,
label='cyl',
values='mpg',
marker='cross',
title="label='cyl', values='mpg', marker='cross'")
# color whisker by cylinder
box_plot7 = BoxPlot(df,
label='cyl',
values='mpg',
whisker_color='cyl',
title="label='cyl', values='mpg', whisker_color='cyl'")
# remove outliers
box_plot8 = BoxPlot(
df,
label='cyl',
values='mpg',
outliers=False,
title="label='cyl', values='mpg', outliers=False, tooltips=True",
tooltips=True)
box_plot8.title_text_font_size = '11pt'
output_file("boxplot_multi.html", title="boxplot_multi.py example")
show(
vplot(hplot(box_plot, box_plot2), hplot(box_plot3, box_plot4),
hplot(box_plot5, box_plot6), hplot(box_plot7, box_plot8)))
def scan_pix_hist(h5_file_name, scurve_sel_pix=200):
with tb.open_file(h5_file_name, 'r') as in_file_h5:
meta_data = in_file_h5.root.meta_data[:]
hit_data = in_file_h5.root.hit_data[:]
en_mask = in_file_h5.root.scan_results.en_mask[:]
Noise_gauss = in_file_h5.root.Noise_results.Noise_pure.attrs.fitdata_noise
Noise_pure = in_file_h5.root.Noise_results.Noise_pure[:]
Thresh_gauss = in_file_h5.root.Thresh_results.Threshold_pure.attrs.fitdata_thresh
Threshold_pure = in_file_h5.root.Thresh_results.Threshold_pure[:]
scan_args = yaml.load(in_file_h5.root.meta_data.attrs.kwargs)
scan_range = scan_args['scan_range']
scan_range_inx = np.arange(scan_range[0], scan_range[1], scan_range[2])
# np.set_printoptions(threshold=np.nan)
param = np.unique(meta_data['scan_param_id'])
ret = []
for i in param:
wh = np.where(hit_data['scan_param_id'] == i) # this can be faster and multi threaded
hd = hit_data[wh[0]]
hits = hd['col'].astype(np.uint16)
hits = hits * 64
hits = hits + hd['row']
value = np.bincount(hits)
value = np.pad(value, (0, 64 * 64 - value.shape[0]), 'constant')
if len(ret):
ret = np.vstack((ret, value))
else:
ret = value
repeat_command = max(ret[-3])
shape = en_mask.shape
ges = 1
for i in range(2):
ges = ges * shape[i]
ret_pure = ()
en_mask = en_mask.reshape(ges)
for n in range(param[-1]):
ret_pure1 = ()
for i in range(ges):
if (str(en_mask[i]) == 'True'):
ret_pure1 = np.append(ret_pure1, ret[n][i])
if n == 0:
ret_pure = ret_pure1
continue
ret_pure = np.vstack((ret_pure, ret_pure1))
ret_pure = ret_pure.astype(int)
s_hist = np.swapaxes(ret_pure, 0, 1)
pix_scan_hist = np.empty((s_hist.shape[1], repeat_command + 10))
for param in range(s_hist.shape[1]):
h_count = np.bincount(s_hist[:, param])
h_count = h_count[:repeat_command + 10]
pix_scan_hist[param] = np.pad(h_count, (0, (repeat_command + 10) - h_count.shape[0]), 'constant')
log_hist = np.log10(pix_scan_hist)
log_hist[~np.isfinite(log_hist)] = 0
data = {
'scan_param': np.ravel(np.indices(pix_scan_hist.shape)[0]),
'count': np.ravel(np.indices(pix_scan_hist.shape)[1]),
'value': np.ravel(log_hist)
}
x = scan_range_inx
px = scurve_sel_pix # 1110 #1539
single_scan = figure(title="Single pixel scan " + str(px), x_axis_label="Injection[V]", y_axis_label="Hits")
single_scan.diamond(x=x, y=s_hist[px], size=5, color="#1C9099", line_width=2)
yf = analysis.scurve(x, 100, Threshold_pure[px], Noise_pure[px])
single_scan.cross(x=x, y=yf, size=5, color="#E6550D", line_width=2)
hist, edges = np.histogram(Threshold_pure, density=False, bins=50)#50
hm1 = HeatMap(data, x='scan_param', y='count', values='value', title='Threshold Heatmap',
palette=Spectral11[::-1], stat=None, plot_width=1000) # , height=4100)
hm1.extra_x_ranges = {
"e": Range1d(start=edges[0] * 1000 * analysis.cap_fac(), end=edges[-1] * 1000 * analysis.cap_fac())}
hm_th = figure(title="Threshold", x_axis_label="pixel #", y_axis_label="threshold [V]",
y_range=(scan_range_inx[0], scan_range_inx[-1]), plot_width=1000)
hm_th.diamond(y=Threshold_pure, x=range(64 * 64), size=1, color="#1C9099", line_width=2)
hm_th.extra_y_ranges = {"e": Range1d(start=scan_range_inx[0] * 1000 * analysis.cap_fac(),
end=scan_range_inx[-1] * 1000 * analysis.cap_fac())}
hm_th.add_layout(LinearAxis(y_range_name="e"), 'right')
plt_th_dist = figure(title='Threshold Distribution ', x_axis_label="threshold [V]", y_axis_label="#Pixel",
y_range=(0, 1.1 * np.max(hist[1:])))
plt_th_dist.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], fill_color="#036564",
line_color="#033649",
legend="# {0:d} mean={1:.2f} std={2:.2f}".format(int(np.sum(hist[:])), round(
Thresh_gauss['mu'] * 1000 * analysis.cap_fac(), 4),
round(Thresh_gauss[
'sigma'] * 1000 * analysis.cap_fac(),
4)))
plt_th_dist.extra_x_ranges = {"e": Range1d(start=edges[0] * 1000 * analysis.cap_fac(),
end=edges[-1] * 1000 * analysis.cap_fac())} # better 7.4?
