from glasflow import RealNVP
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from itertools import chain, permutations
from datetime import datetime
import pickle
from scipy.interpolate import RectBivariateSpline
from scipy.integrate import quad
import time
import shutil

# for JS diveregcen calculation
from scipy.spatial.distance import jensenshannon
from scipy.stats import gaussian_kde
from collections import namedtuple

import seaborn as sns
import os

from scipy.stats import norm, chi2
from scipy.special import expit

from nessai.flowsampler import FlowSampler
from nessai.model import Model
from nessai.utils import setup_logger
from nessai.livepoint import dict_to_live_points
import corner

seed = 1
torch.manual_seed(seed)
np.random.seed(seed)
sns.set_context("notebook")
sns.set_palette("colorblind")
device = "cuda"
date_id = datetime.today().isoformat()
plot_path = '/data/www.astro/chrism/uroboros/{}'.format(date_id)
ns_path = './'
try:
    os.mkdir('{}'.format(plot_path))
except:
    print('unable to make output directory {}'.format(plot_path))
    exit(1)
shutil.copyfile('./uroboros.py', '{}/uroboros.txt'.format(plot_path))

iterations = 500 #200000
batch_size = 1024
plot_step = 50 #10000
n_test = 2    # number of individual test data samples
n_prior = 128  # number of samples used to represent the conditonal prior
n_max = 2     # max the number of measurements per sample
glob_n_max = n_max
n_par = 3     # the number of hyperparameters
n_meas = 64 # the size of a measurement
n_prior_cc_out = 16  # the size of compressed prior
n_meas_cc_out = 16  # the size of compressed measurement
n_marg = 1     # the number of parameters per measurememnt to be marginalised
n_sigma = 1.0   # the scale of the distance noise
n_post = 3000   # the number of posterior samples used for plotting
lr = 1e-3     # the learning rate
run_id = 'cw_amp_s{}_nmax{}_ntest{}'.format(seed,n_max,n_test)
Nreorder = 10
multiflow = False
multiflow_n_max = 1
if multiflow:
    multiflow_n_max = n_max

# define the Flow that will estimate the parameters conditional on new data and compressed prior information
flow = []
for i in range(multiflow_n_max):
    flow.append(RealNVP(
    n_inputs=n_par,                           # number of params
    n_transforms=4,
    n_conditional_inputs=n_prior_cc_out+n_meas_cc_out + 1,      # size of compressed prior plus size of measurement plus segment index
    n_neurons=32,
    n_blocks_per_transform=2,
    batch_norm_within_blocks=True,
    linear_transform='permutation',
    batch_norm_between_transforms=True,
).to(device))
print(f"Created flow and sent to {device}")

class MAB(nn.Module):
    def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
        super(MAB, self).__init__()
        self.dim_V = dim_V
        self.num_heads = num_heads
        self.fc_q = nn.Linear(dim_Q, dim_V)
        self.fc_k = nn.Linear(dim_K, dim_V)
        self.fc_v = nn.Linear(dim_K, dim_V)
        if ln:
            self.ln0 = nn.LayerNorm(dim_V)
            self.ln1 = nn.LayerNorm(dim_V)
        self.fc_o = nn.Linear(dim_V, dim_V)

    def forward(self, Q, K):
        Q = self.fc_q(Q)
        K, V = self.fc_k(K), self.fc_v(K)

        dim_split = self.dim_V // self.num_heads
        Q_ = torch.cat(Q.split(dim_split, 2), 0)
        K_ = torch.cat(K.split(dim_split, 2), 0)
        V_ = torch.cat(V.split(dim_split, 2), 0)

        A = torch.softmax(Q_.bmm(K_.transpose(1,2))/np.sqrt(self.dim_V), 2)
        O = torch.cat((Q_ + A.bmm(V_)).split(Q.size(0), 0), 2)
        O = O if getattr(self, 'ln0', None) is None else self.ln0(O)
        O = O + F.relu(self.fc_o(O))
        O = O if getattr(self, 'ln1', None) is None else self.ln1(O)
        return O

class SAB(nn.Module):
    def __init__(self, dim_in, dim_out, num_heads, ln=False):
        super(SAB, self).__init__()
        self.mab = MAB(dim_in, dim_in, dim_out, num_heads, ln=ln)

    def forward(self, X):
        return self.mab(X, X)

class ISAB(nn.Module):
    def __init__(self, dim_in, dim_out, num_heads, num_inds, ln=False):
        super(ISAB, self).__init__()
        self.I = nn.Parameter(torch.Tensor(1, num_inds, dim_out))
        nn.init.xavier_uniform_(self.I)
        self.mab0 = MAB(dim_out, dim_in, dim_out, num_heads, ln=ln)
        self.mab1 = MAB(dim_in, dim_out, dim_out, num_heads, ln=ln)

    def forward(self, X):
        H = self.mab0(self.I.repeat(X.size(0), 1, 1), X)
        return self.mab1(X, H)

class PMA(nn.Module):
    def __init__(self, dim, num_heads, num_seeds, ln=False):
        super(PMA, self).__init__()
        self.S = nn.Parameter(torch.Tensor(1, num_seeds, dim))
        nn.init.xavier_uniform_(self.S)
        self.mab = MAB(dim, dim, dim, num_heads, ln=ln)

    def forward(self, X):
        return self.mab(self.S.repeat(X.size(0), 1, 1), X)

class SmallSetTransformer(nn.Module):
    def __init__(self,):
        super().__init__()
        self.enc = nn.Sequential(
            SAB(dim_in=n_par, dim_out=32, num_heads=4),
            SAB(dim_in=32, dim_out=32, num_heads=4),
        )
        self.dec = nn.Sequential(
            PMA(dim=32, num_heads=4, num_seeds=1),
            nn.Linear(in_features=32, out_features=n_prior_cc_out),
            nn.Sigmoid(),  # I added this 
        )

    def forward(self, x):
        x = self.enc(x)
        x = self.dec(x)
        return x.squeeze(-2)

