def graph():
#Bar chart for CPU usage
bar_chart = pygal.Bar(width=500,
height=400,
explicit_size=True,
range=(0, 100))
bar_chart.title = 'CPU usage per core (%)'
for i, v in cpu.items():
bar_chart.add('Core' + str(i), v)
chart = bar_chart.render_data_uri()
#Pie chart for disk usage
pie_chart = pygal.Pie(width=500, height=400, explicit_size=True)
pie_chart.title = 'Disk usage (GB)'
for key, value in disk_usage.items():
pie_chart.add(key, value)
dchart = pie_chart.render_data_uri()
#H-bar for memory and swap memory
hbar = pygal.HorizontalBar(width=500, height=400, explicit_size=True)
hbar.title = 'Memory usage (GB)'
for key, value in pmemory.items():
hbar.add(key, value)
hbarchart = hbar.render_data_uri()
hbarswap = pygal.HorizontalBar(width=500, height=400, explicit_size=True)
hbarswap.title = 'Swap memory usage (GB)'
for key, value in pswap.items():
hbarswap.add(key, value)
hbarswapchart = hbarswap.render_data_uri()
return render_template('graphs.html',
chart=chart,
dchart=dchart,
hbarchart=hbarchart,
hbarswapchart=hbarswapchart)
def index():
city_path = "../city.json"
keyword_path = "..keyword.json"
data_city = getData(city_path)
data_keyword = getData(keyword_path)
title = '51job职位分析'
line_charCity = pygal.HorizontalBar()
line_charCity.title = 'Python需求最高的十个城市'
for item in data_city:
line_charCity.add(item[0], item[1])
line_charKeyword = pygal.HorizontalBar()
line_charKeyword.title = 'Python相关职位中提到最多的十项技能'
for item in data_keyword:
line_charKeyword.add(item[0], item[1])
html = """
<html>
<head>
<title>%s</title>
</head>
<body>
%s
<br>
%s
</body>
</html>
""" % (title, line_char.render(is_unicode=True),
line_char2.render(is_unicode=True))
return html
def queryFileIncreInfo(ip, user, password, command):
timeList = []
valfilesList = []
valsizeList = []
client = ssh.SSHClient()
client.set_missing_host_key_policy(ssh.AutoAddPolicy())
client.connect(ip, port=22, username=user, password=password)
stdin, stdout, stderr = client.exec_command(command)
out = stdout.read()
rows = out.split("\n")
for row in rows[3::]:
if (row.endswith('+') == False):
cols = row.split("|")
if (len(cols) > 3):
timeList.append(cols[1])
valsizeList.append(float(cols[2]))
valfilesList.append(float(cols[3]))
line_chart6 = pygal.HorizontalBar(width=650, height=800)
line_chart6.x_labels = timeList
line_chart6.add('File Count', valsizeList)
line_chart6.render_to_png('num_of_files.png')
line_chart7 = pygal.HorizontalBar(width=650, height=800)
line_chart7.x_labels = timeList
line_chart7.add('Total size(G)', valfilesList)
line_chart7.render_to_png('total_size_gb.png')
def region_cpu_instances(region_in):
"""
Flask route for resource counts per region
"""
kwargs = {}
kwargs[u'width'] = 900
kwargs[u'height'] = 400
kwargs[u'explicit_size'] = True
kwargs[u'print_values'] = True
kwargs[u'style'] = self.custom_style
title = u'Resource Usage for %s' % region_in
# create instances and cpu chart
resource_chart = pygal.HorizontalBar(title=title, **kwargs)
for instance, instance_count in instances_by_region[
region_in].items():
resource_chart.add(instance, instance_count)
for cpu, cpu_count in cpus_by_region[region_in].items():
resource_chart.add(cpu, cpu_count)
# create memory and disk chart
mem_disk_chart = pygal.HorizontalBar(title=title, **kwargs)
for mem, mem_count in mem_by_region[region_in].items():
mem_disk_chart.add(mem, float(mem_count))
for disk, disk_count in disk_by_region[region_in].items():
mem_disk_chart.add(disk, disk_count)
context = {}
context[u'title'] = title
context[u'resource_chart'] = resource_chart
context[u'mem_disk_chart'] = mem_disk_chart
