diff --git a/CMB.py b/CMB.py
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+# | # Install instructions
+# |
+# |```bash
+# |python -m venv venv
+# |source venv/bin/activate
+# |pip install tqdm numpy jax anesthetic cosmopower_jax
+# |pip install git+https://github.com/handley-lab/blackjax@nested_sampling
+# |python blackjaxCMB.py
+# |```
+#
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import tqdm
+import blackjax
+import blackjax.ns.adaptive
+
+#| You may need to set jax to double, due to the flatness of the likelihood function near the peak
+jax.config.update('jax_enable_x64', True) 
+
+paramnames = [r'$\Omega_b h^2$',r'$\Omega_c h^2$',r'$h$',r'$\tau$',r'$n_s$',r'$\ln(10^{10}A_s)$']
+params = ['Ωbh2', 'Ωch2', 'h', 'τ', 'ns', 'lnA']
+#| Seed set for reproducibility
+rng_key = jax.random.PRNGKey(0)
+
+#| Define the loglikelihood and logprior functions
+d = 6 # Dimension of the problem
+#| loglikelihood function
+class CMB(object):
+    def __init__(self, Cl):
+        self.Cl = Cl
+        self.l = jnp.arange(2, 2509)
+
+    def rvs(self, shape=()):
+        shape = tuple(jnp.atleast_1d(shape))
+        return jax.random.chisquare(jax.random.PRNGKey(0), 2*self.l+1, shape + self.Cl.shape) * self.Cl / (2*self.l+1)
+    def logpdf(self, x):
+        return (jax.scipy.stats.chi2.logpdf((2*self.l+1)*x/self.Cl, 2*self.l+1) + jnp.log(2*self.l+1) - jnp.log(self.Cl)).sum(axis=-1)
+
+from cosmopower_jax.cosmopower_jax import CosmoPowerJAX 
+emulator = CosmoPowerJAX(probe='cmb_tt')
+planckparams = jnp.array([0.02225, 0.1198, 0.693, 0.097, 0.965, 3.05])
+d_obs = CMB(emulator.predict(planckparams)).rvs()
+
+## save d_obs to file for comparison with other analyses
+np.save('d_obs.npy', d_obs)
+
+def loglikelihood_fn(x):
+    return CMB(jnp.array(emulator.predict(x))).logpdf(d_obs)
+
+parammin, parammax = jnp.array([[0.01865, 0.02625], [0.05, 0.255], [0.64, 0.82], [0.04, 0.12], [0.84, 1.1], [1.61, 3.91]]).T ## Prior set by the emulator's training range (cosmo_power_jax) 
+
+def logprior_fn(x): ## 6D Uniform prior
+    return jax.scipy.stats.uniform.logpdf(x, parammin, parammax).sum()
+
+#| Define the Nested Sampling algorithm
+n_live = 500
+n_delete = 20
+num_mcmc_steps = d * 5
+ndead_max = 1000
+
+#| Initialize the Nested Sampling algorithm
+algo = blackjax.ns.adaptive.nss(
+    logprior_fn=logprior_fn,
+    loglikelihood_fn=loglikelihood_fn,
+    n_delete=n_delete,
+    num_mcmc_steps=num_mcmc_steps,
+)
+
+@jax.jit
+def one_step(carry, xs):
+    state, k = carry
+    k, subk = jax.random.split(k, 2)
+    state, dead_point = algo.step(subk, state)
+    return (state, k), dead_point
+
+#| Sample live points from the prior
+rng_key, init_key = jax.random.split(rng_key, 2)
+initial_particles= jax.random.uniform(init_key, (n_live, d)) * (parammax - parammin) + parammin
+state = algo.init(initial_particles, loglikelihood_fn)
+
+#| Run the Nested Sampling algorithm
+import tqdm
+dead = []
+n_dead = 0
+pbar = tqdm.tqdm(desc="Dead Points", unit="dead points")  # No total specified
+while not state.sampler_state.logZ_live - state.sampler_state.logZ < -5: # Alternate Termination Criteria are possible
+    (state, rng_key), dead_info = one_step((state, rng_key), None)
+    dead.append(dead_info)
+    pbar.update(n_delete)  # Update progress bar
+pbar.close()
+#| anesthetic post-processing
+from anesthetic import NestedSamples
+import numpy as np
+dead = jax.tree.map(lambda *args: jnp.concatenate(args), *dead)
+live = state.sampler_state
+logL = np.concatenate((dead.logL, live.logL), dtype=float)
+logL_birth = np.concatenate((dead.logL_birth, live.logL_birth), dtype=float)
+data = np.concatenate((dead.particles, live.particles), dtype=float)
+samples = NestedSamples(data, logL=logL, logL_birth=logL_birth, columns=params, labels=paramnames)
+samples.to_csv('jaxLCDM.csv')
+
+#| Basic Plotting Code
+from anesthetic import make_2d_axes
+params = ['Ωbh2', 'Ωch2', 'h', 'τ', 'ns', 'lnA']
+fig, axes = make_2d_axes(params, upper = False)
+
+samples.plot_2d(axes,c = '#CC3311', #Plotting with recommended resolution settings
+                      kind={'lower': 'kde_2d', 'diagonal': 'kde_1d'},
+                      diagonal_kwargs={'nplot_1d': 1000},
+                      lower_kwargs={'nplot_2d': 100**2},
+                      ncompress='entropy',label='JAX samples')
+
+for i in range(6): # add truth lines at the input parameters used to generate the data
+    for j in range(i+1):
+        ax = axes.loc[params[i], params[j]]
+        i!=j and ax.axhline(planckparams[i], color='k', linestyle='--')
+        ax.axvline(planckparams[j], color='k', linestyle='--',label='Ground Truth')
+
+axes.iloc[-1, 0].legend(loc='lower center', bbox_to_anchor=(len(axes)/2, len(axes)), ncol=6)
+fig.savefig('jaxLCDM.pdf', format="pdf", bbox_inches = 'tight') 
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