def term_sheet_to_json(self,
window=30,
windows=[30, 60, 90, 120],
quantiles=[0.25, 0.75],
bins=100,
normed=True,
bench='SPY',
open=False):
ext = '.json'
cones_fig, cones_plt = self.cones(windows=windows, quantiles=quantiles)
rolling_quantiles_fig, rolling_quantiles_plt = self.rolling_quantiles(
window=window, quantiles=quantiles)
rolling_extremes_fig, rolling_extremes_plt = self.rolling_extremes(
window=window)
rolling_descriptives_fig, rolling_descriptives_plt = self.rolling_descriptives(
window=window)
histogram_fig, histogram_plt = self.histogram(window=window,
bins=bins,
normed=normed)
mpld3.save_json(cones_fig, self.define_fig("cones", ext))
mpld3.save_json(rolling_quantiles_fig,
self.define_fig("rolling_quantiles", ext))
mpld3.save_json(rolling_extremes_fig,
self.define_fig("rolling_extremes", ext))
mpld3.save_json(rolling_descriptives_fig,
self.define_fig("rolling_desc", ext))
mpld3.save_json(histogram_fig, self.define_fig("histogram", ext))
pyplt.close(cones_fig)
pyplt.close(rolling_quantiles_fig)
pyplt.close(rolling_extremes_fig)
pyplt.close(rolling_descriptives_fig)
pyplt.close(histogram_fig)
def sin_cos_d3_save():
"""
Web上でy=sin(x), y=cos(x)の図の表示するためのデータをJSONで保存
:return:
"""
base = os.path.dirname(os.path.abspath(__file__))
path = os.path.normpath(os.path.join(base, 'result/sin_cos.json'))
plt = sin_cos_plt()
mpld3.save_json(plt.gcf(), open(path, "w"))
def figlist_to_json(rootname, figlist, postfixes=None):
from matplotlib.backends.backend_agg import FigureCanvasAgg
from itertools import izip, count
from mpld3 import save_json
if postfixes is None:
postfixes = ("fig%02d" % i for i in count(1))
for postfix, fig in izip(postfixes, figlist):
FigureCanvasAgg(fig)
save_json(fig, "%s_%s.json" % (rootname, postfix))
def figlist_to_json(rootname, figlist, postfixes=None):
from matplotlib.backends.backend_agg import FigureCanvasAgg
from itertools import izip, count
from mpld3 import save_json
if postfixes is None:
postfixes = ("fig%02d" % i for i in count(1))
for postfix, fig in izip(postfixes, figlist):
FigureCanvasAgg(fig)
save_json(fig, "%s_%s.json" % (rootname, postfix))
def to_html(self, title, subfolder, index_filename, template_name):
subfolder = subfolder.replace(" ","_")
if subfolder != '':
try:
os.makedirs(os.path.join(self.local_folder, subfolder))
except:
pass
subfolder += '/'
#use mpl3d to create a json file for every matplotlib figure
#these json files will be copied to S3, to be loaded by a web browser and visualized by the MPLD3 library
for idx, (key,val) in enumerate(self.items()):
if val['datatype']=='MPLD3':
json_filename = subfolder + key.replace(" ","_") + '.json'
mpld3.save_json(val['data'], os.path.join(self.local_folder, json_filename)) #val['data'] is expected to be a matplotlib figure; save the json file to the local folder, it will be copied to S3
val['filename'] = json_filename #add the json's filename to the dictionary, it will be use dby the jinja2 template (in the javascript to load json from S3)
elif val['datatype']=='MPLPNG':
png_filename = key.replace(" ","_") + '.png'
val['data'].savefig(os.path.join(self.local_folder, subfolder+png_filename)) #val['data'] is expected to be a matplotlib figure
#del val['data']
#gc.collect()
val['filename'] = png_filename #add the png's filename to the dictionary, it will be used by the jinja2 template to create a IMG tag with this file as source)
else:
pass
#use Jinja2 to instantiate a web page that will load these json files and display the plots with the mpl3d javascript front-end
env = Environment(
loader=FileSystemLoader('./covid/templates'),
#autoescape=select_autoescape(['html', 'xml'])
)
template = env.get_template(template_name)
page = template.render(title=title, items=self)
file = open(os.path.join(self.local_folder,subfolder+index_filename),'w')
file.write(page)
file.close()
import mpld3
import matplotlib
import matplotlib.pyplot as plt
labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
sizes = [15, 30, 45, 10]
explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs')
fig, ax = plt.subplots()