return render_template('region.html',
region_name=region_in,
**context)
def stat():
items = get_models()
line_chart1 = pygal.HorizontalBar()
line_chart1.title = 'Companies'
line_chart2 = pygal.HorizontalBar()
line_chart2.title = 'Manufacturies'
pie_chart3 = pygal.Pie()
pie_chart3.title = 'OS'
pie_chart4 = pygal.Pie()
pie_chart4.title = 'Countries'
data1 = dict()
data2 = dict()
data3 = dict()
data4 = dict()
for item in items:
if item.Company not in data1:
data1[item.Company] = 0
data1[item.Company] += 1
if item.Manufacture not in data2:
data2[item.Manufacture] = 0
data2[item.Manufacture] += 1
if item.OS not in data3:
data3[item.OS] = 0
data3[item.OS] += 1
for item in data1.keys():
line_chart1.add(item, data1[item])
for item in data2.keys():
line_chart2.add(item, data2[item])
for item in data3.keys():
pie_chart3.add(item, data3[item])
items = get_companies()
for item in items:
if item.country not in data4:
data4[item.country] = 0
data4[item.country] += 1
for item in data4.keys():
pie_chart4.add(get_location(item).country, data4[item])
str1 = line_chart1.render_data_uri()
str2 = line_chart2.render_data_uri()
str3 = pie_chart3.render_data_uri()
str4 = pie_chart4.render_data_uri()
return render_template(
"stat.html",
graph_data1=str1,
graph_data2=str2,
graph_data3=str3,
graph_data4=str4,
year=datetime.now().year,
)
def airquality_april_to_april():
df_apr_2019 = pd.DataFrame(
parse_bcn_airquality_data_file("2019_04_Abril_qualitat_aire_BCN.csv"))
df_apr_2019 = df_apr_2019.groupby(['date', 'type'],
as_index=False)['daily_avg'].mean()
df_apr_2019 = pd.pivot_table(df_apr_2019,
index='date',
columns='type',
values='daily_avg',
fill_value=0)
df_apr_2020 = pd.DataFrame(
parse_bcn_airquality_data_file("2020_04_Abril_qualitat_aire_BCN.csv"))
df_apr_2020 = df_apr_2020.groupby(['date', 'type'],
as_index=False)['daily_avg'].mean()
df_apr_2020 = pd.pivot_table(df_apr_2020,
index='date',
columns='type',
values='daily_avg',
fill_value=0)
# Build and render charts
dates = df_apr_2020.index.tolist()
# style = pygal.style.LightSolarizedStyle()
# NO2
chart = pygal.HorizontalBar(
title=u'Barcelona Air Quality - NO2 Apr 2019 vs 2020',
x_title="micrograms per cubic meter air µg/m³",
x_label_rotation=20,
show_dots=False,
style=style)
chart.x_labels = dates
chart.add('Apr 2019', df_apr_2019['NO2'])
chart.add('Apr 2020', df_apr_2020['NO2'])
chart.render_to_file(OUTPUT_DIR + 'airquality_apr19-20-NO2.svg')
# PM10
chart = pygal.HorizontalBar(
title=u'Barcelona Air Quality - PM10 Apr 2019 vs 2020',
x_title="micrograms per cubic meter air µg/m³",
x_label_rotation=20,
show_dots=False,
style=style)
chart.x_labels = dates
chart.add('Apr 2019', df_apr_2019['PM10'])
chart.add('Apr 2020', df_apr_2020['PM10'])
chart.render_to_file(OUTPUT_DIR + 'airquality_apr19-20-PM10.svg')
def pygal_bar_horizontal(data, chartname, file_name):
bar_chart = pygal.HorizontalBar()
bar_chart = pygal.HorizontalBar(print_values=True,
style=DefaultStyle(
value_font_family='googlefont:Raleway',
value_font_size=30,
value_colors=('white', )))
bar_chart.title = chartname
for ds in data:
for key, value in ds.items():
bar_chart.add(key, value)
bar_chart.render_to_file(file_name + '.svg')
bar_chart.render_to_png(file_name + '.png')
return True
def plot_guj_1(df):
t = df['Detected District'].value_counts()[:10]
line_chart = pygal.HorizontalBar()
line_chart.title = 'Cities with Highest Cases'
for i in range(len(t)):
line_chart.add(t.index[i], t[i])
line_chart.render_to_file(paths['guj'])
def bar_graph(session, sq, percentage=True, sort=False):
"""
Generate a generic Bar graph from a SurveyQuestions
"""
