import numpy as np
import plotly.graph_objects as go

step = 1 / 1000 t = np.arange(0, 1, step) # time vector
periods = 30 # number of 'days'

# function to produce base sine data
# with a 7th of the base frequency overlap
def data_w_weekend(t):
  if np.sin(periods / 7 * 2 * np.pi * t) > 0.5:
    value = 0.001 * np.sin(periods * 2 * np.pi * t)
    return max(value, 0.0)
  else:
    value = np.sin(periods * 2 * np.pi * t)
    return max(value, 0.0)

# building the data vector
my_data = []
i = 0
while i < 1000:
  my_data.append(data_w_weekend(i / 1000))
  i += 1

# add some noise
random_gain = 0.1  # factor for the noise
i = 0
while i < 1000:
  my_data[i] += np.random.rand() * random_gain
  i += 1

my_data_max = np.amax(my_data)
print('max value is: ' + str(my_data_max))
# normalize the data to a range up to 1.0
my_norm_data = []
i = 0
while i < 1000:
  my_norm_data.append(my_data[i]/my_data_max)
  i += 1

# plot the data
trace0 = go.Scatter(
  x = t,
  y = my_norm_data,
  name='Logons'
)
fig = go.Figure()

layout = go.Layout(title="Logins over time", xaxis={'title':'time'}, yaxis={'title':'occurences'})
fig = go.Figure(data=trace0, layout=layout)
fig.show()