ax.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
ax.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
fig_var = [x[0] for x in locals().items() if isinstance(x[1], matplotlib.pyplot.Figure)][0]
print(fig_var)
globals()[fig_var] = matplotlib.pyplot.Figure
mpld3.save_json(fig_var, fileobj='json_fig.json')
#!/usr/bin/python import matplotlib.pyplot as plt import numpy as np import mpld3 N = 20 fig, ax = plt.subplots() x = np.random.rand(N) y = np.random.rand(N) ax.plot(x, y) mpld3.save_html(fig,"figout.html") mpld3.save_json(fig,"figout.json") html = mpld3.fig_to_html(fig,"figout.json") print html #https://stackoverflow.com/questions/22981359/display-multiple-mpld3-exports-on-a-single-html-page
# In[13]:
## Plot Extents
# Plot pan-Arctic sea ice extent
f = plt.figure(figsize=(10, 5))
ax1 = plt.subplot(1, 1, 1) # Observations
da_81_30_avg.plot(ax=ax1, label='NSIDC NRT\n (Maslanik et al. 1999)')
ds_ext.sel(datetime=da_81_30_avg.time).Extent.plot(label='NSIDC V3', color='m')
ax1.set_ylabel('Millions of square km')
plt.legend(loc='lower right') #bbox_to_anchor=(1.03, 1.05))
# Save to png and json (for website)
f_name = os.path.join(fig_dir, 'panArcticSIC_Forecast_30days')
plt.title('Last 30 days')
f.savefig(f_name + '.png', bbox_inches='tight', dpi=200)
mpld3.save_json(f, f_name + '.json')
mpld3.save_html(f, f_name + '.html')
## Plot pan-Arctic sea ice extent
f = plt.figure(figsize=(10, 5))
ax1 = plt.subplot(1, 1, 1) # Observations
da_81_3m_avg.plot(ax=ax1, label='NSIDC NRT\n (Maslanik et al. 1999)')
ds_ext.sel(datetime=da_81_3m_avg.time).Extent.plot(label='NSIDC V3', color='m')
plt.title('Last 3 months')
ax1.set_ylabel('Millions of square km')
plt.legend(loc='lower right')
f.savefig(os.path.join(fig_dir, 'panArcticSIC_Forecast_3months.png'),
bbox_inches='tight',
dpi=200)
# Plot Recent maps
def PCA2d(sleppa, toflur):
# prófa fyrst að taka bara costoflivingindex og veðrið í viku 30
# töflurnar geta verið prices, costoflivingindex, qualityoflifeindex, weathereurope og weatherchanceseurope
pca = PCA(n_components=2)
fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'))
if (len(toflur) == 1):
if "WeatherEurope" in toflur or "WeatherChancesEurope" in toflur:
query = cur.execute("SELECT * FROM " + toflur[0] +
" where week = 20")
cols = [column[0] for column in query.description]
results = pd.DataFrame.from_records(data=query.fetchall(),
columns=cols)
cities = results['city'].tolist()
dropColumns = ['week', 'city']
if toflur[0] == "WeatherEurope":
dropColumns.append('cloud_cover')
results.drop(columns=dropColumns, inplace=True)
results = results.apply(pd.to_numeric)
resultsScaled = preprocessing.StandardScaler().fit_transform(
results)
principalComponentsScaled = pca.fit_transform(resultsScaled)
principalDfScaled = pd.DataFrame(
data=principalComponentsScaled,
columns=['principal component 1', 'principal component 2'])
N = len(cities)
col = np.random.rand(N)
mynd = ax.scatter(principalDfScaled['principal component 1'],
principalDfScaled['principal component 2'],
c=col,
alpha=0.5)
ax.set_title("2-dimensional PCA of weather in Europe in late June")
if "CostOfLivingIndex" in toflur or "QualityOfLifeIndex" in toflur:
query2 = cur.execute("SELECT * from " + toflur[0])
cols = [column[0] for column in query2.description]
results2 = pd.DataFrame.from_records(data=query2.fetchall(),
columns=cols)
cities2 = results2['City'].tolist()
results2.drop(columns=['City', 'Rank'], inplace=True)
results2 = results2.apply(pd.to_numeric)
results2Scaled = preprocessing.StandardScaler().fit_transform(
results2)
principalComponentsScaled2 = pca.fit_transform(results2Scaled)
principalDfScaled2 = pd.DataFrame(
data=principalComponentsScaled2,
columns=['principal component 1', 'principal component 2'])
col = np.random.rand(len(cities2))
mynd = ax.scatter(principalDfScaled2['principal component 1'],
principalDfScaled2['principal component 2'],
c=col,
alpha=0.5)
if ("CostOfLivingIndex" in toflur):
ax.set_title("2-dimensional PCA of cost of living indeces")
else:
ax.set_title("2-dimensional PCA of quality of life indeces")
if "Prices" in toflur:
