#!/usr/bin/env python
# coding: utf-8

# As a research of bird behavior I am tracking birds with accelerometers using the https://uva-bits.nl system. To study the birds energy usage I want to create a machine learning model. To train this model I need some artificial data. 
# 
# # Real data set
# 
# The https://github.com/NLeSC/eEcology-Annotation-UI/raw/master/demo/tracker.json was derived from the uva-bits database of tracker 355 on 2010-06-28.

# In[12]:


import json
import urllib.request
import pandas as pd

with urllib.request.urlopen('https://github.com/NLeSC/eEcology-Annotation-UI/raw/master/demo/tracker.json') as f:
    data = json.load(f)

frame = 34
df = pd.DataFrame({
    'x': data[frame]['x_acceleration'],
    'y': data[frame]['y_acceleration'], 
    'z': data[frame]['z_acceleration']}, 
    index=data[frame]['time_acceleration']
)
df.info()
df.plot()


# # Let us recreate this with sequgen

# In[56]:


import numpy as np
from sequgen.deterministic.sine import sine
from sequgen.deterministic.constant import constant


# In[79]:


t_predict = np.linspace(0, 2, 40) # 2 seconds of 20Hz


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x = sine(t_predict, wavelength=2/6, amplitude=0.25, phase_shift=0.25) + \
    sine(t_predict, wavelength=2/6, amplitude=0.1, phase_shift=0.1) + \
    constant(t_predict, -0.2)


# In[81]:


z = sine(t_predict, 
         wavelength=2/6, # 6 flaps in 2 seconds
         amplitude=1, 
         phase_shift=0.05) + constant(t_predict, 1) # add Earths gravity


# In[82]:


y = sine(t_predict, wavelength=2/6, amplitude=0.25, phase_shift=0.05) + \
    sine(t_predict, wavelength=2/6, amplitude=0.1, phase_shift=0.2)


# In[83]:


pd.DataFrame({'x':x, 'y':y, 'z': z}, index=t_predict).plot()


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