plt_th_dist.add_layout(LinearAxis(x_range_name="e"), 'above')
plt_th_dist.line(np.arange(edges[1], edges[-1], 0.0001),
analysis.gauss(np.arange(edges[1], edges[-1], 0.0001), Thresh_gauss['height'],
Thresh_gauss['mu'], Thresh_gauss['sigma']), line_color="#D95B43", line_width=8,
alpha=0.7)
hist, edges = np.histogram(Noise_pure, density=False, bins=50) #50
hm_noise = figure(title="Noise", x_axis_label="pixel #", y_axis_label="noise [V]", y_range=(0, edges[-1]),
plot_width=1000)
hm_noise.diamond(y=Noise_pure, x=range(64 * 64), size=2, color="#1C9099", line_width=2)
hm_noise.extra_y_ranges = {"e": Range1d(start=0, end=edges[-1] * 1000 * analysis.cap_fac())} # default 7.6
hm_noise.add_layout(LinearAxis(y_range_name="e"), 'right')
plt_noise_dist = figure(title='Noise Distribution ', x_axis_label="noise [V]", y_axis_label="#Pixel",
y_range=(0, 1.1 * np.max(hist[1:])))
plt_noise_dist.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], fill_color="#036564",
line_color="#033649",
legend="# {0:d} mean={1:.2f} std={2:.2f}".format(int(np.sum(hist[:])), round(
Noise_gauss['mu'] * 1000 * analysis.cap_fac(), 4), round(
Noise_gauss['sigma'] * 1000 * analysis.cap_fac(), 4)))
plt_noise_dist.extra_x_ranges = {
"e": Range1d(start=edges[0] * 1000 * analysis.cap_fac(), end=edges[-1] * 1000 * analysis.cap_fac())}
plt_noise_dist.add_layout(LinearAxis(x_range_name="e"), 'above')
plt_noise_dist.line(np.arange(edges[0], edges[-1], 0.0001),
analysis.gauss(np.arange(edges[0], edges[-1], 0.0001), Noise_gauss['height'],
Noise_gauss['mu'], Noise_gauss['sigma']), line_color="#D95B43", line_width=8,
alpha=0.7)
return vplot(hplot(hm_th, plt_th_dist), hplot(hm_noise, plt_noise_dist), hplot(hm1, single_scan)), s_hist
title="label='cyl' color='blue'",
color='blue')
# color by one dimension and label by two dimensions
box_plot5 = BoxPlot(df, label=['cyl', 'origin'], values='mpg',
title="label=['cyl', 'origin'] color='cyl'",
color='cyl')
# specify custom marker for outliers
box_plot6 = BoxPlot(df, label='cyl', values='mpg', marker='cross',
title="label='cyl', values='mpg', marker='cross'")
# color whisker by cylinder
box_plot7 = BoxPlot(df, label='cyl', values='mpg', whisker_color='cyl',
title="label='cyl', values='mpg', whisker_color='cyl'")
# remove outliers
box_plot8 = BoxPlot(df, label='cyl', values='mpg', outliers=False,
title="label='cyl', values='mpg', outliers=False, tooltips=True",
tooltips=True)
box_plot8.title_text_font_size = '11pt'
output_file("boxplot_multi.html", title="boxplot_multi.py example")
show(vplot(
hplot(box_plot, box_plot2),
hplot(box_plot3, box_plot4),
hplot(box_plot5, box_plot6),
hplot(box_plot7, box_plot8)
) )
scatter4 = Scatter(
df, x='mpg', y='hp', color='cyl', marker='origin', title="x='mpg', y='hp', color='cyl', marker='origin'",
xlabel="Miles Per Gallon", ylabel="Horsepower", legend='top_right')
# Example with nested json/dict like data, which has been pre-aggregated and pivoted
df2 = df_from_json(data)
df2 = df2.sort('total', ascending=False)
df2 = df2.head(10)
df2 = pd.melt(df2, id_vars=['abbr', 'name'])
scatter5 = Scatter(
df2, x='value', y='name', color='variable', title="x='value', y='name', color='variable'",
xlabel="Medals", ylabel="Top 10 Countries", legend='bottom_right')
scatter6 = Scatter(flowers, x=blend('petal_length', 'sepal_length', name='length'),
y=blend('petal_width', 'sepal_width', name='width'), color='species',
title='x=petal_length+sepal_length, y=petal_width+sepal_width, color=species',
legend='top_right')
output_file("scatter_multi.html")
show(vplot(
hplot(scatter0, scatter1),
hplot(scatter2, scatter3),
hplot(scatter4, scatter5),
hplot(scatter6)
))
def single_timewalk_plot(h5_filename):
output_file('/home/mark/Desktop/Stuff/single.html')
p1, p2, single_scan = plot_timewalk2(h5_filename)
save(hplot(vplot(p1, p2), single_scan))
xlabel="Miles Per Gallon", ylabel="Horsepower", legend='top_right')
scatter4 = Scatter(
df, x='mpg', y='hp', color='cyl', marker='origin', title="x='mpg', y='hp', color='cyl', marker='origin'",
xlabel="Miles Per Gallon", ylabel="Horsepower", legend='top_right')
# Example with nested json/dict like data, which has been pre-aggregated and pivoted
df2 = df_from_json(data)