class PI_NeuralNetwork(nn.Module):
    def __init__(self):
        super().__init__()
        self.flatten = nn.Flatten()
        self.nn1 = nn.Sequential(
            nn.Linear(n_par, 64),
            nn.ReLU(),
            nn.Linear(64, 64),
            nn.ReLU(),
            nn.Linear(64, 64), 
            nn.ReLU(),
            nn.Linear(64, 64),            
            nn.ReLU(),
            nn.Linear(64, 64),
        )
        self.nn2 = nn.Sequential(
            nn.Linear(n_par + 64, 64),
            nn.ReLU(),
            nn.Linear(64, 64),
            nn.ReLU(),
            nn.Linear(64, 64),
            nn.ReLU(),
            nn.Linear(64, n_prior_cc_out),
        )

    def forward(self, x0):
        # the input data has shape (bs,n_prior,n_par+1)
        # we want to make this (bs*n_prior,n_par)
        x = x0.flatten(0,1)   # new shape (batch*n_prior,ndim)
        x = self.nn1(x)        # output shape (batch*n_prior,64)
        x = x.reshape(-1,n_prior,64)     # reshape to (batch,n_prior,64)
        print(x.shape,x0.shape)
        x = torch.concat([x,x0],dim=2)   # concat to get (batch, n_prior,64+ndim)
        x = torch.mean(x,dim=1)          # take mean to get (batch,64+ndim)
        x = self.nn2(x)       # process again to get (batch,n_prior_cc_out)   
        return x

cc_prior_model = []
for i in range(multiflow_n_max):
    cc_prior_model.append(SmallSetTransformer().to(device))

# the compression model that takes measurement samples and compresses them
cc_meas_model = nn.Sequential(
    nn.Linear(n_meas, 32),
    nn.ReLU(),
    nn.Linear(32, 32),
    nn.ReLU(),
    #nn.Linear(64, 64),
    #nn.ReLU(),
    nn.Linear(32, n_meas_cc_out),
    nn.Sigmoid()
)
cc_meas_model.to(device)

def calc_median_error(jsvalues, quantiles=(0.16, 0.84)):
    quants_to_compute = np.array([quantiles[0], 0.5, quantiles[1]])
    quants = np.percentile(jsvalues, quants_to_compute * 100)
    summary = namedtuple("summary", ["median", "lower", "upper"])
    summary.median = quants[1]
    summary.plus = quants[2] - summary.median
    summary.minus = summary.median - quants[0]
    return summary


def calculate_js(samplesA, samplesB, ntests=10, xsteps=100):
    js_array = np.zeros(ntests)
    for j in range(ntests):
        nsamples = min([len(samplesA), len(samplesB)])
        A = np.random.choice(samplesA, size=nsamples, replace=False)
        B = np.random.choice(samplesB, size=nsamples, replace=False)
        xmin = np.min([np.min(A), np.min(B)])
        xmax = np.max([np.max(A), np.max(B)])
        x = np.linspace(xmin, xmax, xsteps)
        A_pdf = gaussian_kde(A)(x)
        B_pdf = gaussian_kde(B)(x)

        js_array[j] = np.nan_to_num(np.power(jensenshannon(A_pdf, B_pdf), 2))

    return calc_median_error(js_array)

#def sig(Asin,Acos,f0,t,i):
#    """
#    phi0 - the phase normalise between 0 and 1
#    f0 - the frequency normalised in reference to the nyquist frequency (0-1)
#    N - the number of samples in teh timeseries
#    i - the index of the timeseries
#    """
#    f = (0.4 + 0.2*f0)   # fraction of the Nyquist frequency (0.4 - 0.6)
#    #return 0.25*np.sin(2.0*np.pi*(phi0 + f*0.5*(t + i)))
#    phase = 2.0*np.pi*(f*0.5*(t + i))
#    return 0.25*(Asin*np.cos(phase) + Acos*np.sin(phase))

class cw_model(Model):
    """A simple two-dimensional Gaussian likelihood."""

    def __init__(self,n_meas,n_max,d=None,i_ref=0):
        # Names of parameters to sample
        self.n_meas = n_meas
        self.dvec = d
        self.T = 1.0
        self.i_ref = i_ref  # the starting segment index
        self.dt = self.T/self.n_meas
        self.t = torch.arange(self.n_meas)*self.dt   # define time vector
        #self.t = np.arange(self.n_meas)*self.dt
        self.fmax = 0.5/self.dt
        self.n_max = n_max
        self.n_sigma = 1.0
        self.SNR_factor = 0.25 #0.25  # a scaling that controls the average SNR
        self.n_par = 3   # number of parameters
        self.names = ["A", "f0"]
        self.plot_bounds =[[0,3],[0,1]]
        self.bounds = {"A": [0, 10.0], "f0": [0, 1]}
        for i in range(n_max):
            self.names.append("phase{}".format(i))
            self.plot_bounds.append([0,1])
            self.bounds["phase{}".format(i)] = [0,1]
        #self.repar_bounds =[[0,1],[0,1],[0,3]]

    #def sig(self,x,i):
    #    """
    #    We assume that the input shape of x is [n_par,N] where N=n_data*n_max
    #    We assume that the input shape of i is [N]
    #    All tensors are expanded to have an extra dimension of n_meas
    #    Output has shape [N,n_meas]
    #    """
    #    N = x.shape[1]  # number of locations
    #    x = torch.tile(x.reshape(self.n_par,N,1),(1,1,self.n_meas))  # convert to (n_par,N,n_meas)
    #    Asin = x[0] # the phase sin quadrature (N,n_meas)
    #    Acos = x[1] # the phase cos quadrature (N,n_meas)
    #    f0 = x[2] # the frequency normalised in reference to the nyquist frequency (0-1) (N,n_meas)
    #    i = torch.tile(i.reshape(N,1),(1,n_meas)) #i - the index of the timeseries (N,n_meas)
    #
    #    f = (0.4 + 0.2*f0)*self.fmax   # f0 is fraction of the Nyquist frequency (0.4 - 0.6) (N,n_meas)
    #    phase = 2.0*np.pi*f*(torch.tile(self.t.reshape(1,n_meas),(N,1)) + i*self.T)  # shift the initial time in reference to first segment (N,n_meas)
    #    return self.SNR_factor*(Asin*np.cos(phase) + Acos*np.sin(phase)) # return timeseries (N,n_meas)

    def sig(self,A,f0,phase,t,i):
        """
        phi0 - the phase normalise between 0 and 1
        f0 - the frequency normalised in reference to the nyquist frequency (0-1)
        N - the number of samples in teh timeseries
        i - the index of the timeseries
        """
        f = (0.4 + 0.2*f0)*0.5   # fraction of the Nyquist frequency (0.4 - 0.6)
        #return 0.25*np.sin(2.0*np.pi*(phi0 + f*0.5*(t + i)))
        phi = 2.0*np.pi*(f*(t + i) + phase)
        return self.SNR_factor*A*np.sin(phi)

    def gen_pars(self,n_data,n_max):