results = get_sq_results(session, sq, percentage=percentage, sort=sort)
count, avg_r, min_r, max_r = get_sq_stats(session, sq)
if percentage:
x_legend = "%"
else:
x_legend = "count"
title = sq.text.replace("?", "?\n")
if sq.question.type == "Multiple choice":
stats = f"Stats: {avg_r} avg, max {max_r}"
chart_title = f"NetDevOps Survey ({sq.survey_id})\n{title}\n{stats}"
else:
chart_title = f"NetDevOps Survey ({sq.survey_id})\n{title}"
chart = pygal.HorizontalBar(
legend_at_bottom=False,
width=1920,
height=1080,
title=chart_title,
style=Style(colors=COLORS, **style_args),
x_title=x_legend,
)
chart.show_legend = False
chart.x_labels = map(str, list(results.index))
chart.add(sq.survey.id, list(results["value"]))
return chart
def make_graphs(dist_dict,r):
line_chart = pygal.Line(width=1200, height=600,explicit_size=True,disable_xml_declaration=True)
line_chart.title = 'Pattern of derailment from planned trip v/s stop number'
line_chart.x_title = "Stop number"
line_chart.y_title = "Distance in meters"
for i in dist_dict.keys():
line_chart.add(str(i), list(dist_dict[i].values()))
line_chart.x_labels = map(str, range(1, r))
total_avg_dist_dict={}
#Graph 2
for i in dist_dict.keys():
total_dist=0
for j in list(dist_dict[i].values()):
total_dist+=float(j)
total_dist=total_dist/len(list(dist_dict[i].values()))
total_avg_dist_dict[i]=total_dist
bar_chart = pygal.HorizontalBar(width=1200, height=600,explicit_size=True,disable_xml_declaration=True)
bar_chart.title = 'Average distance from planned position v/s trip numbers'
bar_chart.y_title = "Trip ID"
bar_chart.x_title = "Distance in meters"
for i in total_avg_dist_dict.keys():
bar_chart.add(str(i), total_avg_dist_dict[i])
return line_chart,bar_chart
def make_time_graph(time_dict):
line_chart = pygal.Line(width=1200, height=600,explicit_size=True,disable_xml_declaration=True)
line_chart.title = 'Time difference between planned and actual time at a stop on a given trip'
line_chart.x_title = "Dots represent stops"
line_chart.y_title = "Time in minutes"
for i in time_dict.keys():
line_chart.add(str(i), list(time_dict[i].values()))
line_chart.x_labels = map(str,[])
total_avg_time_dict={}
for i in time_dict.keys():
total_time=0
for j in list(time_dict[i].values()):
total_time+=int(j)
total_time=total_time/len(list(time_dict[i].values()))
total_avg_time_dict[i]=total_time
bar_chart = pygal.HorizontalBar(width=1200, height=600,explicit_size=True,disable_xml_declaration=True)
bar_chart.title = 'Average time delay in a trip'
bar_chart.y_title = "Trip ID"
bar_chart.x_title = "Time in minutes"
for i in total_avg_time_dict.keys():
bar_chart.add(str(i), total_avg_time_dict[i])
return line_chart,bar_chart
def features_visualization(features):
'''
features - a dictionary of track features
prints a bar chart of selected features (on the same scale of 0-1)
'''
feature_dict = copy.deepcopy(features)
# exclude some features
for feature in [
'time_signature', 'mode', 'loudness', 'key', 'tempo',
'duration_ms', 'analysis_url', 'id', 'track_href', 'type', 'uri'
]:
del feature_dict[feature]
# customize chart style
custom_style = Style(background='rgba(253, 247, 246, 0.4)',
plot_background='rgba(249, 228, 225, 1)',
foreground='rgba(0, 0, 0, 0.9)',
foreground_strong='rgba(0, 0, 0, 0.9)',
foreground_subtle='rgba(0, 0, 0, 0.5)',
opacity='.6',
opacity_hover='.9',
colors=('#d94e4c', '#e5884f', '#39929a',
lighten('#d94e4c', 10), darken('#39929a', 15),
lighten('#e5884f',
17), darken('#d94e4c',
10), '#234547'))
# create chart
chart = pygal.HorizontalBar(style=custom_style)
chart.title = 'Track Features'
