def home_plot2(request):
cur.execute(
'select sum(NA_Sales),sum(EU_Sales),sum(JP_Sales) from game_data;')
saleList = cur.fetchall()
# print(saleSet)
# left = range(1,4)
height = [saleList[0][0], saleList[0][1], saleList[0][2]]
tick_label = ['America', 'Europe', 'Japan']
fig, ax = plt.subplots(figsize=(30, 23))
# ax.bar(left, height, tick_label=tick_label,
# width=0.2, color=['red', 'green'])
ax.scatter(tick_label, height, s=20**2.5)
plt.xlabel('Country', fontsize=35)
plt.ylabel('Total Sales', fontsize=30)
ax.tick_params(axis="x", labelsize=30)
ax.tick_params(axis="y", labelsize=23)
plt.title('Scatter Plot of Country Wise Sales(in Millions)', fontsize=30)
canvas = FigureCanvasAgg(fig)
canvas.print_jpg('games/static/games/image/csbar.jpeg')
# print(saleDict)
image_data = open("games/static/games/image/csbar.jpeg", "rb").read()
return HttpResponse(image_data, content_type="image/png")
def yse_pointing_plot(request,field_name,snid): suggested_pointings,suggested_pointing_names,pointings_for_html,transient,sf = get_yse_pointings_base( field_name,snid) rcParams['figure.figsize'] = (12,6) fig=Figure()#dpi=288,figsize=(1,1)) ax=fig.add_subplot(111) #ax=fig.add_axes([0.2,0.2,0.75,0.65]) canvas=FigureCanvas(fig) ra,dec = [],[] for p,n in zip(suggested_pointings,suggested_pointing_names): d = p.dec.deg*np.pi/180 width_corr = 3.1/np.abs(np.cos(d)) rect = Rectangle(xy=[p.ra.deg-width_corr/2.,p.dec.deg-1.55],width=width_corr,height=3.1,fill=False,lw=5) ax.add_artist(rect) ax.text(p.ra.deg,p.dec.deg,n,ha='center',va='center',fontsize=25) ra += [p.ra.deg] dec += [p.dec.deg] ax.plot(transient.ra,transient.dec,'+',ms=30,mew=5,zorder=100,color='r') ax.text(transient.ra-width_corr/10.,transient.dec+0.15,snid,color='r',fontsize=25,ha='left',va='bottom') ax.set_xlim([np.max(ra)+5,np.min(ra)-5]) ax.set_ylim([np.min(dec)-2,np.max(dec)+2]) ax.set_xlabel(r'$\alpha$',fontsize=15) ax.set_ylabel(r'$\delta$',fontsize=15) response=HttpResponse(content_type='image/png') canvas.print_jpg(response) return response
def getimage(request):
fig = Figure() #imp
fig, ax = plt.subplots() #imp
# x = np.arange(0, 2 * np.pi, 0.01)
# s = np.cos(x) ** 2
# plt.plot(x, s)
# plt.xlabel('xlabel(X)')
# plt.ylabel('ylabel(Y)')
# plt.title('Simple Graph!')
# plt.grid(True)
Country = ['USA', 'Canada', 'Germany', 'UK', 'France']
GDP_Per_Capita = [45000, 42000, 52000, 49000, 47000]
# print(Country)
xAxis = [i + 0.5 for i, _ in enumerate(Country)]
plt.bar(xAxis, GDP_Per_Capita, color='teal')
plt.title('Country Vs GDP Per Capita', fontsize=14)
plt.xlabel('Country', fontsize=14)
plt.ylabel('GDP Per Capita', fontsize=14)
plt.xticks([i + 0.5 for i, _ in enumerate(Country)], Country)
plt.show()
buf = io.BytesIO()
plt.savefig(buf, format='png')
# plt.close(fig)
canvas = FigureCanvas(fig)
response = HttpResponse(content_type='image/jpg')
canvas.print_jpg(response)
# fig.savefig(response )
return response
def graficaP(request, funcion, px, py):
def getnums(lis):
return [float(n) for n in lis]
px = px.split(",")
py = py.split(",")
a = min(getnums(px)) - 3
b = max(getnums(px)) + 3
U, D = sp.symbols('U D')
div = lambda U, D: U / D
func = sp.sympify(funcion, locals={'div': div})
xDats = [i for i in np.arange(float(a), float(b) + 1.0, 0.3)]
yDats = [float('{:.15f}'.format(float(func.subs(x, i)))) for i in xDats]
fig = Figure()
canvas = FigureCanvasAgg(fig)
ax = fig.add_subplot(111)
ax.plot(xDats, yDats)
ax.plot(getnums(px), getnums(py), 'k*')