df2 = df2.sort('medals.total', ascending=False)
df2 = df2.head(10)
df2 = pd.melt(df2, id_vars=['abbr', 'name'])
scatter5 = Scatter(
df2, x='value', y='name', color='variable', title="x='value', y='name', color='variable'",
xlabel="Medals", ylabel="Top 10 Countries", legend='bottom_right')
scatter6 = Scatter(flowers, x=blend('petal_length', 'sepal_length', name='length'),
y=blend('petal_width', 'sepal_width', name='width'), color='species',
title='x=petal_length+sepal_length, y=petal_width+sepal_width, color=species',
legend='top_right')
output_file("scatter_multi.html")
show(vplot(
hplot(scatter0, scatter1),
hplot(scatter2, scatter3),
hplot(scatter4, scatter5),
hplot(scatter6)
))
bar_plot3 = Bar(df, label='cyl', values='mpg', agg='mean',
title="label='cyl' values='mpg' agg='mean'")
bar_plot4 = Bar(df, label='cyl', title="label='cyl' color='DimGray'", color='dimgray')
# multiple columns
bar_plot5 = Bar(df, label=['cyl', 'origin'], values='mpg', agg='mean',
title="label=['cyl', 'origin'] values='mpg' agg='mean'")
bar_plot6 = Bar(df, label='origin', values='mpg', agg='mean', stack='cyl',
title="label='origin' values='mpg' agg='mean' stack='cyl'", legend='top_right')
bar_plot7 = Bar(df, label='cyl', values='displ', agg='mean', group='origin',
title="label='cyl' values='displ' agg='mean' group='origin'", legend='top_right')
# ToDo: negative values
# bar_plot8 = Bar(df, label='cyl', values='neg_displ', agg='mean', group='origin', color='origin',
# title="label='cyl' values='displ' agg='mean' group='origin'", legend='top_right')
# collect and display
output_file("bar.html")
show(
vplot(
hplot(bar_plot, bar_plot2, bar_plot3),
hplot(bar_plot4, bar_plot5, bar_plot6),
hplot(bar_plot7)
)
)
from bokeh.charts import Histogram, defaults, vplot, hplot, show, output_file
from bokeh.sampledata.autompg import autompg as df
defaults.plot_width = 400
defaults.plot_height = 350
# input options
hist = Histogram(df['mpg'], title="df['mpg']")
hist2 = Histogram(df, 'displ', title="df, 'displ'")
hist3 = Histogram(df, values='hp', title="df, values='hp'")
hist4 = Histogram(df,
values='hp',
color='cyl',
title="df, values='hp', color='cyl'",
legend='top_right')
hist5 = Histogram(df, values='mpg', bins=50, title="df, values='mpg', bins=50")
output_file("histogram_multi.html", title="histogram_multi.py example")
show(vplot(hplot(hist, hist2), hplot(hist3, hist4), hplot(hist5)))
("Sample Size", "@count")
])
# Kernel Density plot
p1 = figure(title=ttle+" Distribution",tools="save,box_zoom,reset")
hist, edges = np.histogram(county_data[var_], density=True, bins=20)
gkde=gaussian_kde(county_data[var_])
ind = np.linspace(0,county_data[var_].max(),101)
kdepdf = gkde.evaluate(ind)
p1.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],
fill_color="#036564", line_color="#033649")
p1.line(ind, kdepdf, line_color="#D95B43", line_width=4, alpha=1)
p = hplot(map_,p1)
show(p)
# ### Scatter plot matrix
# In[5]:
# Read farm level data
farm_data = pd.read_csv("/home/jared/work/milk_hauling_price/data/combined_data.csv")
cwt_data = pd.read_csv("/home/jared/work/milk_hauling_price/data/raw_data.csv")[['farmerID','CWT Shipped']]
# In[6]:
from bokeh.charts import Histogram
from bokeh.sampledata.autompg import autompg as df
from bokeh.charts import defaults, vplot, hplot, show, output_file
defaults.width = 450
defaults.height = 350
# input options
hist = Histogram(df['mpg'], title="df['mpg']")
hist2 = Histogram(df, 'displ', title="df, 'displ'")
hist3 = Histogram(df, values='hp', title="df, values='hp'")
hist4 = Histogram(df,
values='hp',
color='cyl',
title="df, values='hp', color='cyl'",
legend='top_right')
hist5 = Histogram(df, values='mpg', bins=50, title="df, values='mpg', bins=50")
output_file("histograms.html")
show(vplot(hplot(hist, hist2, hist3), hplot(hist4, hist5)))
# In[2]:
from bokeh.sampledata.autompg import autompg as df
from bokeh.charts import Histogram, output_notebook, show, hplot
# print df
output_notebook()
# In[16]:
hist = Histogram(df, values='mpg', title="Auto MPG Histogram", width=400)
hist2 = Histogram(df, values='displ', label='cyl', color='cyl', legend='top_right',
title="MPG Histogram by Cylinder Count", width=400)
show(hplot(hist, hist2))