        Asin = (torch.randn(size=(n_data,1))).to(device)
        Acos = (torch.randn(size=(n_data,1))).to(device)
        A = torch.sqrt(Asin**2 + Acos**2)
        phase = torch.remainder(torch.atan2(Asin,Acos),2*np.pi)/(2.0*np.pi)
        f0 = (torch.rand(size=(n_data,1))).to(device)    # the true f0 value (bs,1)
        Omega = (torch.concatenate((A,f0,phase),axis=1)).to(device)
        return Omega

    def gen_sig(self,n_data,n_max):

        Omega = self.gen_pars(n_data,n_max)
        A = Omega[:,0]
        f0 = Omega[:,1]
        phase = Omega[:,2]
        print(A,f0,phase)
        #Asin = (torch.randn(size=(n_data,1))).to(device)
        #Acos = (torch.randn(size=(n_data,1))).to(device)
        #f0 = (torch.rand(size=(n_data,1))).to(device)    # the true f0 value (bs,1)
        #Omega = (torch.concatenate((Asin,Acos,f0),axis=1)).to(device)
        n = (self.n_sigma*torch.randn(size=(n_data,n_meas,n_max))).to(device)                                         # noise on distance (bs,n_max)

        # generate the signal - the inputs all have shape (n_data,n_meas,n_max). The output has the same shape.
        meas = self.sig(torch.tile(A.reshape(n_data,1,1),(1,n_meas,n_max)).flatten().cpu(),
                   torch.tile(f0.reshape(n_data,1,1),(1,n_meas,n_max)).flatten().cpu(),
                   torch.tile(phase.reshape(n_data,1,1),(1,n_meas,n_max)).flatten().cpu(),
                   torch.tile(torch.arange(n_meas).reshape(1,n_meas,1),(n_data,1,n_max)).flatten().cpu(),
                   torch.tile(n_meas*torch.arange(n_max).reshape(1,1,n_max),(n_data,n_meas,1)).flatten().cpu()).reshape(n_data,n_meas,n_max).to(device) + n
        meas = meas.reshape(n_data,n_meas,n_max).to(device)
        meas = meas.type(torch.cuda.FloatTensor)
        return meas, Omega, n

    def log_prior(self, x):
        """
        Returns log of prior given a live point assuming uniform
        priors on each parameter.
        """
        # Check if values are in bounds, returns True/False
        # Then take the log to get 0/-inf and make sure the dtype is float
        log_p = np.log(self.in_bounds(x), dtype="float")
        # Iterate through each parameter (x and y)
        # since the live points are a structured array we can
        # get each value using just the name
        #for n in self.names:
        log_p += chi2(2).logpdf(x["A"])
        log_p -= np.log(self.bounds["f0"][1] - self.bounds["f0"][0])                  # uniform prior
        for i in range(self.n_max):
            log_p -= np.log(self.bounds["phase{}".format(i)][1] - self.bounds["phase{}".format(i)][0])
        return log_p

    def log_likelihood(self, x):
        """
        Returns log likelihood of given live point assuming a Gaussian
        likelihood.
        """
        log_l = 0.0  # initialise the log likelihood
        A = torch.from_numpy(np.array(x["A"])).reshape(-1,1)
        f0 = torch.from_numpy(np.array(x["f0"])).reshape(-1,1)
        N = A.shape[0]
        for i,d in enumerate(self.dvec):  # loop over measurements
            #Omega = torch.from_numpy(np.array([x["A"],x["f0"],x["phase{}".format(i)]])).reshape(self.n_par,-1)
            phase = torch.from_numpy(np.array(x["phase{}".format(i)]))
            idx = torch.from_numpy(np.ones(A.shape[0])*i) + self.i_ref
            s = self.sig(torch.tile(A.reshape(N,1,1),(1,n_meas,1)).flatten().cpu(),
                   torch.tile(f0.reshape(N,1,1),(1,n_meas,1)).flatten().cpu(),
                   torch.tile(phase.reshape(N,1,1),(1,n_meas,1)).flatten().cpu(),
                   torch.tile(torch.arange(n_meas).reshape(1,n_meas,1),(N,1,1)).flatten().cpu(),
                   torch.tile(n_meas*i*torch.ones(1).reshape(1,1,1),(N,n_meas,1)).flatten().cpu()).reshape(N,n_meas,1)
            s = s.reshape(N,n_meas)
            #s = self.sig(A,f0,phase,torch.arange(self.n_meas),idx)
            log_l += np.sum(norm.logpdf(s,loc=d,scale=self.n_sigma))
        return log_l

    #def log_likelihood(self, x):
    #    """
    #    Returns log likelihood of given live point assuming a Gaussian
    #    likelihood.
    #    """
    #    log_l = 0.0
    #    for i,d in enumerate(self.dvec):
    #        s = sig(x["Asin"],x["Acos"],x["f0"],np.arange(self.n_meas),self.n_meas*i + self.i_ref*self.n_meas)
    #        log_l += np.sum(norm.logpdf(s,loc=d,scale=n_sigma))
    #    return log_l

    def change_vars(self,samples):
        return samples
        # convert samples in [Asin, Acos, f0] to [phase, f0, Amp]
        #N = samples.shape[0]
        #new_samples = torch.zeros((N,self.n_par))
        #new_samples[:,2] = torch.sqrt(samples[:,0]**2 + samples[:,1]**2)
        #new_samples[:,1] = samples[:,2]
        #new_samples[:,0] = torch.remainder(torch.atan2(samples[:,0],samples[:,1]),2*np.pi)/(2.0*np.pi)
        #return new_samples

