def plotOGimages(waiting_file, bed_file, covid_file):
print("Generating OG images ...", end=" ")
makeOGfile()
# Load data
waiting_data = np.load(waiting_file, allow_pickle=True)
bed_data = np.load(bed_file, allow_pickle=True)
covid_data = np.load(covid_file, allow_pickle=True)
# Combine names
bed_names = proc.capitaliseFirst(bed_data[0])
covid_names = proc.capitaliseFirst(covid_data[0])
allNames = proc.combineNames(waiting_data[0], bed_names)
allNames = proc.combineNames(allNames, covid_names)
for name in allNames:
figName = proc.makeFigureName(name, "og", "png")
fig = makeOGimage(name, waiting_data, bed_data, covid_data)
if fig != None:
fig.savefig("figures/og/{}".format(figName),
bbox_inches='tight',
pad_inches=0)
plt.close(fig)
print("Done.")
def plotBedData(data):
''' Plot the number of beds at NHS England Trusts'''
print("Generating graphs for the number of beds ...", end=" ")
# Load data
NHSdata = np.load(data, allow_pickle=True)
names, dates, beds = NHSdata
# format name to match waiting data
names = proc.capitaliseFirst(names)
#### Plot and save the data ####
# List of old trusts which have since merged into something else
oldTrusts = proc.get_all_dict_values(mergered_trusts)
for i, name in enumerate(names[:]):
if type(name) == str and not (name in oldTrusts):
mask = (beds[i, :] != '-')
if name in mergered_trusts.keys() or sum(mask) >= 4:
figName = proc.makeFigureName(name, "beds", "svg")
fig = plotBeds(name, NHSdata)
fig.savefig("figures/{}".format(figName), bbox_inches='tight')
plt.close()
#### Plot trust change pie chart ####
more, same, fewer = proc.bed_change_per_trust(names, beds, mergered_trusts)
plt.figure(figsize=(6, 4))
labels = 'Fewer Beds', 'Same*', 'More Beds'
sizes = [fewer, same, more]
colors = ['lightcoral', 'lightgray', 'yellowgreen']
explode = (0.05, 0.0, 0.0) # explode 1st slice
# Plot
plt.pie(sizes,
explode=explode,
labels=labels,
colors=colors,
autopct='%1.1f%%',
shadow=True,
startangle=140)
plt.axis('equal')
plt.tight_layout()
plt.annotate("* change smaller than 50 beds.", (0.2, -1.2),
size=13,
color="gray")
plt.savefig("figures/BedsPieChart.svg")
plt.close()
print("Done.")
def plotBeds(name, NHSdata):
if name in mergered_trusts.keys():
fig = plotMergedBedData(name, NHSdata)
else:
names, dates, all_beds = NHSdata
beds = all_beds[names == name][0]
dates = dates[beds != "-"]
beds = beds[beds != "-"]
# format name to match waiting data
names = proc.capitaliseFirst(names)
# rescale large numbers to be in thousands
if max(beds) > 1000:
rescale = 1 / 1000
else:
rescale = 1
ylabel = "# of Overnight Beds" + "\n(Thousands)" * (rescale
== 1 / 1000)
fig = plt.figure(figsize=(6, 4))
if min(beds) > 300 and (max(beds) - min(beds)) < min(beds) / 3:
bax = brokenaxes(ylims=((0, 0.005 * max(beds) * rescale),
(0.95 * min(beds) * rescale,
1.02 * max(beds) * rescale)),
hspace=0.08)
bax.set_ylabel(ylabel, labelpad=50)
else:
bax = fig.add_subplot(111)
bax.set_ylim(0, 1.1 * max(beds) * rescale)
bax.set_ylabel(ylabel)
bax.bar(dates, beds * rescale, width=0.18, color=NHSblue)
xup = int(np.ceil(max(dates))) + 1
xdown = int(np.floor(min(dates)))
xint = range(xdown, xup,
2 * (xup - xdown > 5) + 1 * (xup - xdown <= 5))
bax.set_xticks(xint)
return fig
def MakeHomepage(waiting_data, bed_data):
print("Building homepage...", end=" ")
# Load data
names1, _, _, waiting = np.load(waiting_data, allow_pickle=True)
names2, _, beds = np.load(bed_data, allow_pickle=True)
names2 = pd.capitaliseFirst(names2)
allNames = pd.combineNames(names1, names2)
#print(type(allNames[0]))
# Make list of hospital names
hospitalLinksList = []
for i, name in enumerate(allNames):