query3 = cur.execute("SELECT * from " + toflur[0])
cols = [column[0] for column in query2.description]
print(cols)
results3 = pd.DataFrame.from_records(data=query3.fetchall(),
columns=cols)
cities3 = results3['City'].tolist()
irrelevantAttributes = [
"City", "Monthly Pass", "Volkswagen Golf", "Toyota Corolla",
"Basic Utilities", "Internet", "Fitness Club",
"Tennis Court Rent", "Preschool Month", "Primary School Year",
"Rent 1 Bedroom Center"
"Rent 1 Bedroom Outside Center"
"Rent 3 Bedrooms Center", "Rent 3 Bedrooms Outside Center",
"Price m2 Center", "Price m2 Outside Center",
"Average Monthly Salary After Tax", "Mortgage Interest Rate"
]
results3.drop(columns=irrelevantAttributes, inplace=True)
results3 = results3.apply(pd.to_numeric)
results3Scaled = preprocessing.StandardScaler().fit_transform(
results3)
principalComponentsScaled3 = pca.fit_transform(results3Scaled)
principalDfScaled3 = pd.DataFrame(
data=principalComponentsScaled3,
columns=['principal component 1', 'principal component 2'])
col = np.random.rand(len(cities3))
mynd = ax.scatter(principalDfScaled3['principal component 1'],
principalDfScaled3['principal component 2'],
c=col,
alpha=0.5)
ax.set_title("2-dimensional PCA of prices of goods")
else:
# sameina
toflulisti = []
for tafla in toflur:
if tafla == "WeatherEurope" or tafla == "WeatherChancesEurope":
query = cur.execute("SELECT * FROM " + tafla +
" where week = 20")
cols = [column[0] for column in query.description]
results = pd.DataFrame.from_records(data=query.fetchall(),
columns=cols)
cities = results['city'].tolist()
toflulisti.append(results)
if tafla == "CostOfLivingIndex" or tafla == "QualityOfLifeIndex":
query2 = cur.execute("SELECT * from " + tafla)
cols = [column[0] for column in query2.description]
results2 = pd.DataFrame.from_records(data=query2.fetchall(),
columns=cols)
cities = results2['City'].tolist()
results2.drop(sleppa[1], axis=1)
results2.drop("Rank", axis=1, inplace=True)
results2.rename(index=str,
columns={"City": "city"},
inplace=True)
toflulisti.append(results2)
if tafla == "Prices":
query3 = cur.execute("SELECT * from " + toflur[0])
cols = [column[0] for column in query2.description]
results3 = pd.DataFrame.from_records(data=query3.fetchall(),
columns=cols)
cities3 = results3['City'].tolist()
irrelevantAttributes = [
"City", "Monthly Pass", "Volkswagen Golf",
"Toyota Corolla", "Basic Utilities", "Internet",
"Fitness Club", "Tennis Court Rent", "Preschool Month",
"Primary School Year", "Rent 1 Bedroom Center"
"Rent 1 Bedroom Outside Center"
"Rent 3 Bedrooms Center", "Rent 3 Bedrooms Outside Center",
"Price m2 Center", "Price m2 Outside Center",
"Average Monthly Salary After Tax",
"Mortgage Interest Rate"
]
results3.drop(columns=irrelevantAttributes, inplace=True)
toflulisti.append(results3)
saman = reduce((lambda results, results2: pd.merge(results,
results2,
how='inner',
left_on=['city'],
right_on=['city'])),
toflulisti)
cities = list(saman["city"])
samandrop = ['city', 'week', 'cloud_cover']
saman.drop(columns=samandrop, inplace=True)
samanScaled = preprocessing.StandardScaler().fit_transform(saman)
principalComponentsScaledSaman = pca.fit_transform(samanScaled)
principalDfScaledSaman = pd.DataFrame(
data=principalComponentsScaledSaman,
columns=['principal component 1', 'principal component 2'])
col = np.random.rand(len(saman))
mynd = ax.scatter(principalDfScaledSaman['principal component 1'],
principalDfScaledSaman['principal component 2'],
c=col,
alpha=0.5)
# frekar en að hafa results, results2 o.s.frv. þá get ég haft lista/dict results["WeatherEurope"], results["CostOfLivingIndex"]
# Prices og QualityOfLifeIndex er keimlíkt CostOfLivingIndex
# WeatherChancesEurope er keimlíkt WeatherEurope
# nema það þarf að droppa öðrum dálkum
ax.set_xlabel('Principal Component 1', fontsize=15)
ax.set_ylabel('Principal Component 2', fontsize=15)
if (len(toflur) > 1):
title = "2-dimensional PCA of " + toflur[0]
i = 1
for tafla in toflur[1:]:
if i != (len(toflur) - 1):
title = title + ", " + tafla + " "
else:
title = title + " and " + tafla
i = i + 1
ax.set_title(title)