    #def shift_time(self,samples,i):
    #    # convert samples in [Asin, Acos, f0] to a different reference time
    #    N = samples.shape[0]
    #    new_samples = torch.zeros((N,self.n_par))
    #    phase = torch.remainder(torch.atan2(samples[:,0],samples[:,1]),2*np.pi)/(2.0*np.pi)
    #    f = (0.4 + 0.2*samples[:,2])*0.5
    #    new_phase = phase + 2.0*np.pi*f*self.T*i 
    #    A = torch.sqrt(samples[:,0]**2 + samples[:,1]**2)
    #    new_samples[:,0] = A*torch.sin(new_phase)
    #    new_samples[:,1] = A*torch.cos(new_phase)
    #    new_samples[:,2] = samples[:,2]
    #    return new_samples        

    def scatter_plot(self,ax,samples,ns_samples,reord_samples,x,nd,n_prior,n_post,n_par,change_vars=False):

        samples = torch.permute(samples,(0,3,1,2))[0,:,:,:].reshape(nd,n_post,n_par)
        #old_samples = torch.permute(old_samples,(0,3,1,2))[0,:,:,:].reshape(nd,n_prior,n_par)
        reord_samples = torch.permute(reord_samples,(0,3,1,2))[0,:,:,:].reshape(nd,-1,n_par)
        if change_vars==True:
            samples = signal_model.change_vars(samples[:,:,:n_par].flatten(0,1)).reshape(nd,n_post,n_par).cpu().numpy()
            #old_samples = signal_model.change_vars(old_samples[:,:,:n_par].flatten(0,1)).reshape(nd,n_prior,n_par).cpu().numpy()
            reord_samples = signal_model.change_vars(reord_samples[:,:,:n_par].flatten(0,1)).reshape(nd,-1,n_par).cpu().numpy()
            x = signal_model.change_vars(x.reshape(1,n_par)).flatten().cpu().numpy()
            ns_samples = signal_model.change_vars(torch.from_numpy(ns_samples).flatten(0,2)).reshape(n_max,-1,2,n_par).cpu().numpy()
        else:
            samples = samples.cpu().numpy()
            #old_samples = old_samples.cpu().numpy()
            reord_samples = reord_samples.cpu().numpy()
            x = x.flatten().cpu().numpy()
            #ns_samples = ns_samples.flatten().reshape(n_max,-1,2,n_par).cpu().numpy()

        for k in range(nd-1):
            #            prior_s = old_samples[k+1,:,:n_par].reshape(n_prior,n_par)
            post_s = samples[k,:,:n_par].reshape(n_post,n_par)

            js_a = 1e3*calculate_js(post_s[:,a], ns_samples[k,:,0,a]).median
            js_b = 1e3*calculate_js(post_s[:,b], ns_samples[k,:,0,b]).median

            ax[k].plot(post_s[:,a],post_s[:,b],'.b',alpha=0.5,markersize=1)
            ax[k].plot(ns_samples[k,:,0,a],ns_samples[k,:,0,b],'.r',alpha=0.5,markersize=1)
            ax[k].plot(ns_samples[k,:,1,a],ns_samples[k,:,1,b],'.g',alpha=0.5,markersize=1)
            ax[k].annotate('JS = {:.0f},{:.0f}'.format(js_a,js_b),(0.5,0.9),xycoords='axes fraction')
            #            #ax[j,k].set_xlim(signal_model.repar_bounds[a])
            #            #ax[j,k].set_ylim(signal_model.repar_bounds[b])
            if change_vars==True:
                ax[k].set_xlim(self.repar_bounds[a])
                ax[k].set_ylim(self.repar_bounds[b])
            else:
                ax[k].set_xlim(self.plot_bounds[a])
                ax[k].set_ylim(self.plot_bounds[b])
            ax[k].plot(x[a],x[b],'xk',label='truth',markersize=10)

        js_a = 1e3*calculate_js(samples[-1,:,a], ns_samples[-1,:,0,a]).median
        js_b = 1e3*calculate_js(samples[-1,:,b], ns_samples[-1,:,0,b]).median

        ax[nd-1].plot(samples[-1,:,a],samples[-1,:,b],'.b',alpha=0.5,markersize=1)
        ax[nd-1].plot(ns_samples[-1,:,0,a],ns_samples[-1,:,0,b],'.r',alpha=0.5,markersize=1)
        ax[nd-1].plot(ns_samples[-1,:,1,a],ns_samples[-1,:,1,b],'.g',alpha=0.5,markersize=1)
        ax[nd-1].plot(x[a],x[b],'xk',markersize=10)
        ax[nd-1].annotate('JS = {:.0f},{:.0f}'.format(js_a,js_b),(0.5,0.9),xycoords='axes fraction')
        #        #ax[j,nd-1].set_xlim(signal_model.repar_bounds[a])
        #        #ax[j,nd-1].set_ylim(signal_model.repar_bounds[b])
        if change_vars==True:
            ax[nd-1].set_xlim(self.repar_bounds[a])
            ax[nd-1].set_ylim(self.repar_bounds[b])
        else:
            ax[nd-1].set_xlim(self.plot_bounds[a])
            ax[nd-1].set_ylim(self.plot_bounds[b])

        js_a = 1e3*calculate_js(reord_samples[-1,:,a], ns_samples[-1,:,0,a]).median
        js_b = 1e3*calculate_js(reord_samples[-1,:,b], ns_samples[-1,:,0,b]).median

        ax[nd].plot(reord_samples[-1,:,a],reord_samples[-1,:,b],'.k',alpha=0.5,markersize=1)
        ax[nd].plot(ns_samples[-1,:,0,a],ns_samples[-1,:,0,b],'.r',alpha=0.5,markersize=1)
        #ax[nd].plot(ns_samples[-1,:,1,a],ns_samples[-1,:,1,b],'.g',alpha=0.5,markersize=1)
        ax[nd].plot(x[a],x[b],'xk',markersize=10)
        ax[nd].annotate('JS = {:.0f},{:.0f}'.format(js_a,js_b),(0.5,0.9),xycoords='axes fraction')
        #        #ax[j,nd-1].set_xlim(signal_model.repar_bounds[a])
        #        #ax[j,nd-1].set_ylim(signal_model.repar_bounds[b])
        if change_vars==True:
            ax[nd].set_xlim(self.repar_bounds[a])
            ax[nd].set_ylim(self.repar_bounds[b])
        else:
            ax[nd].set_xlim(self.plot_bounds[a])
            ax[nd].set_ylim(self.plot_bounds[b])