ax.set(xlabel='eje x', ylabel='eje y', title='Grafica de la Funcion')
ax.grid()
response = HttpResponse(content_type='image/jpg')
canvas.print_jpg(response)
return response
def ant_ant_stat_frame(title_text, full_pol_array, station_names, output_name=None, dpi=50, **kwargs):
if output_name is None:
fig = figure(figsize=(32,30), dpi=dpi)
else:
fig=Figure(figsize=(32,30), dpi=dpi)
if output_name is None:
fig.suptitle(title_text, fontsize=30)
else:
fig.suptitle(title_text+' '+output_name, fontsize=30)
ax1=fig.add_subplot(2,2,1)
ant_ant_stat_plot(fig, ax1, title_text+' XX', full_pol_array[:,:,0], station_names, **kwargs)
ax2=fig.add_subplot(2,2,2)
ant_ant_stat_plot(fig, ax2, title_text+' XY', full_pol_array[:,:,1], station_names, **kwargs)
ax3=fig.add_subplot(2,2,3)
ant_ant_stat_plot(fig, ax3, title_text+' YX', full_pol_array[:,:,2], station_names, **kwargs)
ax4=fig.add_subplot(2,2,4)
ant_ant_stat_plot(fig, ax4, title_text+' YY', full_pol_array[:,:,3], station_names, **kwargs)
if output_name is not None:
canvas = FigureCanvasAgg(fig)
if output_name[-4:] in ['.jpg', '.JPG']:
canvas.print_jpg(output_name, dpi=dpi, quality=55)
else:
canvas.print_figure(output_name, dpi=dpi)
pass
pass
def fetch_city(request):
city = request.POST.get('cities')
unique_categories = set()
categories = [
query['categories'].split(', ')
for query in ParsedData.objects.values('categories').filter(city=city)
]
for category in categories:
for cate in category:
unique_categories.add(cate)
cat_list = []
for cate in unique_categories:
cat_list.append((cate, ParsedData.objects.order_by().filter(
city=city, categories__contains=cate).count()))
cat_list = sorted(cat_list, key=lambda x: (-x[1], x[0]))[:5]
# Data for plotting
fig = plt.figure(num=None, figsize=(16, 12), facecolor='w', edgecolor='k')
ax = plt.gca()
x = list(item[0] for item in cat_list)
y = list(item[1] for item in cat_list)
ax.bar(x, y, color='gray')
ax.set(xlabel='Food Type',
ylabel='Number of Restuarant',
title='Ranking Cuisine Based on ' + city + " Food")
# ax.grid()
response = HttpResponse(content_type='image/png')
canvas = FigureCanvasAgg(fig)
canvas.print_jpg(response)
return response
def plot(request, id_sensor, days, pltype):
check_url(days=days, id_sensor=id_sensor)
if pltype != 'moist' and pltype != 'temp':
raise NotFound(detail='only \'moist\' and \'temp\' are available',
code=404)
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.dates import DateFormatter
fig = Figure()
ax = fig.add_subplot(111)
x = []
y1 = []
y2 = []
now = datetime.now()
for el in Sensor.objects.filter(date__gte=now - timedelta(days=days),
date__lte=now,
sensor_id__exact=id_sensor):
x.append(el.date)
if pltype == 'moist':
y1.append(el.vw_30cm)
y2.append(el.vw_60cm)
else:
y1.append(el.t_30cm)
y2.append(el.t_60cm)
ax.plot_date(x, y1, 'r-', label='глубина 30 см')
ax.plot_date(x, y2, '-', label='глубина 60 см')
ax.legend()
ax.xaxis.set_major_formatter(DateFormatter('%Y-%m-%d'))
fig.autofmt_xdate()
canvas = FigureCanvas(fig)
response = HttpResponse(content_type='image/jpg')
canvas.print_jpg(response)
return response
def saveWithAxes(img):
fig = Figure()
axes = fig.add_subplot(111)
axes.imshow(img, cmap=cm.Greys_r)
canvas = FigureCanvas(fig)
# TODO: Need to find a way to avoid this hack!
canvas.print_jpg("run.img.tmp.jpg")
return numpy.array(Image.open("run.img.tmp.jpg"))
def saveWithAxes(img):
fig = Figure()
axes = fig.add_subplot(111)
axes.imshow(img, cmap=cm.Greys_r)
canvas = FigureCanvas(fig)
