def plot(self):
if self.backend == "mpl":
plt.show()
elif self.backend == "ipt":
iplot_mpl(self.fig)
else:
plot_mpl(self.fig)
def plot(herd, title):
colors = ['r','g','b']
fig, ax = plt.subplots()
plt.title(title)
for num, group in enumerate(herd.groups):
x = [cow.energy_requirement for cow in group]
y = [cow.protein_requirement for cow in group]
ax.scatter(x, y, color=colors[num%3])
py.plot_mpl(fig, filename=title+".html", image_filename=title)
def plot(herd, title):
colors = ['r', 'g', 'b']
fig, ax = plt.subplots()
plt.title(title)
for num, group in enumerate(herd.groups):
x = [cow.energy_requirement for cow in group]
y = [cow.protein_requirement for cow in group]
ax.scatter(x, y, color=colors[num % 3])
py.plot_mpl(fig, filename=title + ".html", image_filename=title)
def get_plot_path_matplotlib_plotly(self, file_name='matplotlib_plotly.html'):
path_plotly = self.path_dir_plotly_html + os.sep + file_name
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = np.pi * (15 * np.random.rand(N)) ** 2 # 0 to 15 point radii
scatter_mpl_fig = plt.figure()
plt.scatter(x, y, s=area, c=colors, alpha=0.5)
pyof.plot_mpl(scatter_mpl_fig, filename=path_plotly, resize=True, auto_open=False)
return path_plotly
trace=True,
error_action='ignore',
suppress_warnings=True,
stepwise=True)
print(stepwise_model.aic())
stepwise_model.fit(train)
future_forecast = stepwise_model.predict(n_periods=test.shape[0])
print(future_forecast)
future_forecast = pd.DataFrame(future_forecast, index=test.index)
if i == 0:
plt.title('EC2-Instances(USD)')
if i == 1:
plt.title('EC2-Andere(USD)')
if i == 2:
plt.title('EC2-ELB(USD)')
if i == 3:
plt.title('S3(USD)')
if i == 4:
plt.title('Gesamtkosten (USD)')
plt.plot(future_forecast, label='Prediction')
plt.plot(test, label='True')
plt.legend()
plt.show()
result = seasonal_decompose(data, model='multiplicative', freq=10)
fig = result.plot()
plot_mpl(fig)
import csv
import matplotlib.pyplot as plt
import plotly
plotly.offline.init_notebook_mode(connected=True)
import plotly.offline as py
import numpy as np
# Learn about API authentication here: https://plot.ly/python/getting-started
# Find your api_key here: https://plot.ly/settings/api
results = []
with open("result2.csv") as csvfile:
reader = csv.reader(
csvfile, quoting=csv.QUOTE_NONNUMERIC) # change contents to floats
for row in reader: # each row is a list
results.append(row)
x = results[0]
y = results[1]
fig, ax = plt.subplots()
ax.scatter(x, y)
plot_url = py.plot_mpl(fig, filename="mpl-scatter")