    def plot_test_data(self,d_test_tensor,n_test_tensor,plot_path):
        """
        Function to plot the test data
        """
        d_test = d_test_tensor.cpu().numpy()
        n_test = n_test_tensor.cpu().numpy()
        fig, ax = plt.subplots(d_test.shape[0],1, figsize=(32, 3*d_test.shape[0]), dpi=100)
        i = 0
        for d,n in zip(d_test,n_test):
            t = np.linspace(0,self.T*d.shape[1],d.shape[1]*d.shape[0])
            ax[i].plot(t,d.flatten())
            ax[i].plot(t,d.flatten()-n.flatten())
            i += 1
        plt.savefig('{}/test_data.png'.format(plot_path))
        plt.close()

def make_data(n_data,n_prior=100,n_max=1,n_post=None,flow=None,cc_prior_model=None,cc_meas_model=None,meas=None,valtest=False):
    """
    function to make training data
    n_data = number of training samples to make
    n_prior = the number of samples to use from the prior before compression 
    n_post = the number of posterior samples 
    n_max = the max number of measurements per training data sample
    meas = the distance measurements
    """
    Omega = None
    n = None
    if meas is None:
        meas, Omega, n = cw_model(n_meas,n_max).gen_sig(n_data,n_max)

    # put everything else into eval mode
    for f,c in zip(flow,cc_prior_model):
        f.eval()
        c.eval()

    # compress the measured data - we should be in training mode for this since
    # it is the only place we do the compression
    if not valtest:
        cc_meas_model.train()
        c_meas = torch.zeros(n_data,n_meas_cc_out,n_max).to(device)
        for i in range(n_max):
            c_meas[:,:,i] = cc_meas_model(meas[:,:,i].detach())
    else:        
        cc_meas_model.eval()
        c_meas = torch.zeros(n_data,n_meas_cc_out,n_max).to(device)
        for i in range(n_max):
            with torch.no_grad():
                c_meas[:,:,i] = cc_meas_model(meas[:,:,i].detach())

    # initialise the prior samples tensor
    # there should be a small set of prior samples (and log probs) for each measurement and for each sample 
    prior_label = torch.zeros(n_data,n_prior,n_par,n_max).to(device)   # initialise the prior label tensor

    # PRIOR 1 - for 1st signals sample from the original prior
    #test = flow.inverse(torch.ones(n_data*n_prior,n_par),conditional=test_cond).to(device)
    #prior_label[:,:,0,0] = test[:,0].reshape(n_prior,n_data).transpose(1,0).to(device)
    prior_label[:,:,0,0] = torch.sqrt(torch.randn(size=(n_data,n_prior))**2 + torch.randn(size=(n_data,n_prior))**2).to(device)
    prior_label[:,:,1,0] = torch.rand(size=(n_data,n_prior)).to(device)
    prior_label[:,:,2,0] = torch.rand(size=(n_data,n_prior)).to(device)
    #prior_label[:,:,3,0] = (-0.5*prior_label[:,:,0,0]**2 - 0.5*prior_label[:,:,1,0]**2 - np.log(2.0*np.pi)).to(device) #torch.zeros(size=(n_data,n_prior)).to(device)
    
    # sort the prior samples by log-lik order
    #temp, idx = torch.sort(prior_label[:,:,3,0],dim=1)
    #prior_label[:,:,3,0] = temp 

    ###############################################################
    # we can stop here IF ONLY 1 measurement is being considered
    # Otherwise we need to put the 1st (nth) measurement through the flow to get a new prior for the 2nd (n+1) measurement 
    #flow.eval()              # we DO NOT train the flow in the data generation step
    #cc_prior_model.eval()    # we DO NOT train the prior compression in the data generation step
    for i in range(n_max-1):   # loop over each event from the zeroth to the n-1'th (we don't want to do the last one)
        ii = i if multiflow else 0
        with torch.no_grad():
            c_prior = cc_prior_model[ii](prior_label[:,:,:,i]).detach() #.flatten(1,2)).detach()    # compress the i'th prior data
        test_cond = torch.cat((c_meas[:,:,i].detach(),c_prior,(i/float(glob_n_max))*torch.ones(size=(n_data,1)).to(device)),dim=1).to(device)    # combine the compressed measurement and prior and measurement indices
        test_cond = test_cond.tile(n_prior,1).to(device)        # tile it to generate n_prior samples for each of n_data (n_data*n_prior,n_cc+n_meas)
        with torch.no_grad():   # run the current flow state to generate new posterior -> prior samples and log-likelihoods 
            prior_samples = flow[ii].sample(n_data*n_prior,conditional=test_cond).to(device)   # output shape should be (n_data*n_prior,n_cos)
            #prior_samples, _ = flow.inverse(torch.tile(z_prior,(n_data,1)),conditional=test_cond)
            #prior_logprob = flow[ii].log_prob(prior_samples,conditional=test_cond).to(device)    # output shape should be (n_data*n_prior)

        # fill in the prior labels - these are now the priors for the NEXT measurement
        prior_label[:,:,0,i+1] = prior_samples[:,0].reshape(n_prior,n_data).transpose(1,0).to(device)
        prior_label[:,:,1,i+1] = prior_samples[:,1].reshape(n_prior,n_data).transpose(1,0).to(device)
        prior_label[:,:,2,i+1] = prior_samples[:,2].reshape(n_prior,n_data).transpose(1,0).to(device) 
        #prior_label[:,:,3,i+1] = prior_logprob.reshape(n_prior,n_data).transpose(1,0).to(device)