# Check if the trust is in contained in the values of merged_trust
# i.e. is it an old trust which has since merged into something else.
oldTrust = name in pd.get_all_dict_values(mergered_trusts)
if not oldTrust:
ane_points, bed_points = 0, 0
merged = name in mergered_trusts.keys()
if name in names1:
ane_points = len(
waiting[names1 == name][waiting[names1 == name] != '-'])
if name in names2:
bed_points = len(
beds[names2 == name][beds[names2 == name] != '-'])
if ane_points >= 10 or bed_points >= 4 or merged:
url = makeURL(name)
hospitalLinksList.append(
"<li><a href=\"{}\">{}</a></li>\n".format(url, name))
hospitalLinks = ''.join(hospitalLinksList)
file = open("index.html", "w")
file.write(''.join([homeHTML1, hospitalLinks, homeHTML2, footer]))
file.close()
print("Done")
def plotCovidData(datafile):
''' Plot daily covid deaths'''
print("Generating Covid-19 graphs...", end=" ")
# Load data
data = np.load(datafile, allow_pickle=True)
names = data[0]
names = proc.capitaliseFirst(names)
### Plot and save the data ###
for i, name in enumerate(names[:]):
figName = proc.makeFigureName(name, "covid", "svg")
fig = makeCovidGraph(name, data)
fig.savefig("figures/{}".format(figName))
plt.close(fig)
print("Done.")
def makeOGimage(name, waiting_data, bed_data, covid_data):
matplotlib.rc('font', size=18)
# load data
ane_names, dates, attendance, waiting = waiting_data
bed_names, dates, beds = bed_data
bed_names = proc.capitaliseFirst(bed_names)
covid_names, dates, deaths = covid_data
covid_names = proc.capitaliseFirst(covid_names)
AnEblock, bedblock, covidblock = whichChunks(name, ane_names, bed_names,
covid_names, waiting, beds,
covid_data)
#print(AnEblock, bedblock)
if not (AnEblock or bedblock):
return None
# Make temp files
makeOGtempImgs(name, waiting_data, bed_data, covid_data, AnEblock,
bedblock, covidblock)
canvas = np.zeros((630, 1200, 4))
# solid pale blue canvas
canvas[:, :, 1] = 0.4
canvas[:, :, 2] = 1
canvas[:, :, 3] = 0.1
fig = plt.figure(figsize=(12, 6.3), dpi=100)
ax1 = fig.add_axes((0, 0, 1, 1))
images = []
# Make and save the figures as png files
if AnEblock:
img = imread('og_temp_AnE.png')
img = addBorder(img)
images.append(img)
if bedblock:
img = imread('og_temp_bed.png')
img = addBorder(img)
images.append(img)
if covidblock:
img = imread('og_temp_covid.png')
img = addBorder(img)
images.append(img)
if len(images) == 1:
# Only one plot
img1 = images[0]
y1 = 140
x1 = 260
canvas[y1:img1.shape[0] + y1, x1:img1.shape[1] + x1, :] = img1
ax1.imshow(canvas)
elif len(images) > 1:
# Two plots
num_images = len(images)
# pixels from the top
yup = 210
ydown = 50
ys = np.linspace(yup, ydown, num_images, dtype='int')
xup = 310
xdown = 70
xs = np.linspace(xup, xdown, num_images, dtype='int')
#print(xs)
for i in range(num_images):
canvas[ys[i]:images[i].shape[0] + ys[i],
xs[i]:images[i].shape[1] + xs[i], :] = images[i]
ax1.imshow(canvas)
else:
return None
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_axis_off()
fig = addLogo(fig)
# delete temp files
deleteTempOGFiles()
return fig
def build_trust_pages(waiting_data, beds_data, covid_data, news_file):
print("Building trust pages...", end=" ")
# Load data
names1, dates, attendance, waiting = np.load(waiting_data,
allow_pickle=True)
names2, dates, beds = np.load(beds_data, allow_pickle=True)
names3, dates, covid_deaths = np.load(covid_data, allow_pickle=True)
# Format and combine all names
names2 = pd.capitaliseFirst(names2)
names3 = pd.capitaliseFirst(names3)
allNames = pd.combineNames(names1, names2)
allNames = pd.combineNames(allNames, names3)
# Load news
newsDict = news.makeNewsDictionary(allNames, news_file)
# list of old trusts
oldTrusts = pd.get_all_dict_values(mergered_trusts)