# TODO: Need to find a way to avoid this hack!
canvas.print_jpg("run.img.tmp.jpg")
return numpy.array(Image.open("run.img.tmp.jpg"))
def finderim(self, request, transient_id, clobber=False):
import os
from .util import mkFinderChart
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
import pylab as plt
transient = Transient.objects.get(pk=transient_id)
basedir = "%sYSE_App/images/findercharts" % (
djangoSettings.STATIC_ROOT)
if not os.path.exists(basedir):
os.makedirs(basedir)
print(basedir)
outputOffsetFileName = '%s/%s/%s.offsetstars.txt' % (
basedir, transient.name, transient.name)
outputFinderFileName = '%s/%s/%s.finder.png' % (
basedir, transient.name, transient.name)
if not os.path.exists(os.path.dirname(outputFinderFileName)):
os.makedirs(os.path.dirname(outputFinderFileName))
#if os.path.exists(outputOffsetFileName) and\
# os.path.exists(outputFinderFileName):
# return HttpResponseRedirect(reverse('transient_detail',
# args=(transient.id,)))
find = mkFinderChart.finder()
parser = find.add_options(usage='')
options, args = parser.parse_args()
options.ra = str(transient.ra)
options.dec = str(transient.dec)
options.snid = transient.name
options.outputOffsetFileName = outputOffsetFileName
options.outputFinderFileName = outputFinderFileName
find.options = options
fig = Figure()
if not os.path.exists(options.outputFinderFileName) or \
not os.path.exists(options.outputOffsetFileName) or \
clobber:
ax = fig.add_axes([0.2, 0.3, 0.6, 0.6])
canvas = FigureCanvas(fig)
ax, offdictlist = find.mkChart(options.ra,
options.dec,
options.outputFinderFileName,
ax=ax,
saveImg=True)
else:
image = mpimg.imread(options.outputFinderFileName)
ax = fig.add_axes([0, 0, 1, 1])
ax.set_xticks([])
ax.set_yticks([])
canvas = FigureCanvas(fig)
ax.imshow(image)
response = django.http.HttpResponse(content_type='image/png')
canvas.print_jpg(response)
return response
def getWordCloud(request):
# 补充停用词
f = open("stopwords-en.txt", mode='r', encoding="UTF-8")
li = []
for word in f.readlines():
li.append(word.replace('\n', ''))
stopwords = set(STOPWORDS)
stopword_list = ['f**k', 'f*****g', 'shit', 'good', 'bad', 'think', 'make', 'know', 'even', 'really', 'one',
'going', 'need', 'got', 'look', 'still']
for word in stopword_list:
stopwords.add(word)
for word in li:
stopwords.add(word)
# 连接数据库
sql_conn = sqlite3.connect('reddit_new.sqlite')
# 读取post
reddit_id = request.GET['id']
subreddit = request.GET['subreddit']
author = request.GET['author']
if reddit_id == "" and subreddit == "" and author == "":
messages.warning(request, "Please input something!")
return render(request, "query.html")
# 查找
if reddit_id != "" and subreddit == "" and author == "":
sql_query = "select body from reddit where id = '%s'"%(reddit_id)
elif reddit_id == "" and subreddit != "" and author == "":
sql_query = "select body from reddit where subreddit = '%s'"%(subreddit)
elif reddit_id == "" and subreddit == "" and author != "":
sql_query = "select body from reddit where author = '%s'"%(author)
elif reddit_id != "" and subreddit != "" and author == "":
sql_query = "select body from reddit where subreddit = '%s' and id = '%s'"%(subreddit,reddit_id)
elif reddit_id == "" and subreddit != "" and author != "":
sql_query = "select body from reddit where subreddit = '%s' and author = '%s'"%(subreddit,author)
elif reddit_id != "" and subreddit == "" and author != "":
sql_query = "select body from reddit where id = '%s' and author = '%s'"%(reddit_id,author)
elif reddit_id != "" and subreddit != "" and author != "":
sql_query = "select body from reddit where id = '%s' and author = '%s' and subreddit = '%s'"%(reddit_id,author,subreddit)
df = pd.read_sql(sql_query, sql_conn)
# 文本生成
text = "".join(list(df['body'])).lower()