# vantar að staðfesta að röðin sé eins?
tooltip = mpld3.plugins.PointLabelTooltip(mynd, labels=cities)
mpld3.plugins.connect(fig, tooltip)
mpld3.save_json(fig, "PCA.json")
def one_bar_per_cmd(args):
matplotlib.rcParams['axes.labelsize'] = args.fontsize
matplotlib.rcParams['axes.titlesize'] = args.fontsize + 2
matplotlib.rcParams['xtick.labelsize'] = args.fontsize
matplotlib.rcParams['ytick.labelsize'] = args.fontsize
matplotlib.rcParams['legend.fontsize'] = args.fontsize
matplotlib.rcParams['font.family'] = 'serif'
matplotlib.rcParams['font.serif'] = ['Computer Modern Roman']
matplotlib.rcParams['text.usetex'] = True
matplotlib.rcParams['figure.max_open_warning'] = 400
matplotlib.rcParams['image.cmap'] = "gray"
matplotlib.rcParams['image.interpolation'] = "none"
matplotlib.rcParams['figure.autolayout'] = True
pylab.clf() # Essential! Otherwise the plots will be f**d up!
pylab.figure(1)
pylab.gca().yaxis.grid(True) # Makes it easier to compare bars
pylab.gca().xaxis.grid(False)
pylab.gca().set_axisbelow(True)
plt.style.use(args.pyplot_style)
cmd_list = json.load(args.results)['cmd_list']
cmd_list = util.filter_cmd_list(cmd_list, args.labels_to_include,
args.labels_to_exclude)
(cmd_list, label_map) = util.translate_dict(cmd_list, args.label_map)
# For a bar-chart, we need labels, values, and errors
labels = [util.texsafe(cmd['label']) for cmd in cmd_list]
values = []
std = []
for cmd in cmd_list:
res = util.extract_succeed_results(cmd, args.parse_regex, args.py_type)
if len(res) == 0:
res = [0]
values.append(util.mean(res))
std.append(util.standard_deviation(res))
# Let's write the bar-chart using matplotlib
margin = 0.05
width = (1. - 1. * margin) / len(cmd_list)
ind = np.arange(len(cmd_list)) # the x locations for each bar
fig, ax = plt.subplots()
ax.bar(ind * width, values, width, log=args.ylog, yerr=std)
if args.ymin is not None:
plt.ylim(ymin=float(args.ymin))
if args.ymax is not None:
plt.ylim(ymax=float(args.ymax))
if args.title is not None:
plt.title(util.texsafe(args.title))
# Add some text for labels, title and axes ticks
ax.set_xticks(ind * width + width / 2.)
ax.set_xticklabels(labels, rotation=args.xticklabel_rotation)
if args.ylabel is not None:
ax.set_ylabel(args.ylabel)
# Now make some labels
value_labels(ax, ax.patches)
if args.output is not None:
fname = args.output.name
fformat = fname.split('.')[-1]
print("Writing file '%s' using format '%s'." % (fname, fformat))
if args.mpld3:
import mpld3
if fformat == "html":
mpld3.save_html(fig, args.output)
elif fformat == "json":
mpld3.save_json(fig, args.output)
else:
raise ValueError(
"--mpld3: The output must be either `html` or `json`")
else:
pylab.savefig(fname, format=fformat)
else:
if args.mpld3:
import mpld3
mpld3.show()
else:
pylab.show()
def makePlot(self,
noTightLayout=False,
figHeight='100%',
figWidth='100%',
centeredStyle=None):
current_figure = plt.gcf()
if self.usesMPLD3():
plugins.connect(current_figure, plugins.MousePosition(fontsize=14))
if self.outputType == PlotSaveTYPE.HTML_STRING:
if centeredStyle == None:
centeredStyle = self.centeredStyle
outString = mpld3.fig_to_html(current_figure,
template_type='notebook',
d3_url=self.d3js,
mpld3_url=self.mpld3js,
figHeight=figHeight,
figWidth=figWidth,
styles=centeredStyle)
self.createdPlots.append(outString)
plt.close(current_figure)
return
if self.outputType == PlotSaveTYPE.D3:
mpld3.show(current_figure)
if self.save_to_file:
exactFilename = self.save_file + ".%02d." + PlotSaveTYPE.getFileExtension(
self.outputType)
exactFilename = exactFilename % self.saved_plot
self.saved_plot += 1
if self.outputType == PlotSaveTYPE.HTML:
mpld3.save_html(current_figure,
exactFilename,