for feature, value in feature_dict.items():
chart.add(feature, value)
chart.render()
return chart.render_data_uri()
def chart(publications):
pubs = publications.values('meshHeadings')
#Data Generation and Formatting
g = []
for y in pubs:
text = re.sub('[^A-Za-z, ]', '',
str(y['meshHeadings'].split(", ")))
g.append(text)
counts = []
for i in g:
i = i.split(", ")
counts.append(i)
thingy = {}
for a in counts:
for b in a:
if b in thingy:
thingy[b] += 1
else:
thingy[b] = 1
x = dict(Counter(thingy).most_common(10))
#Graph Creation
bar_chart = pygal.HorizontalBar(
legend_at_bottom=True,
title=u'Top Research Interests by MeSH Headings',
x_title='Frequency',
y_title='MeSH Heading',
print_values=True,
print_labels=True,
style=BlueStyle(value_font_family='googlefont:Raleway',
value_font_size=30,
value_colors=('white', )))
for y in x:
bar_chart.add(y, x[y])
bar_chart.render_to_file('./searchFunction/static/mesh_chart.svg')
def main():
all_url = []
queue = Queue.Queue()
firsturl = raw_input('请输入音乐分类：')
firsturl = urllib.quote(firsturl)
first_url = 'http://music.163.com/discover/playlist/?cat=' + firsturl + '&order=hot'
second_url = 'http://music.163.com/discover/playlist/?order=hot&cat=' + firsturl + '&limit=35&offset='
all_url.append(first_url)
for i in range(42):
last_url = second_url + (str((i + 1) * 35))
all_url.append(last_url)
for url in all_url:
queue.put(url)
for i in range(10):
t = GetAllcolum(queue)
t.setDaemon(True)
t.start()
queue.join()
# 制作成条形图（svg格式）
line_chart = pygal.HorizontalBar()
line_chart.title = u"网易云 "
for i in range(len(injuries)):
if injuries[i] > 100:
line_chart.add(province[i], injuries[i])
line_chart.render_to_file('data_file/music_hot_cato.svg')
def open_data_progress():
client = pymongo.MongoClient(os.environ['MONGO_CASES_URI'])
db = client.va_circuit_court_cases
data = db.case_numbers.aggregate([{
'$sort': SON([('court', 1), ('name', 1)])
}, {
'$group': {
'_id': {
'court': '$court'
},
'firstName': {
'$first': '$name'
},
'lastName': {
'$last': '$name'
},
'count': {
'$sum': 1
}
}
}, {
'$sort': SON([('_id.court', 1)])
}])['result']
chart = pygal.HorizontalBar(style=pygal.style.RedBlueStyle,
show_legend=False,
height=1500)
chart.x_labels = [x['_id']['court'] for x in data][::-1]
chart.add('', [x['count'] for x in data][::-1])
return chart.render() + str(
render_template('open_data_progress.html', data=data))
def main(reasons):
chart = pygal.HorizontalBar()
chart.title = 'Pikabu banned users statistics'
for case in reasons:
chart.add(case, reasons[case])
chart.render_to_file('data.svg')
SVG(filename='data.svg')
def horizBar(name, graphdata):
# Create horizontal bar chart object and set title
chart = pygal.HorizontalBar(show_legend=False,
style=custom_style,
x_label_rotation=30,
y_labels_major_every=1000)
#chart.title = name
# Turn data into respective data and label array
x_labels = []
x_data = []
for data in graphdata:
x_labels.append(data[0])
diction = {'value': data[1]}
if len(data) > 2:
diction['xlink'] = str(data[2])
x_data.append(diction)
# Insert the data
chart.add('', x_data)
chart.x_labels = x_labels
# Export to an svg file
#chart.render_to_file('CarRentalCompany/templates/CarRentalCompany/Charts/' + name + '.svg')
return chart.render(is_unicode=True)
def send_graph(bot, update):
global comandi
global chiamate
global graph_file_counter
chiamate[comandi.index("graph")] += 1
graph_file_counter = graph_file_counter + 1
if graph_file_counter == 10:
graph_file_counter = 1
os.system("rm -f graph%d.png" % graph_file_counter)
bot.sendMessage(update.message.chat_id,
"Genero la statistica dei comandi ricevuti finora...")