# 词云生成
wc = WordCloud(background_color="white",
stopwords=stopwords,
max_words=200,
max_font_size=120,
mask=bg_pic,
random_state=42)
wc.generate(text)
fig = plt.figure()
plt.imshow(wc, interpolation="bilinear")
plt.axis("off")
# 转HTML
canvas = FigureCanvasAgg(fig)
response = HttpResponse(content_type='image/png')
canvas.print_jpg(response)
plt.close(fig)
return response
def lineTemp(response):
fig = Figure()
canvas = FigureCanvas(fig)
ax = fig.add_subplot(111)
data_df = pd.read_csv("MainApp/static/ourdata/scv_file2.csv")
data_df = pd.DataFrame(data_df)
data_df.plot(ax=ax, y='Temperature', x='Severity')
response = HttpResponse(content_type='image/jpg')
canvas.print_jpg(response)
return response
def histPop(response):
fig = Figure()
canvas = FigureCanvas(fig)
ax = fig.add_subplot(111)
data_df = pd.read_csv("MainApp/static/ourdata/scv_file2.csv")
data_df = pd.DataFrame(data_df)
data_df.plot(ax=ax, y='Population', kind='hist')
response = HttpResponse(content_type='image/jpg')
canvas.print_jpg(response)
return response
def boxAge(response):
fig = Figure()
canvas = FigureCanvas(fig)
ax = fig.add_subplot(111)
data_df = pd.read_csv("MainApp/static/ourdata/scv_file2.csv")
data_df = pd.DataFrame(data_df)
data_df.boxplot(ax=ax, column='Median_Age', by='Severity')
response = HttpResponse(content_type='image/jpg')
canvas.print_jpg(response)
return response
def mplimage(request):
fig = Figure()
canvas = FigureCanvas(fig)
ax = fig.add_subplot(111)
x = np.arange(-4, 0.5, .02)
y = np.sin(np.exp(2 * x))
ax.plot(x, y)
s = io.StringIO()
response = HttpResponse(s.getvalue(), content_type='image/jpg')
canvas.print_jpg(response)
return response
def ret_graph():
fig = Figure()
canvas = FigureCanvas(fig)
df1[df1.columns].plot(kind='line')
buf = io.BytesIO()
plt.savefig(buf, format='png')
plt.close(fig)
response = HttpResponse(content_type='image/jpg')
canvas.print_jpg(response)
return response
def seaPlot(response):
fig = Figure()
canvas = FigureCanvas(fig)
ax = fig.add_subplot(111)
data_df = pd.read_csv("MainApp/static/ourdata/scv_file2.csv")
del data_df['Unnamed: 0.1']
del data_df['Unnamed: 0']
corr = data_df.corr()
sns.heatmap(corr, ax=ax)
response = HttpResponse(content_type='image/jpg')
canvas.print_jpg(response)
return response
def thirteen(request):
if request.method == 'POST':
form = graphcountry(request.POST)
form.fields['competition'].choices = [(i, i) for i in choice2]
if form.is_valid():
b = form.cleaned_data['competition']
import mysql.connector
mydb = mysql.connector.connect(host="localhost",
user="******",
passwd="albertbolt23",
database="sports2",
auth_plugin='mysql_native_password')
mycursor = mydb.cursor()
sql = "SELECT place_of_birth,count(*) FROM project_records,project_event,project_competition,project_sportsman where project_records.event_id_id=project_event.id and project_event.Competition_name_id=project_competition.name and project_records.player_name_id=project_sportsman.name and project_competition.name='%s' group by place_of_birth order by count(*)" % (
b)
mycursor.execute(sql)
k = mycursor.fetchall()
a = []
for i in k:
for j in i:
a.append(j)
a23 = []
a24 = []
for i in range(0, len(a), 2):
a23.append(a[i])
for i in range(1, len(a), 2):
a24.append(a[i])
y_pos = np.arange(len(a23))
fig = Figure(figsize=(10, 5))
canvas = FigureCanvas(fig)
ax = fig.add_subplot(111)
y_pos = np.arange(len(a23))
ax.barh(a23, a24, align='center', alpha=0.5)
ax.set_xlabel('No of medals')
ax.set_ylabel('Countries')
response = HttpResponse(content_type='image/jpg')
canvas.print_jpg(response)
return response
def line_plot(request, year=date.today().year, month=date.today().month):
"""
Function based view that creates a line graph plotting the daily spendings for the current month.