# In[5]:
# Modules needed from Bokeh.
from bokeh.io import output_file, show
from bokeh.plotting import figure
from bokeh.models import LinearAxis, Range1d
# Seting the params for the first figure.
s1 = figure(x_axis_type="datetime",plot_width=1000,
plot_height=600)
# Setting the second y axis range name and range
s1.extra_y_ranges = {"foo": Range1d(start=-100, end=200)}
bar_plot6 = Bar(df, label='origin', values='mpg', agg='mean', stack='cyl',
title="label='origin' values='mpg' agg='mean' stack='cyl'",
legend='top_right')
bar_plot6.title_text_font_size = '10pt'
bar_plot7 = Bar(df, label='cyl', values='displ', agg='mean', group='origin',
title="label='cyl' values='displ' agg='mean' group='origin'",
legend='top_right')
bar_plot7.title_text_font_size = '10pt'
bar_plot8 = Bar(df, label='cyl', values='neg_mpg', agg='mean', group='origin',
color='origin', legend='top_right',
title="label='cyl' values='neg_mpg' agg='mean' group='origin'")
bar_plot8.title_text_font_size = '9pt'
# infer labels from index
df = df.set_index('cyl')
bar_plot9 = Bar(df, values='mpg', agg='mean', legend='top_right', title='inferred labels')
bar_plot9.title_text_font_size = '10pt'
output_file("bar_multi.html", title="bar_multi.py example")
show(vplot(
hplot(bar_plot, bar_plot2),
hplot(bar_plot3, bar_plot4),
hplot(bar_plot5, bar_plot6),
hplot(bar_plot7, bar_plot8),
hplot(bar_plot9)
))
title="Interpreters (x='date', y, dash=['python', 'pypy', 'jython'])",
ylabel='Duration',
legend=True)
line3 = Line(
df,
x='date',
y=['python', 'pypy', 'jython'],
dash=['python', 'pypy', 'jython'],
color=['python', 'pypy', 'jython'],
title=
"Interpreters (x='date', y, dash, color=['python', 'pypy', 'jython'])",
ylabel='Duration',
legend=True)
line4 = Line(
df,
x='date',
y=['python', 'pypy', 'jython'],
dash='test',
color=['python', 'pypy', 'jython'],
title=
"Interpreters (x='date', y, color=['python', 'pypy', 'jython'], dash='test')",
ylabel='Duration',
legend=True)
output_file("line_multi.html", title="line examples")
show(vplot(hplot(line), hplot(line0, line1), hplot(line2, line3),
hplot(line4)))
np_stacked = Bar(
random, cat=categories, title="Numpy Array input | Stacked",
ylabel="Random Number", xlabel="", width=width, height=height,
stacked=True
)
np_negative_grouped = Bar(
random * -1, cat=categories, title="All negative input | Grouped",
ylabel="Random Number", width=width, height=height
)
np_custom = Bar(
mixed, cat=categories, title="Custom range (start=-3, end=0.4)",
ylabel="Random Number", width=width, height=height,
continuous_range=Range1d(start=-3, end=0.4)
)
np_mixed_grouped = Bar(
mixed, cat=categories, title="Mixed-sign input | Grouped",
ylabel="Random Number", width=width, height=height
)
# collect and display
output_file("bar.html")
show(vplot(
hplot(dict_stacked, df_grouped),
hplot(np_stacked, np_negative_grouped),
hplot(np_mixed_grouped, np_custom),
))
int_vhover.line_policy = 'interp'
iphover = int_point_line.select(HoverTool)
iphover.mode = 'mouse'
iphover.line_policy = 'interp'
tphover = point_line.select(HoverTool)
tphover.mode = 'mouse'
shover = scatter.select(HoverTool)
shover.mode = 'vline'
shoverp = scatter_point.select(HoverTool)
shoverp.mode = 'mouse'
# set up tooltips
int_vhover.tooltips = int_hhover.tooltips = TOOLTIPS
tphover.tooltips = iphover.tooltips = TOOLTIPS
shover.tooltips = shoverp.tooltips = TOOLTIPS
vhover.tooltips = hhover.tooltips = TOOLTIPS
output_file("hover_span.html", title="hover_span.py example")
show(
vplot(
hplot(hline, vline),
hplot(int_hline, int_vline),
hplot(int_point_line, point_line),
hplot(scatter_point, scatter),
))
from bokeh.charts import BoxPlot, output_file, show
from bokeh.sampledata.autompg import autompg as df
from bokeh.charts import defaults, vplot, hplot
defaults.width = 450
defaults.height = 350
box_plot = BoxPlot(df, label='cyl', values='mpg', title="label='cyl', values='mpg'")
box_plot2 = BoxPlot(df, label=['cyl', 'origin'], values='mpg', title="label=['cyl', 'origin'], values='mpg'")
box_plot3 = BoxPlot(df, label='cyl', values='mpg', agg='mean',
title="label='cyl' values='mpg' agg='mean'")
box_plot4 = BoxPlot(df, label='cyl', title="label='cyl' color='DimGray'", color='dimgray')
# collect and display
output_file("boxplot.html")
show(
vplot(
hplot(box_plot, box_plot2),
hplot(box_plot3, box_plot4),
)
)
df2 = df_from_json(data)
df2 = df2.sort('total', ascending=False)
df2 = df2.head(10)
df2 = pd.melt(df2, id_vars=['abbr', 'name'])
scatter5 = Scatter(df2,
x='value',
y='name',
color='variable',
title="x='value', y='name', color='variable'",
xlabel="Medals",
ylabel="Top 10 Countries",
legend='bottom_right')
scatter6 = Scatter(
flowers,
x=blend('petal_length', 'sepal_length', name='length'),
y=blend('petal_width', 'sepal_width', name='width'),
color='species',
title=
'x=petal_length+sepal_length, y=petal_width+sepal_width, color=species',
legend='top_right')