    # If we want the iteratively generated posteriors -> priors for plotting then we generate more samples
    # we still use the fixed lower number of samples generated above for each stage
    # and we now do compute the posterior after the final measurement 
    post_label = None
    if n_post is not None:

        post_label = torch.zeros(n_data,n_post,n_par,n_max).to(device)   # initialise the posterior label tensor
        #flow.eval()              # we DO NOT train the flow in the data generation step
        #cc_prior_model.eval()    # we DO NOT train the prior compression in the data generation step
        for i in range(n_max):
            ii = i if multiflow else 0
            # POST 1 - compress the uniform prior and add the 1st meas condition
            with torch.no_grad():
                c_prior = cc_prior_model[ii](prior_label[:,:,:,i]).detach() #.flatten(1,2)).detach()
            test_cond = torch.cat((c_meas[:,:,i].detach(),c_prior,(i/float(glob_n_max))*torch.ones(size=(n_data,1)).to(device)),dim=1).to(device)
            test_cond = test_cond.tile(n_post,1).to(device)        # has shape (n_data*n_prior,n_cc+n_meas)

            # keep doing this stage until we have the desired number of samples AFTER importance sampling
            #flag = True
            #rsum = 0
            #prior_samples = np.zeros((n_post,n_par))
            #prior_logprob = np.zeros(n_post)
            #while flag:
            with torch.no_grad():
                temp_prior_samples = flow[ii].sample(n_data*n_post,conditional=test_cond).to(device)   # output shape should be (n_data*n_post,n_cos)
                #temp_prior_logprob = flow[ii].log_prob(temp_prior_samples,conditional=test_cond).to(device)    # output shape should be (n_data*n_post)

            # fill in the posteriors - these are the posteriors AFTER each event
            post_label[:,:,0,i] = temp_prior_samples[:,0].reshape(n_post,n_data).transpose(1,0).to(device)
            post_label[:,:,1,i] = temp_prior_samples[:,1].reshape(n_post,n_data).transpose(1,0).to(device)
            post_label[:,:,2,i] = temp_prior_samples[:,2].reshape(n_post,n_data).transpose(1,0).to(device)
            #post_label[:,:,3,i] = temp_prior_logprob.reshape(n_post,n_data).transpose(1,0).to(device)

    return Omega, meas, c_meas, prior_label, post_label, n

# save or load the true params and measurements
try:
    os.mkdir('{}/{}'.format(ns_path,run_id))
except:
    pass
parfile = '{}/{}/testdata_{}.dat'.format(ns_path,run_id,run_id)
print(parfile)
if os.path.isfile(parfile)==False:

    # generate test data
    print('making test data')
    data_test_tensor, d_test_tensor, _, _, _, n_test_tensor = make_data(n_data=n_test,
                                                                        n_max=n_max,
                                                                        n_prior=n_prior,
                                                                        flow=flow,
                                                                        cc_prior_model=cc_prior_model,
                                                                        cc_meas_model=cc_meas_model,
                                                                        valtest=True)
    test_n_sig = torch.ones(n_test)*n_max
    print('made test data')

    # and save it to file
    with open(parfile, 'wb') as f:  # Python 3: open(..., 'wb')
        pickle.dump([data_test_tensor.cpu().numpy(), d_test_tensor.cpu().numpy(), n_test_tensor.cpu().numpy()], f)
    print('saved par file {}'.format(parfile))

else:

    # open the parfile and read the data and parameters
    with open(parfile, 'rb') as f:  # Python 3: open(..., 'rb')
        data_test_tensor, d_test_tensor, n_test_tensor = pickle.load(f)
    data_test_tensor = torch.tensor(data_test_tensor).to(device)
    d_test_tensor = torch.tensor(d_test_tensor).to(device)
    n_test_tensor = torch.tensor(n_test_tensor).to(device)
    test_n_sig = torch.ones(n_test)*n_max
    print('read in par file {}'.format(parfile))
    print('read in test params')

# plot test data
print('plotting test data')
signal_model = cw_model(n_meas,n_max)
signal_model.plot_test_data(d_test_tensor,n_test_tensor,plot_path)
print('plotted test data')

### TEMP FIX ### allows us to use existing saved data with the NS results
d_test_tensor = d_test_tensor.transpose(2,1)

# The FlowSampler object is used to managed the sampling. Keyword arguments
# are passed to the nested sampling.
j = 0
ns_samples = []
print(d_test_tensor.shape)
for x,d in zip(data_test_tensor.cpu().numpy(),d_test_tensor.cpu().numpy()):
    for i in range(n_max):
        for k in range(2):
            if os.path.isfile("{}/{}/ns_{}_{}_{}.dat".format(ns_path,run_id,i,j,k))==False:
                print('starting ns')
                output = "{}/{}/ns_{}_{}_{}/".format(ns_path,run_id,i,j,k)
                logger = setup_logger(output=output)
                print('data shape {}'.format(d.shape))
                print(d.shape)
                if k==0:
                    fs = FlowSampler(cw_model(n_meas,n_max,d=d[:i+1,:]), output=output, resume=False, reset_flow=8, volume_fraction=0.98, seed=1234)
                else:
                    fs = FlowSampler(cw_model(n_meas,n_max,d=d[i,:].reshape(1,-1),i_ref=i), output=output, reset_flow=8, volume_fraction=0.98, resume=False, seed=1234)
                fs.run()
                temp_ns_samples = []
                for s in fs.posterior_samples:
                    temp_ns_samples.append([s[q] for q in np.arange(n_par)])
                temp_ns_samples = np.array(temp_ns_samples,dtype=np.float64)
                ns_samples.append(temp_ns_samples)