"""
# creating the data to be presented in the graph
x = []
y = []
if month == 2:
if year % 4 == 0 and (year % 100 != 0 or year % 400 == 0):
days = np.arange(1, 29, 1)
else:
days = np.arange(1, 28, 1)
elif month in [1, 3, 5, 7, 8, 10, 12]:
days = np.arange(1, 31, 1)
elif month in [4, 6, 9, 11]:
days = np.arange(1, 30, 1)
for day in days:
x.append(day)
y.append(
UserDetails.objects.filter(
user=request.user,
date_added__year=year,
date_added__month=month,
date_added__day=day).aggregate(suma=Sum('cost'))['suma']
or 0.00)
# creating the figure to plot the graph
fig = Figure()
ax = fig.add_subplot(111)
# creating the axes
ax.plot(x, y)
fig.suptitle('Your daily spendings', fontsize=20)
ax.set(xlabel='day', ylabel='spendings (€)')
ax.grid()
# FigureCanvas is the area onto which the figure is drawn
canvas = FigureCanvas(fig)
# creating the response as image type jpeg
response = HttpResponse(content_type="image/jpg")
canvas.print_jpg(response)
return response
def stat3(request):
f = Figure()
co = sqlite3.connect('./db.sqlite3')
data = pd.read_sql_query('SELECT count(date_demande),date_demande FROM forms_demande GROUP BY date_demande;',co)
df = pd.DataFrame(data)
f, ax = plt.subplots()
df.plot(kind = 'bar', color = 'limegreen',ax=ax, x = 'date_demande',figsize=(11,11), title = "Nombre de commandes par jour" )
plt.xlabel('Date')
plt.ylabel("Nombre de commandes")
plt.legend('')
fig, ax = plt.subplots()
df.plot(ax=ax)
canvas = FigureCanvasAgg(f)
response = HttpResponse(content_type='image\jpg')
canvas.print_jpg(response)
plt.close(f)
return response
def stat2(request):
f = Figure()
co = sqlite3.connect('./db.sqlite3')
data = pd.read_sql_query('SELECT count(nom), ville FROM forms_compte GROUP BY nom;',co)
df = pd.DataFrame(data)
f, ax = plt.subplots()
df.plot(kind = 'bar', color = 'gold',ax=ax, x = 'ville',figsize=(11,11), title = "Nombre de foyers par ville" )
plt.xlabel('Ville')
plt.ylabel("Nombre de foyers")
plt.legend('')
fig, ax = plt.subplots()
df.plot(ax=ax)
canvas = FigureCanvasAgg(f)
response = HttpResponse(content_type='image\jpg')
canvas.print_jpg(response)
plt.close(f)
return response
def histogram(img):
fig = Figure()
axes = fig.add_subplot(111)
if len(img.shape) == 2 or img.shape[2:][0] == 1:
axes.hist(img.flatten(), 300, color='blue', edgecolor='none')
else:
img = cv2.cvtColor(img, cv2.cv.CV_RGB2HSV)
axes.hist([img[...,0], img[...,1], img[...,2]], 128, color=['red', 'green', 'blue'], edgecolor='none')
axes.legend(('Hue', 'Saturation', 'Value'))
#axes.legend(('Red', 'Green', 'Blue'))
# If hsv, should look something like this.
# axes.hist([img[...,0], img[...,1], img[...,2]], 300, color=['r','g','b'])
axes.grid(True)
setp(axes.get_yticklabels(), visible=False)
canvas = FigureCanvas(fig)
canvas.print_jpg("run.hist.tmp.jpg")
return Image.open("run.hist.tmp.jpg")
def plot(request):
text = request.GET.get('name')
value = Game.objects.get(name=text)
left = [1, 2, 3, 4]
height = [
value.NA_Sales, value.EU_Sales, value.JP_Sales, value.Other_Sales
]
tick_label = ['America', 'Europe', 'Japan', 'Other']
fig, ax = plt.subplots()
ax.bar(left,
height,
tick_label=tick_label,
width=0.8,
color=['red', 'green'])
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Bar Graph of Sales in Country(in Millions)')
canvas = FigureCanvasAgg(fig)
canvas.print_jpg('games/static/games/image/bar.jpeg')
labels = tick_label
sizes = height
explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs')
fig1, ax1 = plt.subplots()
ax1.pie(sizes,
explode=explode,
labels=labels,
autopct='%1.1f%%',
shadow=True,
startangle=90)
ax1.axis(
'equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.title('Pie Chart of Sales')
plt.legend(loc="upper left")
canvas1 = FigureCanvasAgg(fig1)
canvas1.print_jpg('games/static/games/image/pie.jpeg')
return render(request, 'games/graph.html')
def search(request):
if request.method == 'POST':
search_id = request.POST.get('textfield', None)
try:
queried_data = ParsedData.objects.values('city').annotate(
type_count=models.Sum(models.F('stars') *
models.F('review_count'),
output_field=models.IntegerField()) /
models.Count('business_id')).filter(
categories__contains=search_id).order_by(
'-type_count')[:10]
diction = {}
for data in queried_data:
diction[data['city']] = data['type_count']
# Data for plotting
fig = plt.figure(num=None,
figsize=(16, 12),
facecolor='w',
edgecolor='k')
ax = plt.gca()
plt.xticks(rotation=45)
x = list(diction.keys())
y = list(diction.values())
ax.bar(x, y, color='gray')
ax.set(xlabel='Cities',
ylabel='Score',
title='Ranking Cities Based on ' + search_id + " Food")
# ax.grid()
response = HttpResponse(content_type='image/png')
canvas = FigureCanvasAgg(fig)
canvas.print_jpg(response)
return response
except ValueError:
return HttpResponse(str(ValueError) + "Invalid Search.")