scatter6.title_text_font_size = '10pt'
output_file("scatter_multi.html", title="scatter_multi.py example")
show(
vplot(hplot(scatter0, scatter1), hplot(scatter2, scatter3),
hplot(scatter4, scatter5), hplot(scatter6)))
def getFeature():
if request.method == 'GET':
return render_template('feature.html')
else:
bronx=[[2009,'https://data.cityofnewyork.us/resource/en2c-j6tw.json'],[2010,'https://data.cityofnewyork.us/resource/n2s5-fumm.json'],[2011,'https://data.cityofnewyork.us/resource/bawj-6bgn.json'],[2012,'https://data.cityofnewyork.us/resource/3qfc-4tta.json']]
brooklyn=[[2009,'https://data.cityofnewyork.us/resource/rmv8-86p4.json'],[2010,'https://data.cityofnewyork.us/resource/w6yt-hctp.json'],[2011,'https://data.cityofnewyork.us/resource/5mw2-hzqx.json'],[2012,'https://data.cityofnewyork.us/resource/bss9-579f.json']]
manhattan=[[2009,'https://data.cityofnewyork.us/resource/956m-xy24.json'],[2010,'https://data.cityofnewyork.us/resource/ad4c-mphb.json'],[2011,'https://data.cityofnewyork.us/resource/ikqj-pyhc.json'],[2012,'https://data.cityofnewyork.us/resource/dvzp-h4k9.json']]
queens=[[2009,'https://data.cityofnewyork.us/resource/m59i-mqex.json'],[2010,'https://data.cityofnewyork.us/resource/crbs-vur7.json'],[2011,'https://data.cityofnewyork.us/resource/s3zn-tf7c.json'],[2012,'https://data.cityofnewyork.us/resource/jcih-dj9q.json']]
statenIsland=[[2009,'https://data.cityofnewyork.us/resource/cyfw-hfqk.json'],[2010,'https://data.cityofnewyork.us/resource/wv4q-e75v.json'],[2011,'https://data.cityofnewyork.us/resource/a5qt-5jpu.json'],[2012,'https://data.cityofnewyork.us/resource/tkdy-59zg.json']]
featureNames=[['comparable_rental_2_market_value_per_sqft', 'Market value per square foot'],['comparable_rental_2_full_market_value', 'Full market value'],['comparable_rental_2_year_built', 'Year Built'],['comparable_rental_2_gross_income_per_sqft', 'Gross income per square foot']]
#request was a POST (get the var from the form)
#common feature
app_xplor.vars['feat'] = request.form['feat']
#groups A and B
app_xplor.vars['boA'] = request.form['boA']
app_xplor.vars['boB'] = request.form['boB']
app_xplor.vars['yA'] = request.form['yA']
app_xplor.vars['yB'] = request.form['yB']
#Translating name of the feature into the name in the original database
dbFeatureName = convertField(app_xplor.vars['feat'],featureNames)
#group A
if (app_xplor.vars['boA']=='Bronx'):
queryA = buildQuery(int(app_xplor.vars['yA']),bronx,dbFeatureName)
elif (app_xplor.vars['boA']=='Brooklyn'):
queryA = buildQuery(int(app_xplor.vars['yA']),brooklyn,dbFeatureName)
elif (app_xplor.vars['boA']=='Manhattan'):
queryA = buildQuery(int(app_xplor.vars['yA']),manhattan,dbFeatureName)
elif (app_xplor.vars['boA']=='Queens'):
queryA = buildQuery(int(app_xplor.vars['yA']),queens,dbFeatureName)
elif (app_xplor.vars['boA']=='Staten Island'):
queryA = buildQuery(int(app_xplor.vars['yA']),statenIsland,dbFeatureName)
#group B
if (app_xplor.vars['boB']=='Bronx'):
queryB = buildQuery(int(app_xplor.vars['yB']),bronx,dbFeatureName)
elif (app_xplor.vars['boB']=='Brooklyn'):
queryB = buildQuery(int(app_xplor.vars['yB']),brooklyn,dbFeatureName)
elif (app_xplor.vars['boB']=='Manhattan'):
queryB = buildQuery(int(app_xplor.vars['yB']),manhattan,dbFeatureName)
elif (app_xplor.vars['boB']=='Queens'):
queryB = buildQuery(int(app_xplor.vars['yB']),queens,dbFeatureName)
elif (app_xplor.vars['boB']=='Staten Island'):
queryB = buildQuery(int(app_xplor.vars['yB']),statenIsland,dbFeatureName)
rawA = pd.read_json(queryA)
rawB = pd.read_json(queryB)
allData = pd.concat([rawA, rawB], axis=1)
cleanData= allData.dropna()
cleanData.columns=['A','B']
#plot
defaults.width = 450
defaults.height = 350
tA=str(app_xplor.vars['boA'])+'/'+str(app_xplor.vars['yA']) + '/' + str(app_xplor.vars['feat'])
tB=str(app_xplor.vars['boB'])+'/'+str(app_xplor.vars['yB']) + '/' + str(app_xplor.vars['feat'])
histA = Histogram(cleanData['A'], title=tA)
histB = Histogram(cleanData['B'], title=tB)
output_file("templates/results.html")
show(
vplot(
hplot(histA,histB)
)
)
return redirect('/goFeat')
line1 = Line(df, x='date', y=['python', 'pypy', 'jython'],
title="Interpreters (x='date', y=['python', 'pypy', 'jython'])", ylabel='Duration', legend=True)
line2 = Line(df, x='date', y=['python', 'pypy', 'jython'],
dash=['python', 'pypy', 'jython'],
title="Interpreters (x='date', y, dash=['python', 'pypy', 'jython'])", ylabel='Duration', legend=True)
line3 = Line(df, x='date', y=['python', 'pypy', 'jython'],
dash=['python', 'pypy', 'jython'],
color=['python', 'pypy', 'jython'],
title="Interpreters (x='date', y, dash, color=['python', 'pypy', 'jython'])", ylabel='Duration', legend=True)
line4 = Line(df, x='date', y=['python', 'pypy', 'jython'],
dash='test',
color=['python', 'pypy', 'jython'],
title="Interpreters (x='date', y, color=['python', 'pypy', 'jython'], dash='test')", ylabel='Duration',
legend=True)