line_chart = pygal.HorizontalBar()
line_chart.title = startup_time
line_chart.add('rele', chiamate[comandi.index("rele")])
line_chart.add('foto', chiamate[comandi.index("foto")])
line_chart.add('video', chiamate[comandi.index("video")])
line_chart.add('logo', chiamate[comandi.index("logo")])
line_chart.add('pdf', chiamate[comandi.index("pdf")])
line_chart.add('music', chiamate[comandi.index("music")])
line_chart.add('dog', chiamate[comandi.index("dog")])
line_chart.add('jingle', chiamate[comandi.index("jingle")])
line_chart.add('graph', chiamate[comandi.index("graph")])
line_chart.render_to_png('graph%d.png' % graph_file_counter)
bot.sendPhoto(update.message.chat_id,
open('graph%d.png' % graph_file_counter))
def graph12():
"""This graph about Compare graph of Classified by type og alcohol in 2544 - 2557"""
sheet = workbook.sheet_by_index(5)
data = [[sheet.cell_value(r, c) for c in range(sheet.ncols)]
for r in range(sheet.nrows)]
for i in range(2, sheet.nrows):
for k in range(1, 6):
list_data[i - 2].append({
'value':
round((data[i][k] / sum(data[i][j]
for j in range(1, 6))) * 100, 2),
'label':
'%.2f%s' % (round(
(data[i][k] / sum(data[i][j]
for j in range(1, 6))) * 100, 2), '%')
})
line_chart = pygal.HorizontalBar(print_labels=True, stack_from_top=False)
line_chart.title = 'เปรียบเทียบรอยละของประชากรอายุ 15 ปขึ้นไปที่ดื่มสุราหรือเครื่องดื่มมึนเมา จำแนกตามประเภทของสุราที่ดื่มบ่อย ปี 2544 - 2557'
line_chart.x_labels = [
'เบียร์', 'สุราแช่พื้นบ้าน (สาโท อุ กระแช่)',
'สุราขาว, สุราสี, สุรากลั่น', 'ไวน์', 'อื่นๆ'
]
line_chart.y_labels = map(int, range(0, 61, 10))
for i in range(4):
line_chart.add(data_name[i], list_data[i])
line_chart.render_to_file(
'12Compare graph of Classified by type og alcohol in 2544 - 2557.svg')
def graph11():
"""This graph about Compare graph of Classified by frequency of drinking in 2544 - 2557"""
sheet = workbook.sheet_by_index(4)
data = [[sheet.cell_value(r, c) for c in range(sheet.ncols)]
for r in range(sheet.nrows)]
for i in range(2, sheet.nrows):
for k in range(1, 6):
list_data[i - 2].append({
'value':
round((data[i][k] / sum(data[i][j]
for j in range(1, 6))) * 100, 2),
'label':
'%.2f%s' % (round(
(data[i][k] / sum(data[i][j]
for j in range(1, 6))) * 100, 2), '%')
})
line_chart = pygal.HorizontalBar(print_labels=True, stack_from_top=False)
line_chart.title = 'เปรียบเทียบรอยละของประชากรอายุ 15 ปขึ้นไปที่ดื่มสุราหรือเครื่องดื่มมึนเมา จําแนกตามความถี่ในการดื่มสุราหรือเครื่องดื่มมึนเมา ปี 2544 - 2557'
line_chart.x_labels = [
"ดื่มทุกวัน", "5-6 วันต่อสัปดาห์", "3-4 วันต่อสัปดาห์",
"1-2 วันต่อสัปดาห์", "ดื่มนานๆครั้ง"
]
line_chart.y_labels = map(int, range(0, 71, 10))
for i in range(6):
line_chart.add(data_name[i], list_data[i])
line_chart.render_to_file(
'11Compare graph of Classified by frequency of drinking in 2544 - 2557.svg'
)
def __init__(self):
super(Chart, self).__init__()
self.chart_types = {
"pie": pygal.Pie(),
"horizontal_bar": pygal.HorizontalBar()
}
def rate_movies():
""" Create Rated of Movies horizontalbar_graph of movies in year 2000-2016 and Load Dataset title and revenue"""
for year in range(2000, 2017):
print(">> Year : %i" % year)
# Start display
print(">> [status] Create Graph Starting!")