else:
return HttpResponse("Invalid Post.")
def timeseries_station_page(ms, station_name, time_slots, data, fn=abs, output_name=None):
dpi=50
if output_name is None:
fig = figure(figsize=(32,24), dpi=dpi)
else:
fig = Figure(figsize=(32,24), dpi=dpi)
station_name_list = list(ms.tables['antennae']['NAME'])
station_id = station_name_list.index(station_name)
num_ant = len(ms.tables['antennae'])
tsn = time_slots-time_slots[0]
pol_names = corr_type(ms.tables['polarization']['CORR_TYPE'][0])
ref_freq_mhz = ms.tables['spectral_windows'][0]['REF_FREQUENCY']/1.e6
fig.suptitle(ms.msname+': '+fn.__name__+'(vis) with '+station_name+' at %3.2f MHz' % (ref_freq_mhz,), fontsize='large')
median_amp = ma.median(ma.mean(ma.median(fn(data[station_id,:,0::3,:]), axis=-1), axis=-1), axis=-1)
for id2,name in enumerate(station_name_list):
ax = fig.add_subplot(ceil(num_ant/4.0),4, id2+1)
ax.plot(tsn, fn(data[station_id,id2,0,:]), c='blue' , label=pol_names[0])
ax.plot(tsn, fn(data[station_id,id2,1,:]), c='green' , label=pol_names[1])
ax.plot(tsn, fn(data[station_id,id2,2,:]), c='purple', label=pol_names[2])
ax.plot(tsn, fn(data[station_id,id2,3,:]), c='red' , label=pol_names[3])
ax.grid()
ax.set_ylabel(station_name_list[id2], rotation='horizontal')
ax.set_ylim(0.0, 3*median_amp)
ax.set_yticklabels([])
if id2 < len(station_name_list)-4:
ax.set_xticklabels([])
else:
ax.set_xlabel('Time [s]')
pass
fig.subplots_adjust(hspace=0.0, top=0.95, bottom=0.04)
if output_name is not None:
canvas = FigureCanvasAgg(fig)
if output_name[-4:] in ['.jpg', '.JPG']:
canvas.print_jpg(output_name, dpi=dpi, quality=55)
else:
canvas.print_figure(output_name, dpi=dpi)
pass
pass
def grafica(request, funcion, a, b):
U, D = sp.symbols('U D')
div = lambda U, D: U / D
func = sp.sympify(funcion, locals={'div': div})
xDats = [i for i in np.arange(float(a), float(b) + 1.0, 0.3)]
yDats = [float('{:.15f}'.format(float(func.subs(x, i)))) for i in xDats]
fig = Figure()
canvas = FigureCanvasAgg(fig)
ax = fig.add_subplot(111)
ax.plot(xDats, yDats)
ax.set(xlabel='eje x', ylabel='eje y', title='Grafica de la Funcion')
ax.grid()
response = HttpResponse(content_type='image/jpg')
canvas.print_jpg(response)
return response
def histogram(img):
fig = Figure()
axes = fig.add_subplot(111)
if len(img.shape) == 2 or img.shape[2:][0] == 1:
axes.hist(img.flatten(), 300, color='blue', edgecolor='none')
else:
img = cv2.cvtColor(img, cv2.cv.CV_RGB2HSV)
axes.hist([img[..., 0], img[..., 1], img[..., 2]],
128,
color=['red', 'green', 'blue'],
edgecolor='none')
axes.legend(('Hue', 'Saturation', 'Value'))
#axes.legend(('Red', 'Green', 'Blue'))