output_file("lines.html", title="line.py example")
show(
vplot(
hplot(line0, line1),
hplot(line2, line3),
hplot(line4)
)
)
values='displ',
agg='mean',
group='origin',
title="label='cyl' values='displ' agg='mean' group='origin'",
legend='top_right')
dot_plot8 = Dot(df,
label='cyl',
values='neg_mpg',
agg='mean',
group='origin',
color='origin',
legend='top_right',
title="label='cyl' values='neg_mpg' agg='mean' group='origin'")
dot_plot8.title_text_font_size = '11pt'
# infer labels from index
df = df.set_index('cyl')
dot_plot9 = Dot(df,
values='mpg',
agg='mean',
legend='top_right',
title='inferred labels')
output_file("dots_multi.html", title="dots_multi.py example")
show(
vplot(hplot(dot_plot, dot_plot2), hplot(dot_plot3, dot_plot4),
hplot(dot_plot5, dot_plot6), hplot(dot_plot7, dot_plot8),
hplot(dot_plot9)))
from bokeh.charts import Histogram
from bokeh.sampledata.autompg import autompg as df
from bokeh.charts import defaults, vplot, hplot, show, output_file
defaults.width = 450
defaults.height = 350
# input options
hist = Histogram(df['mpg'], title="df['mpg']")
hist2 = Histogram(df, 'displ', title="df, 'displ'")
hist3 = Histogram(df, values='hp', title="df, values='hp'")
hist4 = Histogram(df, values='hp', color='cyl',
title="df, values='hp', color='cyl'", legend='top_right')
hist5 = Histogram(df, values='mpg', bins=50,
title="df, values='mpg', bins=50")
output_file("histograms.html")
show(
vplot(
hplot(hist, hist2, hist3),
hplot(hist4, hist5)
)
)
def scan_pix_hist(h5_file_name, scurve_sel_pix = 200):
with tb.open_file(h5_file_name, 'r') as in_file_h5:
meta_data = in_file_h5.root.meta_data[:]
hit_data = in_file_h5.root.hit_data[:]
scan_args = yaml.load(in_file_h5.root.meta_data.attrs.kwargs)
scan_range = scan_args['scan_range']
scan_range_inx = np.arange(scan_range[0], scan_range[1], scan_range[2])
repeat_command = scan_args['repeat_command']
np.set_printoptions(threshold=np.nan)
k = 5
param = np.unique(meta_data['scan_param_id'])
ret = []
for i in param:
wh = np.where(hit_data['scan_param_id'] == i) #this can be faster and multi threaded
hd = hit_data[wh[0]]
hits = hd['col'].astype(np.uint16)
hits = hits * 64
hits = hits + hd['row']
value = np.bincount(hits)
value = np.pad(value, (0, 64*64 - value.shape[0]), 'constant')
if len(ret):
ret = np.vstack((ret, value))
else:
ret = value
s_hist = np.swapaxes(ret,0,1)
indices = np.indices(s_hist.shape)
param_inx = np.ravel(indices[1].astype(np.float64))#*0.05 - 0.6)
param_inx_string = param_inx.astype('|S5')
pix_scan_hist = np.empty((s_hist.shape[1],repeat_command + 10))
for param in range(s_hist.shape[1]):
h_count = np.bincount(s_hist[:,param])
h_count = h_count[:repeat_command+10]
pix_scan_hist[param] = np.pad(h_count, (0, (repeat_command + 10) - h_count.shape[0]), 'constant')
log_hist = np.log10(pix_scan_hist)
log_hist[~np.isfinite(log_hist)] = 0
data = {
'scan_param': np.ravel(np.indices(pix_scan_hist.shape)[0]),
'count': np.ravel(np.indices(pix_scan_hist.shape)[1]),
'value': np.ravel(log_hist)
}
hm1 = HeatMap(data, x='scan_param', y='count', values='value', title='s-scans', palette=Spectral11[::-1], stat=None, plot_width=1000) #, height=4100)
mean = np.empty(64*64)
noise = np.empty(64*64)
x = scan_range_inx
for pix in range (64*64):
mu, sigma = analysis.fit_scurve(s_hist[pix], x) #this can multi threaded
mean[pix] = mu
noise[pix] = sigma
px = scurve_sel_pix #1110 #1539
single_scan = figure(title="Single pixel scan " + str(px) )
single_scan.diamond(x=x, y=s_hist[px], size=5, color="#1C9099", line_width=2)
yf = analysis.scurve(x, 100, mean[px], noise[px])
single_scan.cross(x=x, y=yf, size=5, color="#E6550D", line_width=2)
mean[mean > scan_range_inx[-1]] = 0
hist, edges = np.histogram(mean, density=True, bins=50)
#print "sigma:",noise
hm_th = figure(title="Threshold", x_axis_label = "pixel #", y_axis_label = "threshold [V]", y_range=(scan_range_inx[0], scan_range_inx[-1]), plot_width=1000)
hm_th.diamond(y=mean, x=range(64*64), size=1, color="#1C9099", line_width=2)
hm_th.extra_y_ranges = {"e": Range1d(start=scan_range_inx[0]*1000*7.6, end=scan_range_inx[-1]*1000*7.6)}
hm_th.add_layout(LinearAxis(y_range_name="e"), 'right')
barray = fit_gauss(edges[2:], hist[1:])
plt_th_dist = figure(title= 'Threshold Distribution ' + '\sigma = ' + str(round(barray[2],4)) + ' \mu =' + str(round(barray[1],4)), x_axis_label = "threshold [V]", y_range=(0, 1.1*np.max(hist[1:])))
plt_th_dist.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], fill_color="#036564", line_color="#033649",)
plt_th_dist.extra_x_ranges = {"e": Range1d(start=edges[0]*1000*7.6, end=edges[-1]*1000*7.6)}
plt_th_dist.add_layout(LinearAxis(x_range_name="e"), 'above')
plt_th_dist.line(np.arange(edges[1], edges[50], 0.0001),
gauss(np.arange(edges[1], edges[50], 0.0001), barray[0], barray[1], barray[2]), line_color="#D95B43",
line_width=8, alpha=0.7)
noise[noise > 0.02] = 0.02 #this should be done based on 6sigma?