dataset = pd.read_csv("Data_Export/%i.csv" % (year))
title = dataset["title"].tolist() #Title Movies
revenue = dataset["revenue"].tolist() #Revenue
graph = pygal.HorizontalBar(x_title="Movie Name & Revenue")
graph.title = "Revenue of Movies in year %i Dataset" % year
for i in range(len(title)):
if revenue[i] != 0:
graph.add(title[i], revenue[i])
graph.render_to_file(
"Graph_Export/Revenue_Movies/HorizontalBar/Revenue_of_Movies_%i.svg"
% year)
# End display
print(">> [status] Created Graph Successful!")
print()
# Used time
print(">> [status] Completed : Used time = %s seconds" %
(time.time() - start_time))
def plotPermBarCharts(dataFilePath, outputPath):
for short_name in US_STATES:
with open(dataFilePath + short_name + '.csv', 'rb') as csvfile:
stateData = list(csv.reader(csvfile))
darken_style = DarkenStyle('#ff8723', step=20)
custom_style = Style(tooltip_font_size=18,
legend_font_size=13,
value_font_size=15,
title_font_size=18)
bar_chart = pygal.HorizontalBar(
legend_box_size=10,
hieght=100,
style=darken_style,
print_values=True,
margin=20,
# show_legend=False,
legend_at_bottom=True,
tooltip_fancy_mode=True,
tooltip_border_radius=10)
# bar_chart.title = 'Top 30 foreign-worker-friendly Companies in</br>' + US_STATE_SHORT_dic[short_name]
topX = 20
if (short_name == 'MO'):
topX = 17
for row in stateData[1:topX + 1]:
bar_chart.add(str(row[0]), float(row[1]), rounded_bars=3)
# bar_chart.render_in_browser()
bar_chart.render_to_file(outputPath + short_name + '.svg')
def getProtocolPlot(filename):
tcap = pyshark.FileCapture(filename, only_summaries=True)
protocolList = {}
for packet in tcap:
line = str(
packet
) # Each packet object (i.e a single line that will have the data of a specific packet we see in wireshark will be typecasted into a string and stored in a variable).
formattedLine = line.split(
" "
) # List of different columns of the packet capture file which will be splitted based on the seperator " "(i.e space) which is the default seperator of that data.
# print(formattedLine)
# if(formattedLine[4]=="ARP"):#Extracting all the ARP packet ID's.
if (formattedLine[4] in protocolList.keys()):
protocolList[formattedLine[4]] += 1
else:
protocolList.update({formattedLine[4]: 1})
arp_packet_count = protocolList["ARP"]
line_chart = pygal.HorizontalBar()
line_chart._title = "Packet count"
for i in protocolList:
line_chart.add(i, int(protocolList[i]))
chart = line_chart.render()
#print(chart)
html = """{}""".format(chart)
return html, arp_packet_count
def graph():
try:
if (session['adminlogin'] == True):
candidates = Candidate.query.all()
candiname = []
candivote = []
for i in candidates:
candiname.append(i.Name)
candivote.append(i.Count)
line_chart = pygal.HorizontalBar() #bar graph
line_chart.title = 'Vote Count'
for i in range(len(candiname)):
line_chart.add(candiname[i], candivote[i])
line_chart = line_chart.render_data_uri()
voter_voted = Voter.query.filter(Voter.Voted.like('1')).all()
voter_not_voted = Voter.query.filter(Voter.Voted.like('0')).all()
pie_chart = pygal.Pie()
pie_chart.title = "Voting Turnout"
pie_chart.add('Voted', len(voter_voted))
pie_chart.add('Not Voted', len(voter_not_voted))
pie_chart = pie_chart.render_data_uri()
return render_template("graph.html",
line_chart=line_chart,
pie_chart=pie_chart)
else:
return redirect('/')
except:
flash("Error Loading Graph!", "info")
return render_template("graph.html")
def home():
if 'username' not in session:
return redirect(url_for('login'))
northCount = 0
westCount = 0
eastCount = 0
southCount = 0
stats = fireS.get('userInfo', None)
print(stats)
for i in stats:
if stats[i]['region'] == 'North':
northCount += 1
elif stats[i]['region'] == 'West':
westCount += 1