for i, name in enumerate(allNames):
#print(name)
AnEblock, bedblock, covidblock = whichChunks(name, names1, names2,
names3, attendance, beds,
covid_deaths)
if (AnEblock or bedblock or covidblock) and (name not in oldTrusts):
url = makeURL(name)
file = open(url, "w")
if name == "England":
subTitle = "NHS England Overview"
else:
subTitle = name
meta_HTML = generate_meta(name, AnEblock, bedblock)
subTitleHTML = '''
<div class = \"box\">
\n<h2 class = \"subtitle\">{}</h2>\n'''.format(subTitle)
tab_HTML = '<div class="tab">' + \
"<button class=\"tablinks\" onclick=\"openCity(event, 'AnE')\" id=\"defaultOpen\">A&E</button>"*AnEblock + \
"<button class=\"tablinks\" onclick=\"openCity(event, 'beds')\" id=\"defaultOpen\">Beds</button>"*bedblock + \
"<button class=\"tablinks\" onclick=\"openCity(event, 'covid')\" id=\"defaultOpen\">Covid-19</button>"*covidblock + \
"<button class=\"tablinks\" onclick=\"openCity(event, 'news')\" id=\"defaultOpen\">News</button></div>"
supTextHTML = ""
if AnEblock:
supTextHTML += "<div id=\"AnE\" class=\"tabcontent\">" + make_AnE_waiting_block(
waiting_data, name) + "</div>\n"
if bedblock:
supTextHTML += "<div id=\"beds\" class=\"tabcontent\">" + make_bed_block(
beds_data, name) + "</div>\n"
if covidblock:
supTextHTML += "<div id=\"covid\" class=\"tabcontent\">" + makeCovidBlock(
covid_data, name) + "</div>\n"
supTextHTML += news.makeNewsBlock(name, newsDict)
supTextHTML += "</div>\n"
file.write(''.join([
headHTML1,
headHTML2.format(name, meta_HTML), subTitleHTML, tab_HTML,
supTextHTML, footer2, tab_script
]))
file.close()
print("Done.")
def makeCovidBlock(covid_data, name):
names, dates, covid_deaths = np.load(covid_data, allow_pickle=True)
names = pd.capitaliseFirst(names)
#print(name, covid_deaths[names == name].shape)
englandDeaths = covid_deaths[names == "England"][0]
totalEnglandDeaths = sum(englandDeaths)
weeksEnglandDeaths = sum(englandDeaths[-7::])
trustDeaths = covid_deaths[names == name][0]
totalTrustDeaths = sum(trustDeaths)
weeksTrustDeaths = sum(trustDeaths[-7::])
# were/was and death/deaths
were_was = "were" * (trustDeaths[-1] != 1) + "was" * (trustDeaths[-1] == 1)
death_deaths = "death" + "s" * (trustDeaths[-1] != 1)
# image html
figName = pd.makeFigureName(name, "covid", "svg")
path = "../figures/{}".format(figName)
imgHTML = "<center><img src=\"{}\" alt=\"{}\"></center>".format(
path, "Number of Covid-19 related deaths for {}.".format(name))
# todays date
todayDate = date.today()
today = "{}/{}/{}".format(todayDate.day, todayDate.month, todayDate.year)
compare_to_total = '''
<p>
So far, there have been a total of {} Covid-19 related deaths in English hospitals.
{} of these {} recorded yesterday and {} over the last seven days. <span style="color:#999999;">Last updated: {}</span>.
'''.format(format_number(int(totalEnglandDeaths)),
format_number(int(englandDeaths[-1])), were_was,
format_number(int(weeksEnglandDeaths)), today)
if name == "England":
first_para = '''<p>
Since the start of the pandemic, there have been {} reported deaths related to Covid-19 in English hospitals.
There {} {} {} recorded yesterday and {} over the last seven days. <span style="color:#999999;">Last updated: {}</span>.
</p>'''.format(format_number(int(totalEnglandDeaths)), were_was,
format_number(int(englandDeaths[-1])), death_deaths,
format_number(int(weeksEnglandDeaths)), today)
elif totalTrustDeaths == 0:
first_para = '''
<p>
Fortunatlely, there have not yet been any reported deaths in hospital related to Covid-19 at this Trust.
</p>'''
elif totalTrustDeaths == 1:
first_para = '''
<p>
There has been one death related to Covid-19 reported at a hospital under this Trust.