hist, edges = np.histogram(noise, density=True, bins=50)
hm_noise = figure(title="Noise", x_axis_label = "pixel #", y_axis_label = "noise [V]", y_range=(0, edges[-1]), plot_width=1000)
hm_noise.diamond(y=noise, x=range(64*64), size=2, color="#1C9099", line_width=2)
hm_noise.extra_y_ranges = {"e": Range1d(start=0, end=edges[-1]*1000*7.6)}
hm_noise.add_layout(LinearAxis(y_range_name="e"), 'right')
gauss_params_noise = fit_gauss(edges[5:], hist[4:])
plt_noise_dist = figure(title='Noise Distribution ' + '\sigma = ' + str(round(gauss_params_noise[2],4)) + ' \mu = ' + str(round(gauss_params_noise[1],4)) , x_axis_label = "noise [V]", y_range=(0, 1.1*np.max(hist[1:])))
plt_noise_dist.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], fill_color="#036564", line_color="#033649",)
plt_noise_dist.extra_x_ranges = {"e": Range1d(start=edges[0]*1000*7.6, end=edges[-1]*1000*7.6)}
plt_noise_dist.add_layout(LinearAxis(x_range_name="e"), 'above')
plt_noise_dist.line(np.arange(edges[1],edges[50],0.0001),gauss(np.arange(edges[1],edges[50],0.0001),gauss_params_noise[0],gauss_params_noise[1],gauss_params_noise[2]),line_color="#D95B43", line_width=8, alpha=0.7)
return vplot(hplot(hm_th, plt_th_dist), hplot(hm_noise,plt_noise_dist), hplot(hm1, single_scan) ), s_hist
int_vhover.line_policy = 'interp'
iphover = int_point_line.select(dict(type=HoverTool))
iphover.mode = 'mouse'
iphover.line_policy = 'interp'
tphover = point_line.select(dict(type=HoverTool))
tphover.mode = 'mouse'
shover = scatter.select(dict(type=HoverTool))
shover.mode = 'vline'
shoverp = scatter_point.select(dict(type=HoverTool))
shoverp.mode = 'mouse'
int_vhover.tooltips = int_hhover.tooltips = tphover.tooltips = iphover.tooltips = shover.tooltips = shoverp.tooltips = vhover.tooltips = hhover.tooltips = OrderedDict([
("y", "$~y"),
("x", "$~x"),
# ("data_x", "$~x"),
# ("data_y", "$~y"),
])
show(
vplot(
hplot(hline, vline),
hplot(int_hline, int_vline),
hplot(int_point_line, point_line),
hplot(scatter_point, scatter),
)
)
def scan_pix_hist(h5_file_name, scurve_sel_pix=200):
with tb.open_file(h5_file_name, 'r') as in_file_h5:
meta_data = in_file_h5.root.meta_data[:]
hit_data = in_file_h5.root.hit_data[:]
en_mask = in_file_h5.root.scan_results.en_mask[:]
Noise_gauss = in_file_h5.root.Noise_results.Noise_pure.attrs.fitdata_noise
Noise_pure = in_file_h5.root.Noise_results.Noise_pure[:]
Thresh_gauss = in_file_h5.root.Thresh_results.Threshold_pure.attrs.fitdata_thresh
Threshold_pure = in_file_h5.root.Thresh_results.Threshold_pure[:]
scan_args = yaml.load(in_file_h5.root.meta_data.attrs.kwargs)
scan_range = scan_args['scan_range']
scan_range_inx = np.arange(scan_range[0], scan_range[1], scan_range[2])
# np.set_printoptions(threshold=np.nan)
param = np.unique(meta_data['scan_param_id'])
ret = []
for i in param:
wh = np.where(hit_data['scan_param_id'] == i) # this can be faster and multi threaded
hd = hit_data[wh[0]]
hits = hd['col'].astype(np.uint16)
hits = hits * 64
hits = hits + hd['row']
value = np.bincount(hits)
value = np.pad(value, (0, 64 * 64 - value.shape[0]), 'constant')
if len(ret):
ret = np.vstack((ret, value))
else:
ret = value
repeat_command = max(ret[-3])
shape = en_mask.shape
ges = 1
for i in range(2):
ges = ges * shape[i]
ret_pure = ()
en_mask = en_mask.reshape(ges)
for n in range(param[-1]):
ret_pure1 = ()
for i in range(ges):
if (str(en_mask[i]) == 'True'):
ret_pure1 = np.append(ret_pure1, ret[n][i])
if n == 0:
ret_pure = ret_pure1
continue
ret_pure = np.vstack((ret_pure, ret_pure1))
ret_pure = ret_pure.astype(int)
s_hist = np.swapaxes(ret_pure, 0, 1)
pix_scan_hist = np.empty((s_hist.shape[1], repeat_command + 10))
for param in range(s_hist.shape[1]):
h_count = np.bincount(s_hist[:, param])
h_count = h_count[:repeat_command + 10]