# If hsv, should look something like this.
# axes.hist([img[...,0], img[...,1], img[...,2]], 300, color=['r','g','b'])
axes.grid(True)
setp(axes.get_yticklabels(), visible=False)
canvas = FigureCanvas(fig)
canvas.print_jpg("run.hist.tmp.jpg")
return Image.open("run.hist.tmp.jpg")
def home_plot(request):
yearSet = set()
curSet = []
cur.execute('select year from game_data')
curSet = cur.fetchall()
for each in curSet:
yearSet.add(each[0])
# print(yearSet)
cur.execute('select year, sum(global_sales) from game_data group by year')
saleList = cur.fetchall()
saleDict = {}
for each in saleList:
saleDict[each[0]] = each[1]
keys = []
for each in list(saleDict.keys()):
keys.append(each)
# print(saleDict.values())
left = range(1, 38)
height = saleDict.values()
tick_label = keys
fig, ax = plt.subplots(figsize=(30, 23))
ax.bar(left, height, tick_label=tick_label, width=0.8, color=['red'])
plt.xlabel('Years', fontsize=30)
plt.ylabel('Global Sales', fontsize=30)
ax.tick_params(axis="x", labelsize=23, rotation=90)
ax.tick_params(axis="y", labelsize=23)
plt.title('Bar Graph of Global Sales in Years(in Millions)', fontsize=30)
canvas = FigureCanvasAgg(fig)
canvas.print_jpg('games/static/games/image/gsbar.jpeg')
# print(saleDict)
image_data = open("games/static/games/image/gsbar.jpeg", "rb").read()
return HttpResponse(image_data, content_type="image/png")
def plotResults(request, question_id):
fig = Figure()
ax = fig.add_subplot(1, 1, 1)
p = get_object_or_404(Question,
pk=question_id) # Get the poll object from django
x = matplotlib.numpy.arange(1, p.choice_set.count())
choices = p.choice_set.all()
votes = [choice.votes for choice in choices]
sum_votes = sum(votes)
names = [choice.choice_text for choice in choices]
votes_percentage = [choice.votes * 100 / sum_votes for choice in choices]
numTests = p.choice_set.count()
ind = matplotlib.numpy.arange(numTests) # the x locations for the groups
cols = ['purple', 'yellow', 'red'] * 10
cols = cols[0:len(ind)]
ax.barh(names, votes_percentage, color=cols, align='center')
ax.set_yticks(ind + 0.1)
ax.set_yticklabels(names)
ax.set_ylabel("Choices")
ax.set_xlabel("Votes in Percentage(%)")
title = "Dynamically Generated Results -- Plotted Bar Graph"
ax.set_title(title)
#ax.grid(True)
canvas = FigureCanvas(fig)
response = HttpResponse(content_type='image/jpg')
canvas.print_jpg(response)
return response
def home_plot5(request):
cur.execute(
'select platform, count(platform) from game_data group by platform;')
saleList = cur.fetchall()
saleDict = {}
for each in saleList:
saleDict[each[0]] = each[1]
keys = []
for each in list(saleDict.keys()):
keys.append(each)
# print(saleDict.values())
left = range(1, 25)
height = saleDict.values()
tick_label = keys
fig, ax = plt.subplots(figsize=(30, 23))
ax.bar(left,
height,
tick_label=tick_label,
width=0.8,
color=['red', 'green'])
# ax.plot(tick_label, height)
plt.xlabel('Platform', fontsize=30)
plt.ylabel('Count', fontsize=30)
ax.tick_params(axis="x", labelsize=23, rotation=90)
ax.tick_params(axis="y", labelsize=23)
plt.title('Count of games per Platform', fontsize=30)
canvas = FigureCanvasAgg(fig)
canvas.print_jpg('games/static/games/image/ggbar.jpeg')
# print(saleDict)
image_data = open("games/static/games/image/ggbar.jpeg", "rb").read()
return HttpResponse(image_data, content_type="image/png")
def uv_only_plot(request):
ra,dec,sr = [float(request.GET.get(_)) for _ in ['ra', 'dec', 'sr']]
field = request.GET.get('field')
width = 600
work = uv_only_worker(ra, dec, sr)
value = work['galex']['table'][field]
nidx = work['nidx']
fig = Figure(facecolor='white', figsize=(width/72, width*0.5/72), dpi=72, tight_layout=True)
ax = fig.add_subplot(111)
ax.hist(value, bins=np.arange(15, 25, 0.5), alpha=0.5, label='All');