                # save nested sampling samples
                print('saving ns')
                with open("{}/{}/ns_{}_{}_{}.dat".format(ns_path,run_id,i,j,k), "wb") as f:
                    temp_ns_samples.tofile(f)

            else:
                fn = '{}/{}/ns_{}_{}_{}.dat'.format(ns_path,run_id,i,j,k)
                print('loading ns file {}'.format(fn))
                with open("{}/{}/ns_{}_{}_{}.dat".format(ns_path,run_id,i,j,k), "rb") as f:
                    ns_samples.append(np.fromfile(f).reshape(-1,n_par).astype(np.float64))

    j += 1


# restructure the ns samples
k = 0
min_len = 1e16
for i in range(n_test):
    for j in range(n_max):
        for b in range(2):
            if ns_samples[k].shape[0] < min_len:
                min_len = ns_samples[k].shape[0]
            k += 1
ns_min = min(min_len,n_post)
temp_ns_samples = np.zeros((n_test,n_max,ns_min,2,n_par))
k = 0
for i in range(n_test):
    for j in range(n_max):
        for b in range(2):
            idx = np.random.choice(ns_samples[k].shape[0],size=ns_min)
            temp_ns_samples[i,j,:,b,:] = ns_samples[k][idx,:]
            k += 1

ns_samples = torch.from_numpy(temp_ns_samples).flatten(0,3).reshape(n_test,n_max,ns_min,2,n_par).cpu().numpy()

flow_pars = []
cc_prior_pars = [] #cc_prior_model[0].parameters()
for i in range(multiflow_n_max):
    flow_pars = chain(flow_pars,flow[i].parameters())
    cc_prior_pars = chain(cc_prior_pars,cc_prior_model[i].parameters())
all_pars = chain(flow_pars,cc_prior_pars,cc_meas_model.parameters())
optimiser = torch.optim.Adam(all_pars,lr=lr)

#optimiser = torch.optim.Adam(chain(flow.parameters(),cc_prior_model.parameters(),cc_meas_model.parameters()),lr=lr)
n_loss_avg = 1000
loss = dict(train=[], val=[])
train_loss_smooth = []
val_loss_smooth = []
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimiser, iterations, eta_min=1e-6, last_epoch=-1)
current_n_max = 1
sub_batch = np.zeros(n_max+1)
sub_batch[0] = batch_size

for i in range(iterations+1):

    # perform inference (while training) on the final measurement
    data_train_tensor = torch.empty(0,n_par).to(device)         # <--- initialize 
    train_cond = torch.empty(0,n_meas_cc_out+n_prior_cc_out+1).to(device)         # <--- initialize
    #orig_data_train_tensor, _, dist_train_tensor, priors_train_tensor, _ = make_data(n_data=batch_size,n_max=current_n_max,n_prior=n_prior,flow=flow,cc_prior_model=cc_prior_model,cc_meas_model=cc_meas_model)
    #start_idx = 0
    optimiser.zero_grad()
    _loss = 0.0
    for k in range(current_n_max):
        kk = k if multiflow else 0
        #end_idx = start_idx + int(sub_batch[k])
        # all networks are set to eval mode inside the data generation - except for the measurement compression
        temp_data_train_tensor, _, temp_dist_train_tensor, temp_priors_train_tensor, _, _ = make_data(n_data=int(sub_batch[k]),n_max=k+1,n_prior=n_prior,flow=flow,cc_prior_model=cc_prior_model,cc_meas_model=cc_meas_model)
        #temp_data_train_tensor = orig_data_train_tensor[start_idx:end_idx,:].detach()
        #temp_dist_train_tensor = dist_train_tensor[start_idx:end_idx,:,:k+1]
        #temp_priors_train_tensor = priors_train_tensor[start_idx:end_idx,:,:,:k+1].detach()
        temp_data_train_tensor = temp_data_train_tensor.detach()
        temp_priors_train_tensor = temp_priors_train_tensor.detach()
        cc_prior_model[kk].train()
        compressed_prior = cc_prior_model[kk](temp_priors_train_tensor[:,:,:,-1]) #.flatten(1,2))   # take the last event prior
        temp_train_cond = torch.cat((temp_dist_train_tensor[:,:,-1],compressed_prior,(k/float(glob_n_max))*torch.ones(size=(int(sub_batch[k]),1)).to(device)),dim=1).to(device)
        data_train_tensor = torch.cat((data_train_tensor,temp_data_train_tensor[:,:n_par]),dim=0)
        train_cond = torch.cat((train_cond,temp_train_cond),dim=0)
        #start_idx += int(sub_batch[k])

        flow[kk].train()
        #optimiser.zero_grad()
        _loss -= flow[kk].log_prob(data_train_tensor, conditional=train_cond).mean()*(sub_batch[k]/float(batch_size))      # compute the loss

    _loss.backward()
    optimiser.step()
    scheduler.step()
    train_loss = _loss.mean().item()
    loss["train"].append(train_loss)
    train_loss_smooth.append(np.median(loss["train"][max(i-n_loss_avg,0):]))

    # validation
    if not i % 100:
        data_val_tensor = torch.empty(0,n_par).to(device)         # <--- initialize
        val_cond = torch.empty(0,n_meas_cc_out+n_prior_cc_out+1).to(device)         # <--- initialize
        #orig_data_val_tensor, _, dist_val_tensor, priors_val_tensor, _ = make_data(n_data=batch_size,n_max=current_n_max,n_prior=n_prior,flow=flow,cc_prior_model=cc_prior_model,cc_meas_model=cc_meas_model,valtest=True)
        #start_idx = 0
        _loss = 0.0
        for k in range(current_n_max):
            ii = i if multiflow else 0
            #end_idx = start_idx + int(sub_batch[k])
            temp_data_val_tensor, _, temp_dist_val_tensor, temp_priors_val_tensor, _, _ = make_data(n_data=int(sub_batch[k]),n_max=k+1,n_prior=n_prior,flow=flow,cc_prior_model=cc_prior_model,cc_meas_model=cc_meas_model,valtest=True)
            #temp_data_val_tensor = orig_data_val_tensor[start_idx:end_idx,:].detach()
            #temp_dist_val_tensor = dist_val_tensor[start_idx:end_idx,:,:k+1].detach()
            #temp_priors_val_tensor = priors_val_tensor[start_idx:end_idx,:,:,:k+1].detach()
            temp_data_val_tensor = temp_data_val_tensor.detach()
            temp_dist_val_tensor = temp_dist_val_tensor.detach()
            temp_priors_val_tensor = temp_priors_val_tensor.detach()
            cc_prior_model[kk].eval()
            with torch.no_grad():
                compressed_prior = cc_prior_model[kk](temp_priors_val_tensor[:,:,:,-1]) #.flatten(1,2))   # take the last event prior    
            temp_val_cond = torch.cat((temp_dist_val_tensor[:,:,-1],compressed_prior,(k/float(glob_n_max))*torch.ones(size=(int(sub_batch[k]),1)).to(device)),dim=1).to(device)
            data_val_tensor = torch.cat((data_val_tensor,temp_data_val_tensor[:,:n_par]),dim=0)
            val_cond = torch.cat((val_cond,temp_val_cond),dim=0)
            #start_idx += int(sub_batch[k])

