#!/usr/bin/python

# -*- coding: utf-8 -*-

from __future__ import division

import datetime as dt

import numpy as np

import matplotlib.pyplot as plt

import spacepy

from spacepy.toolbox import binHisto

import spacepy.plot as splot

import substorm_model as msm

splot.style('spacepy')

sdict = dict()

sdict['ACE'] = {'delta': dt.timedelta(seconds=60),

'minlen': dt.timedelta(hours=100),

'tau0': 2.75*3600,

}

sdict['WIND'] = {'delta': 60,

'minlen': 60*60*100,

'tau0': 2.69*3600,

}

numericDelta = 60

satname = 'ACE'#'WIND' #'ACE'

if satname=='ACE':

data = spacepy.datamodel.fromHDF5('BAS_ACEdata.h5')

data['time'] = spacepy.datamodel.dmarray([dt.datetime.strptime(z.decode('UTF-8'), '%Y-%m-%dT%H:%M:%S') for z in data['time']])

vel = np.sqrt(data['vx']**2 + data['vy']**2 + data['vz']**2)*1e3

b2 = data['bx']**2 + data['by']**2 + data['bz']**2

btot = np.sqrt(b2)*1e-9

theta = np.arctan2(data['by'], data['bz'])

pow_in = np.sin(theta/2.)**4 * vel * btot**2

elif satname=='WIND':

data = spacepy.datamodel.fromHDF5('Wind_NAL.h5')

pow_in = data['input']

istart, iend = msm.findContiguousData(data['time'], sdict[satname]['delta'],

minLength=sdict[satname]['minlen'])

results = msm.msm(numericDelta, data['time'], pow_in, istart,

iend, tau0=sdict[satname]['tau0'], restartmode='random') #ACE is 2.75; Wind is 2.69

# plot histogram of inter-substorm intervals

def convert_tdelt(inputlist, units='hours'):

return np.asarray(outputlist)

isi = convert_tdelt(results['tau_valid'], units='minutes')

isi_hr = convert_tdelt(results['tau_valid'], units='hours')

tau_ax = np.arange(0,30*60,.2)

try:

from sklearn.neighbors.kde import KernelDensity

kern_type = 'epanechnikov'

kern_lab = '{0}{1} KDE'.format(kern_type[0].upper(), kern_type[1:])

kernel = KernelDensity(kernel=kern_type, bandwidth=60).fit(isi[:, np.newaxis])

kde_plot = np.exp(kernel.score_samples(tau_ax[:, np.newaxis]))

except ImportError:

from scipy import stats

kern_lab = 'Gaussian KDE'

kernel = stats.gaussian_kde(isi, bw_method='scott')

kde_plot = kernel.evaluate(tau_ax)

fig, ax = splot.set_target(None)

ax.hist(isi_hr, bins=np.arange(0,25,0.5), histtype='step', density=True, lw=1.5, label='Binned Data')

ax.plot(tau_ax/60., kde_plot*60., lw=1.5, label=kern_lab)

ax.set_xlim([0,25])

ax.set_ylabel('Probability')

ax.set_xlabel(r'Inter-substorm Interval, $\tau$ [hours]') #raw string req'd (else \t in \tau becomes [tab]au

ax.legend()

fig.suptitle('MSM$_{Python}$: ' + '{0} (1998-2002)'.format(satname))

plt.show()