ax.hist(value[nidx], bins=np.arange(15, 25, 0.5), alpha=0.5, label='Not matched');
ax.set_xlabel(field)
ax.set_title('ra=%s dec=%s sr=%s' % (ra, dec, sr))
ax.legend()
canvas = FigureCanvas(fig)
response = HttpResponse(content_type='image/jpeg')
canvas.print_jpg(response)
return response
def update_picture_self_coords(image, lidar_points, self_position,
only_nearby_meters, additional_points):
fig = Figure(figsize=figsize_from_image_size(image))
canvas = FigureCanvas(fig)
ax = fig.gca()
x_points = [x[0] for x in lidar_points]
y_points = [x[1] for x in lidar_points]
ax.scatter(x_points, y_points, marker='o', s=5, c='b')
if self_position is None:
ax.scatter([0], [0], marker='o', s=50, c='r')
else:
rect = patches.Rectangle((self_position['x'], self_position['y']),
self_position['w'],
self_position['h'],
linewidth=3,
edgecolor='r',
facecolor='none')
ax.add_patch(rect)
if additional_points is not None:
ax.scatter(additional_points['x'],
additional_points['y'],
marker='o',
s=25,
c='g')
ax.set_xlim(-only_nearby_meters, only_nearby_meters)
ax.set_ylim(-only_nearby_meters, only_nearby_meters)
ax.grid(which='both', linestyle='--', alpha=0.5)
canvas.draw()
jpeg = BytesIO()
canvas.print_jpg(jpeg)
image.value = jpeg.getvalue()
def station_gain_bar_chart(ms, station_name, time_slots, data, output_name= None):
dpi=50
if output_name is None:
fig = figure(figsize=(38,24), dpi=dpi)
else:
fig = Figure(figsize=(38,24), dpi=dpi)
station_name_list = list(ms.tables['antennae']['NAME'])
num_stations = len(station_name_list)
station_id = station_name_list.index(station_name)
ref_freq_mhz = ms.tables['spectral_windows'][0]['REF_FREQUENCY']/1.e6
is_autocorrelation = array([station_name == name for name in ms.tables['antennae']['NAME']])
noise = ma.array(data[station_id,:, 1:3,:].imag.std(axis=-1).mean(axis=-1),
mask = is_autocorrelation)
sig = median(abs(data[station_id, :, :, :]),axis=-1)
signal = ma.array(sig, mask = is_autocorrelation[:, newaxis]*ones((num_stations, 4)))
ax = fig.add_subplot(1,1,1)
xx_bars = ax.bar(arange(len(station_name_list))-0.4, signal[:, 0], width=0.2, color='blue', label='xx')
xy_bars = ax.bar(arange(len(station_name_list))-0.2, signal[:, 1], width=0.2, color='lightblue', label='xy')
yx_bars = ax.bar(arange(len(station_name_list)) , signal[:, 2], width=0.2, color='lightpink', label='yx')
yy_bars = ax.bar(arange(len(station_name_list))+0.2, signal[:, 3], width=0.2, color='red', label='yy')
for x_pos, name in enumerate(station_name_list):
if name != station_name:
ax.text(x_pos, signal[x_pos,:].max()*1.02, name, rotation='vertical',
horizontalalignment='center', verticalalignment='bottom',
fontsize=25)
ax.text(x_pos, signal[x_pos,:].max()*-0.01, name, rotation='vertical',
horizontalalignment='center', verticalalignment='top',
fontsize=25)
else:
ax.text(x_pos, 0.0, ' Reference station: '+name, rotation='vertical',
horizontalalignment='center', verticalalignment='bottom',
fontsize=25)
ax.set_xlabel('Station', fontsize=40)
ax.set_ylabel('Visibility amplitude', fontsize=40)
ax.set_ylim(0, ma.max(signal)*1.2)
ax.set_xlim(-1.0, num_stations)
ax.set_xticklabels([])
ax.set_title('%s:\nVis. amp. with station %s at %5.2f MHz' %
(ms.msname, station_name, ref_freq_mhz),
fontsize=40)
old_legend_fontsize = rcParams['legend.fontsize']
rcParams.update({'legend.fontsize': 25})
legend_instance = ax.legend()
for label in ax.get_yticklabels():
label.set_fontsize(40)
if output_name is not None:
canvas = FigureCanvasAgg(fig)
if output_name[-4:] in ['.jpg', '.JPG']:
canvas.print_jpg(output_name, dpi=dpi, quality=55)
else:
canvas.print_figure(output_name, dpi=dpi)
pass
rcParams.update({'legend.fontsize': old_legend_fontsize})
