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import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import statsmodels.api as sm

#Load the PCE and UMCSENT datasets
df = pd.read_csv(filepath_or_buffer='UMCSENT_PCE.csv', header=0, index_col=0,
                 infer_datetime_format=True, parse_dates=['DATE'])
#Set the index frequency to 'Month-Start'
df = df.asfreq('MS')

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import numpy as np
import pandas as pd
from lifelines import KaplanMeierFitter
from lifelines.statistics import survival_difference_at_fixed_point_in_time_test
from lifelines import CoxPHFitter
from matplotlib import pyplot as plt

#dataset link
#https://statistics.stanford.edu/research/covariance-analysis-heart-transplant-survival-data
#http://www.stat.rice.edu/~sneeley/STAT553/Datasets/survivaldata.txt

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import pandas as pd
import scipy.stats as st
import statsmodels.api as sm
import statsmodels.graphics.tsaplots as tsap
from statsmodels.compat import lzip
from statsmodels.stats.diagnostic import het_white
from matplotlib import pyplot as plt
import seaborn as sns

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import pandas as pd
import statsmodels.formula.api as smf
import statsmodels.api as sm
from patsy import dmatrices
from matplotlib import pyplot as plt
import numpy as np


#Load the US Census Bureau data into a Dataframe
df = pd.read_csv('us_census_bureau_acs_2015_2019_subset.csv', header=0)

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import pandas as pd
from patsy import dmatrices
import numpy as np
import statsmodels.api as sm
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt

#create a pandas DataFrame for the counts data set
df = pd.read_csv('nyc_bb_bicyclist_counts.csv', header=0, infer_datetime_format=True, parse_dates=[0], index_col=[0])

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import pandas as pd
from patsy import dmatrices
import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt


#Create a pandas DataFrame for the counts data set.
df = pd.read_csv('nyc_bb_bicyclist_counts.csv', header=0, infer_datetime_format=True, parse_dates=[0], index_col=[0])

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    def nloglikeobs(self, params):
        #Reconstitute the q and beta matrices from the current values of all the params
        self.reconstitute_parameter_matrices(params)

        #Build the regime wise matrix of Poisson means
        self.compute_regime_specific_poisson_means()

        #Build the matrix of Markov transition probabilities by standardizing all the q values to
        # the 0 to 1 range
        self.compute_markov_transition_probabilities()

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