            flow[kk].eval()
            with torch.no_grad():
                _loss -= flow[kk].log_prob(data_val_tensor, conditional=val_cond).mean()*(sub_batch[k]/float(batch_size))  # .item()
        
        val_loss = _loss.item()
        loss["val"].append(val_loss)
        val_loss_smooth.append(val_loss)
        if np.isnan(loss["val"][-1]):
            print('validation loss is nan: i={}'.format(i))
            print('sub_batch is {}'.format(sub_batch))
            #print('last_idx is {}'.format(last_idx))
            print('cond_val any nans = {}'.format(torch.isnan(val_cond).any()))
            print('y_val any nans = {}'.format(torch.isnan(orig_data_val_tensor).any()))
            #print('x_val any nans = {}'.format(torch.isnan(x_val).any()))
            print('x_comp_val any nans = {}'.format(torch.isnan(dist_val_tensor).any()))
            print('y_prior_val any nans = {}'.format(torch.isnan(temp_priors_val_tensor).any()))
            print('compressed_prior any nans = {}'.format(torch.isnan(compressed_prior).any()))
            print('cond_val any nans = {}'.format(torch.isnan(val_cond).any()))

    if not i % plot_step:
        my_lr = scheduler.get_last_lr()[0]
        print(f"Epoch {i} - train: {loss['train'][-1]:.3f}, val: {loss['val'][-1]:.3f}, lr: {my_lr:.3e}, n_max: {current_n_max}, sub_batch: {sub_batch}")
        for a in range(n_par):
            for b in range(a+1,n_par):
                fig1, ax1 = plt.subplots(n_test,n_max+1, figsize=(4*(n_max+1),4*n_test), dpi=100)
                #fig2, bx2 = plt.subplots(n_test,n_max+1, figsize=(4*(n_max+1),4*n_test), dpi=100)
                loss_fig, loss_ax = plt.subplots(1, 1, figsize=(8, 8), dpi=100)
                j = 0
                for x,d,nd in zip(data_test_tensor,d_test_tensor,test_n_sig.cpu().numpy().astype(int)):
                    _, _, _, _, samples, _ = make_data(1,n_prior=n_prior,n_max=nd,n_post=n_post,flow=flow,cc_prior_model=cc_prior_model,cc_meas_model=cc_meas_model,meas=d.reshape(1,n_meas,n_max),valtest=True)

                    # reorder observations
                    #reord_old_samples = torch.empty(1,0,n_par,n_max).to(device)
                    reord_samples = torch.empty(1,0,n_par,n_max).to(device) 
                    for _ in range(Nreorder):
                        idx = torch.randperm(n_max)
                        _, _, _, _, temp_samples, _ = make_data(1,n_prior=n_prior,n_max=nd,n_post=int(n_post/float(Nreorder)),flow=flow,cc_prior_model=cc_prior_model,cc_meas_model=cc_meas_model,meas=d.reshape(1,n_meas,n_max)[:,:,idx],valtest=True)
                        #reord_old_samples = torch.cat((reord_old_samples,temp_old_samples),dim=1)                
                        reord_samples = torch.cat((reord_samples,temp_samples),dim=1)

                    # reshape and plot
                    signal_model.scatter_plot(ax1[j],samples,ns_samples[j],reord_samples,x,nd,n_prior,n_post,n_par)
                    #signal_model.scatter_plot(bx2[j],samples,ns_samples[j],reord_samples,x,nd,n_prior,n_post,n_par,change_vars=True)
                    j += 1

                fig1.savefig('{}/training_{}_{}{}.png'.format(plot_path,i,a,b))
                #fig2.savefig('{}/training_cv_{}_{}{}.png'.format(plot_path,i,a,b))

        loss_ax.semilogx(train_loss_smooth, alpha=0.5, label="Train")
        loss_ax.semilogx(np.arange(len(val_loss_smooth))*100,val_loss_smooth, alpha=0.5, label="Val.")
        #loss_ax.semilogx(val_loss_smooth, alpha=0.5, label="Val.")
        loss_ax.set_ylim(np.min(train_loss_smooth)-0.1, np.percentile(np.array(train_loss_smooth),90))
        loss_ax.set_xlim([1000,iterations])
        loss_ax.set_xlabel("Epoch")
        loss_ax.set_ylabel("Loss")
        loss_ax.legend()
        loss_ax.grid('on')
        loss_fig.savefig('{}/loss.png'.format(plot_path))
        plt.close('all')

    frac = np.zeros(n_max+1)
    sub_idx = (i+1)*(4*n_max)/float(iterations)
    for q in range(n_max+1):
        frac[q] = int(batch_size*max(((sub_idx-q)/sub_idx),0))
    sub_batch = -(np.diff(frac)) 
    sub_batch = np.append(sub_batch,0)
    old_n_max = current_n_max
    current_n_max = int(np.argwhere(sub_batch==0)[0])
    if old_n_max != current_n_max:
        print('updated current nmax to {} and sub batch size to {}'.format(current_n_max,sub_batch))
    if i > int(iterations/4):
        sub_batch = (batch_size/n_max)*np.ones(n_max)

    # if starting training another flow then copy the weights across from the previous one
    if current_n_max > old_n_max and multiflow:
        print('updated current_n_max from {} -> {}'.format(old_n_max,current_n_max))
        start = old_n_max - 1
        end = current_n_max - 1
        flow[end].load_state_dict(flow[start].state_dict())
        cc_prior_model[end].load_state_dict(cc_prior_model[start].state_dict())
        print('copied model {} to model {}'.format(start,end))
        old_n_max = current_n_max

    #if i>0 and not i % 50000 and current_n_max<n_max:
    #     current_n_max += 1
    #     sub_batch = np.zeros(n_max)
    #     sub_batch[:current_n_max] = np.ones(current_n_max)*(batch_size // current_n_max)
    #     print('updated current nmax to {} and sub batch size to {}'.format(current_n_max,sub_batch))

flow.eval()
cc_prior_model.eval()
cc_meas_model.eval()
print("Finished training")