</p>'''
elif totalTrustDeaths > 1:
first_para = '''
<p>
Unfortunately, since the start of the pandemic in March, there have been {} reported deaths related to Covid-19 at in hospitals under this Trust. There {} {} {} recorded yesterday and a total of {} over the last seven days.
</p>'''.format(format_number(int(totalTrustDeaths)), were_was,
format_number(int(trustDeaths[-1])), death_deaths,
format_number(int(weeksTrustDeaths)))
please_note = '''
<p>Please note that these figures, particularly the most recent ones, are subject to change as new reports are made. Additionally, these figures do not include deaths outside hospital, such as those in care homes. For additional information, please see the <a href="https://www.england.nhs.uk/statistics/statistical-work-areas/covid-19-daily-deaths/">official NHS page</a> for Covid-19 death statistics.
</p>
'''
call_to_action = '''
<a href="..\whatnext.html" style = "text-decoration: none;"><div class="action">Call to Action</div></a>
'''
chunk = first_para + compare_to_total * (
name !=
"England") + imgHTML + please_note + covid_failures + call_to_action
return chunk
def make_bed_block(beds_data, name):
''' Generates the chunk of HTML relating to the A&E waiting time data for NHS trust <name>.
'''
names, dates, beds = np.load(beds_data, allow_pickle=True)
names = pd.capitaliseFirst(names)
i = np.where(names == name)[0]
figName = pd.makeFigureName(name, "beds", "svg")
path = "../figures/{}".format(figName)
imgHTML = "<center><img src=\"{}\" alt=\"{}\"></center>".format(
path, "Number of available overnight beds for {}.".format(name))
england_bed_change = beds[0][-1] - beds[0][0]
england_bed_change_perc = (beds[0][-1] - beds[0][0]) / beds[0][0] * 100
trusts_with_more, _, trust_with_fewer = pd.bed_change_per_trust(
names, beds, mergered_trusts)
# Calculate fractional change
if name not in mergered_trusts.keys():
mask = beds[i][0] != "-"
num_change = beds[i][0][mask][0] - beds[i][0][mask][-1]
change = num_change / beds[i][0][mask][-1]
else:
# Combine merged trust data
totalBeds = pd.combineBedData(beds, names, name, mergered_trusts)
mask = totalBeds != "-"
# calculate change
num_change = totalBeds[mask][0] - totalBeds[mask][-1]
change = num_change / totalBeds[mask][-1]
if i == 0:
chunk = england_beds.format(format_number(-num_change),
format_number(-num_change), imgHTML,
int(trust_with_fewer),
int(trusts_with_more), brexit_et_al)
elif change < -0.05 and change > -1:
start_date = int(np.floor(dates[mask][-1]))
percentage_change = abs(change) * 100
chunk = beds_worse.format(name, format_number(-num_change), start_date,
int(percentage_change), imgHTML,
england_bed_change,
int(england_bed_change_perc),
int(trust_with_fewer), int(trusts_with_more),
brexit_et_al)
elif change == -1:
# All of the beds are gone
start_date = int(np.floor(dates[mask][-1]))
chunk = beds_all_gone.format(name, format_number(-num_change),
start_date, imgHTML, england_bed_change,
int(england_bed_change_perc),
int(trust_with_fewer),
int(trusts_with_more), brexit_et_al)
elif change > 0.05:
# Things are better
start_date = int(np.floor(dates[mask][-1]))
chunk = beds_better.format(name, int(trusts_with_more), start_date,
format_number(num_change), imgHTML,
england_bed_change,
int(england_bed_change_perc),
int(trust_with_fewer),
int(trusts_with_more), brexit_et_al)
else:
# Not much change
#print(name)
start_date = int(np.floor(dates[mask][-1]))
if num_change > 1:
more_or_less = "{} more beds".format(format_number(
abs(num_change)))
elif num_change == 1:
more_or_less = "exactly 1 more bed"
elif num_change < -1:
more_or_less = "{} fewer beds".format(
format_number(abs(num_change)))
elif num_change == -1:
more_or_less = "exactly 1 less bed"
else:
more_or_less = "exactly the same number of beds"
chunk = beds_little_change.format(name, more_or_less, start_date,
imgHTML, england_bed_change,
int(england_bed_change_perc),
int(trust_with_fewer),
int(trusts_with_more), brexit_et_al)
return chunk