joshdorrington

#run in python 2.7
from hmmlearn.hmm import GaussianHMM
data=np.fromfile("np_input_file.txt").reshape([200001,6])

#MODEL PARAMS
K=3 
iter_num=3000
convergence_tolerance=0.001
stat_vec_tol=0.999
 

joshdorrington

import numpy as np
import pandas as pd
from pomegranate import *

#IMPORT THE SAME DATA FROM 2 FILE FORMATS USING 2 METHODS
pd_data_df=pd.read_csv("pd_input_file.txt",engine="python", delimiter="\\t",names=['x1','x2','x3','x4','x5','x6'])
pd_data=pd_data_df.values.reshape([1,200001,6])
np_data=np.fromfile("np_input_file.txt").reshape([1,200001,6])

#IF ALL 3 OF THESE STATEMENTS ARE TRUE THEN THE DATA SHOULD BE TOTALLY INDISTINGUISHABLE FROM EACH OTHER:

joshdorrington

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from matplotlib.cbook import get_sample_data

def imscatter(x, y, image, ax=None, zoom=1,alpha=1):
    if ax is None:
        ax = plt.gca()
    try:
        image = plt.imread(image)

joshdorrington

import numpy as np
import xarray as xr
from scipy.interpolate import griddata
import napari
import matplotlib.pyplot as plt
from vispy.color import Colormap
import cmocean.cm as cm

# Make land colormap with transparent ocean:
cmap = plt.cm.gist_earth