pix_scan_hist[param] = np.pad(h_count, (0, (repeat_command + 10) - h_count.shape[0]), 'constant')
log_hist = np.log10(pix_scan_hist)
log_hist[~np.isfinite(log_hist)] = 0
data = {
'scan_param': np.ravel(np.indices(pix_scan_hist.shape)[0]),
'count': np.ravel(np.indices(pix_scan_hist.shape)[1]),
'value': np.ravel(log_hist)
}
x = scan_range_inx
px = scurve_sel_pix # 1110 #1539
single_scan = figure(title="Single pixel scan " + str(px), x_axis_label="Injection[V]", y_axis_label="Hits")
single_scan.diamond(x=x, y=s_hist[px], size=5, color="#1C9099", line_width=2)
yf = analysis.scurve(x, 100, Threshold_pure[px], Noise_pure[px])
single_scan.cross(x=x, y=yf, size=5, color="#E6550D", line_width=2)
hist, edges = np.histogram(Threshold_pure, density=False, bins=50)
hm1 = HeatMap(data, x='scan_param', y='count', values='value', title='Threshold Heatmap',
palette=Spectral11[::-1], stat=None, plot_width=1000) # , height=4100)
hm1.extra_x_ranges = {
"e": Range1d(start=edges[0] * 1000 * analysis.cap_fac(), end=edges[-1] * 1000 * analysis.cap_fac())}
hm_th = figure(title="Threshold", x_axis_label="pixel #", y_axis_label="threshold [V]",
y_range=(scan_range_inx[0], scan_range_inx[-1]), plot_width=1000)
hm_th.diamond(y=Threshold_pure, x=range(64 * 64), size=1, color="#1C9099", line_width=2)
hm_th.extra_y_ranges = {"e": Range1d(start=scan_range_inx[0] * 1000 * analysis.cap_fac(),
end=scan_range_inx[-1] * 1000 * analysis.cap_fac())}
hm_th.add_layout(LinearAxis(y_range_name="e"), 'right')
plt_th_dist = figure(title='Threshold Distribution ', x_axis_label="threshold [V]", y_axis_label="#Pixel",
y_range=(0, 1.1 * np.max(hist[1:])))
plt_th_dist.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], fill_color="#036564",
line_color="#033649",
legend="# {0:d} mean={1:.2f} std={2:.2f}".format(int(np.sum(hist[:])), round(
Thresh_gauss['mu'] * 1000 * analysis.cap_fac(), 4),
round(Thresh_gauss[
'sigma'] * 1000 * analysis.cap_fac(),
4)))
plt_th_dist.extra_x_ranges = {"e": Range1d(start=edges[0] * 1000 * analysis.cap_fac(),
end=edges[-1] * 1000 * analysis.cap_fac())} # better 7.4?
plt_th_dist.add_layout(LinearAxis(x_range_name="e"), 'above')
plt_th_dist.line(np.arange(edges[1], edges[-1], 0.0001),
analysis.gauss(np.arange(edges[1], edges[-1], 0.0001), Thresh_gauss['height'],
Thresh_gauss['mu'], Thresh_gauss['sigma']), line_color="#D95B43", line_width=8,
alpha=0.7)
hist, edges = np.histogram(Noise_pure, density=False, bins=50)
hm_noise = figure(title="Noise", x_axis_label="pixel #", y_axis_label="noise [V]", y_range=(0, edges[-1]),
plot_width=1000)
hm_noise.diamond(y=Noise_pure, x=range(64 * 64), size=2, color="#1C9099", line_width=2)
hm_noise.extra_y_ranges = {"e": Range1d(start=0, end=edges[-1] * 1000 * analysis.cap_fac())} # default 7.6
hm_noise.add_layout(LinearAxis(y_range_name="e"), 'right')
plt_noise_dist = figure(title='Noise Distribution ', x_axis_label="noise [V]", y_axis_label="#Pixel",
y_range=(0, 1.1 * np.max(hist[1:])))
plt_noise_dist.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], fill_color="#036564",
line_color="#033649",
legend="# {0:d} mean={1:.2f} std={2:.2f}".format(int(np.sum(hist[:])), round(
Noise_gauss['mu'] * 1000 * analysis.cap_fac(), 4), round(
Noise_gauss['sigma'] * 1000 * analysis.cap_fac(), 4)))
plt_noise_dist.extra_x_ranges = {
"e": Range1d(start=edges[0] * 1000 * analysis.cap_fac(), end=edges[-1] * 1000 * analysis.cap_fac())}
plt_noise_dist.add_layout(LinearAxis(x_range_name="e"), 'above')
plt_noise_dist.line(np.arange(edges[0], edges[-1], 0.0001),
analysis.gauss(np.arange(edges[0], edges[-1], 0.0001), Noise_gauss['height'],
Noise_gauss['mu'], Noise_gauss['sigma']), line_color="#D95B43", line_width=8,
alpha=0.7)
return vplot(hplot(hm_th, plt_th_dist), hplot(hm_noise, plt_noise_dist), hplot(hm1, single_scan)), s_hist