template_type='simple',
d3_url=self.d3js,
mpld3_url=self.mpld3js)
elif self.outputType == PlotSaveTYPE.JSON:
mpld3.save_json(current_figure,
exactFilename,
d3_url=self.d3js,
mpld3_url=self.mpld3js)
elif self.outputType == PlotSaveTYPE.PNG:
plt.savefig(exactFilename,
transparent=self.transparent_bg,
bbox_inches='tight')
self.createdPlots.append(exactFilename)
else: # if self.outputType == PlotSaveTYPE.MPL
if not noTightLayout:
legends = current_figure.legends
makeTightLayout = True
for lgd in legends + [
ax.legend_ for ax in current_figure.axes
]:
if lgd == None:
continue
lgd.set_draggable(True)
makeTightLayout = makeTightLayout and lgd._bbox_to_anchor == None
if makeTightLayout:
plt.tight_layout()
plt.show()
plt.close(current_figure)
def line_per_cmd(args):
import matplotlib
if args.output is not None:
matplotlib.use('Agg') # We need this to make matplotlib work on systems with no DISPLAY
import matplotlib.pyplot as plt
import pylab
matplotlib.rcParams.update({'figure.subplot.right': args.plot_size})
matplotlib.rcParams.update({'figure.subplot.bottom': args.plot_size})
plt.style.use(args.pyplot_style)
# First we create `means` which map a command label and date to a pair of mean and standard deviation
# e.g. means['Bean']['2017-05-02 13:29:34.87'] = (0.1289, 0.0006)
(means, cmd_labels, meta_keys) = util.means_series_map(args)
# Let's write a plot line for each command label
fig, ax = plt.subplots()
lines = []
for cmd_label in cmd_labels:
x, y, err = ([], [], [])
for i in range(len(meta_keys)):
(mean, std) = means[cmd_label].get(meta_keys[i], (0, 0))
x.append(i)
y.append(mean)
err.append(std)
lines.append(ax.errorbar(x, y, fmt='-o', yerr=err))
if args.ymin is not None:
plt.ylim(ymin=float(args.ymin))
if args.ymax is not None:
plt.ylim(ymax=float(args.ymax))
if args.title is not None:
plt.title(util.texsafe(args.title))
# Add some text for labels, title and axes ticks
ax.set_xticks(range(len(meta_keys)))
ax.set_xticklabels(meta_keys, rotation=args.xticklabel_rotation)
if not args.no_legend and not args.mpld3: # mpld3 comes with its own legends
ax.legend(lines, cmd_labels, loc='best', fancybox=True, shadow=True)
if args.mpld3:
import mpld3
from mpld3 import plugins
interactive_legend = plugins.InteractiveLegendPlugin(lines, cmd_labels)
plugins.connect(fig, interactive_legend)
if args.output is not None:
fname = args.output.name
fformat = fname.split('.')[-1]
print ("Writing file '%s' using format '%s'." % (fname, fformat))
if args.mpld3:
if fformat == "html":
mpld3.save_html(fig, args.output)
elif fformat == "json":
mpld3.save_json(fig, args.output)
else:
raise ValueError("--mpld3: The output must be either `html` or `json`")
else:
pylab.savefig(args.output, format=fformat)
else:
if args.mpld3:
mpld3.show()
else:
pylab.show()
fg = folium.FeatureGroup('my map')
min = 200
max = 300
m = folium.Map(location=[12.9752848, 77.5070119], zoom_start=10)
# MON = list(data["Mon"])
# TUE = list(data["Tue"])
x=[' ', '']
y=[5,9]
plt.xlabel('MON________________________________________________TUE ')
plt.ylabel('Sales->')
plt.title('Data')
# plt.bar(x,y)
plt.bar(x,y)
plt.savefig("teettt.png")
vis3= mpld3.save_json(plt.figure(), "teettttttt.html")
n = "teettttttt.html"
html_image = "<!DOCTYPE html><html><body><h2>HTML Image</h2><img src='C:/Users/admin/Desktop/teettt.jpg' alt=\"Girl in a jacket\" width=\"500\" height=\"600\"></body></html>"
bar = "http://127.0.0.1:8898/"
text_file = open("test_image.html", "w")
n = text_file.write(html_image)
text_file.close()
cluster = folium.plugins.MarkerCluster().add_to(m)
def to_json(self):
str_buffer = StringIO.StringIO()
mpld3.save_json(self.content, str_buffer)
return str_buffer.getvalue()