elif stats[i]['region'] == 'East':
eastCount += 1
elif stats[i]['region'] == 'South':
southCount += 1
line_chart = pygal.HorizontalBar()
line_chart.title = 'Numbers of users in Smart Kampung'
line_chart.add('North', northCount)
line_chart.add('East', eastCount)
line_chart.add('West', westCount)
line_chart.add('South', southCount)
graph_data = line_chart.render_data_uri()
return render_template("home.html",line_chart=graph_data)
def frequency_dist(data, n, title):
#data should be a list of words
#n is number of most common words
dist = FreqDist(data)
most_common = dist.most_common(n)
words_ordered = []
frequencies_ordered = []
for word in most_common:
words_ordered.append(word[0])
frequencies_ordered.append(word[1])
line_chart = pygal.Bar()
line_chart.title = 'Word frequency'
line_chart.x_labels = words_ordered
line_chart = pygal.HorizontalBar()
line_chart.title = title
for i in range(len(words_ordered)):
line_chart.add(words_ordered[i], frequencies_ordered[i])
return line_chart.render_data_uri()
def chart(publications):
pubs = publications.values('authors')
#Data Generation and Formatting
g = []
for y in pubs:
text = re.sub('[^A-Za-z, ]', '', str(y['authors'].split(", ")))
g.append(text)
counts = []
for i in g:
i = i.split(", ")
counts.append(i)
thingy = {}
for a in counts:
for b in a:
if b in thingy:
thingy[b] += 1
else:
thingy[b] = 1
x = dict(
Counter(thingy).most_common(6)[1:]
) #The "[1:]" list slice here is because the most common researchers will always be themselves, so we remove the self-reference.
#Graph Creation
bar_chart = pygal.HorizontalBar(
title=u'Top Co-authors',
x_title='Number of papers co-authored',
y_title='Co-author',
print_values=True,
print_labels=True,
style=BlueStyle(value_font_family='googlefont:Raleway',
value_font_size=30,
value_colors=('white', )))
for y in x:
bar_chart.add(y, x[y])
bar_chart.render_to_file('./searchFunction/static/author_chart.svg')
def frequency_dist(data, n, title, stopwords=None):
'''
Computes the word-frequency distribution graph and returns a base 64 encoded data uri version of it.
Parameters
----------
data : list
List of text tokens.
Returns
----------
Word-frequency distribution graph in base 64 encoded data uri.
'''
if stopwords is not None:
data = [x for x in data if not x in stopwords]
dist = FreqDist(data)
most_common = dist.most_common(n)
words_ordered = []
frequencies_ordered = []
for word in most_common:
words_ordered.append(word[0])
frequencies_ordered.append(word[1])
line_chart = pygal.HorizontalBar()
line_chart.title = title
for i in range(len(words_ordered)):
line_chart.add(words_ordered[i], frequencies_ordered[i])
return line_chart.render_data_uri()
def revenue_movies():
""" Create Top-20 Revenue of Movies bar_graph of movies in year 2000-2016 and Load Dataset title and rate"""
for year in range(2000, 2017):
# Start display
print(">> Year : %i" % year)
print(">> [status] Create Graph Starting!")
dataset = pd.read_csv("Top-100_Export/Top-100_%i.csv" % (year))
title = dataset["title"].tolist() #Title Movies
revenue = dataset["revenue"].tolist() #Revenue
data = []
for j in range(len(title)):
data.append([title[j], revenue[j]])
data.sort(key=lambda x: x[1], reverse=True)
graph = pygal.HorizontalBar(x_title="Revenue")
graph.value_formatter = lambda x: '{:.10g}M'.format(x)
graph.title = "Top-20 Revenue of Movies in %i Dataset" % year
for i in range(20):
graph.add(data[i][0], data[i][1] / 1000000 // 0.01 / 100)
graph.render_to_file(
"Graph_Export/Top-20_Revenue/Top-20_Revenue_%i.svg" % year)
# End display
print(">> [status] Created Graph Successful!")
# Used time
print(">> [status] Completed : Used time = %s seconds" %
(time.time() - start_time))
