Merge pull request astheeggeggs#143 from szhan/update_demo_haploid

Update notebook to reflect API changes

notebooks/demo_haploid.ipynb

@@ -24,7 +24,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# First, let's simulate some haplotypes to use as a reference panel.\n",
+    "# Simulate some haplotypes to use as a reference panel.\n",
     "seed = 1208\n",
     "ts = msprime.sim_mutations(\n",
     "    msprime.sim_ancestry(\n",
@@ -52,10 +52,7 @@
     "\n",
     "# A query must be of size (1, number of sites).\n",
     "# It can contain MISSING values, but not NONCOPY values.\n",
-    "query = ref_panel[:, 0].reshape(1, ts.num_sites)\n",
-    "\n",
-    "# The number of distinct alleles per site is needed to get per-site mutation rates.\n",
-    "num_alleles = lshmm.core.get_num_alleles(ref_panel, query)\n"
+    "query = ref_panel[:, 0].reshape(1, ts.num_sites)\n"
    ]
   },
   {
@@ -64,10 +61,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Per-site recombination and mutation probabilities are needed\n",
-    "# to parametrise the LS HMM.\n",
+    "# Set per-site recombination and mutation probabilities.\n",
     "rho = np.zeros(ts.num_sites, dtype=np.float64) + 1e-4\n",
     "rho[0] = 0\n",
+    "\n",
     "mu = np.zeros(ts.num_sites, dtype=np.float64) + 1e-4\n"
    ]
   },
@@ -84,11 +81,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Let's determine the best copying path and its log-likelihood using the Viterbi algorithm.\n",
+    "# Infer the best copying path using the Viterbi algorithm.\n",
     "path, log_lik = lshmm.api.viterbi(\n",
     "    ref_panel,\n",
     "    query,\n",
-    "    num_alleles,\n",
+    "    ploidy=1,\n",
     "    prob_recombination=rho,\n",
     "    prob_mutation=mu,\n",
     "    scale_mutation_rate=True,\n",
@@ -103,14 +100,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# There is a function to evaluate the log-likelihood of a copying path.\n",
-    "# Let's check that we get the same log-likelihood of the same copying path \n",
-    "# under the same parameters using the function.\n",
+    "# Evaluate the log-likelihood of the best copying path.\n",
+    "# We should get the same log-likelihood.\n",
     "log_lik_2 = lshmm.api.path_loglik(\n",
     "    ref_panel,\n",
     "    query,\n",
-    "    num_alleles,\n",
-    "    path,\n",
+    "    ploidy=1,\n",
+    "    path=path,\n",
     "    prob_recombination=rho,\n",
     "    prob_mutation=mu,\n",
     "    scale_mutation_rate=True,\n",
@@ -124,13 +120,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# We should get a different log-likelihood value if the parameters are not the same.\n",
-    "# Let's check the log-likelihood of the same copying path under different parameters.\n",
+    "# We should get a different log-likelihood value if the parameters are different.\n",
+    "# Check the log-likelihood of the same copying path under different parameters.\n",
     "log_lik_3 = lshmm.api.path_loglik(\n",
     "    ref_panel,\n",
     "    query,\n",
-    "    num_alleles,\n",
-    "    path,\n",
+    "    ploidy=1,\n",
+    "    path=path,\n",
     "    prob_recombination=rho,\n",
     "    prob_mutation=mu + 1e-5,    # Increase\n",
     "    scale_mutation_rate=True,\n",
@@ -151,13 +147,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# To calculate the forward probability matrix of a query,\n",
-    "# let's use the forward algorithm.\n",
+    "# Calculate the forward probability matrix of a query,\n",
     "np.set_printoptions(linewidth=100, precision=4)\n",
     "fwd_mat, norm_factor, log_lik = lshmm.api.forwards(\n",
     "    ref_panel,\n",
     "    query,\n",
-    "    num_alleles=num_alleles,\n",
+    "    ploidy=1,\n",
     "    prob_recombination=rho,\n",
     "    prob_mutation=mu,\n",
     "    scale_mutation_rate=True,\n",
@@ -172,13 +167,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Now, to calculate the backward probability matrix of the same query,\n",
-    "# let's use the backward algorithm.\n",
+    "# Calculate the backward probability matrix of the same query.\n",
     "np.set_printoptions(linewidth=100, precision=4)\n",
     "bwd_mat = lshmm.api.backwards(\n",
     "    ref_panel,\n",
     "    query,\n",
-    "    num_alleles,\n",
+    "    ploidy=1,\n",
     "    normalisation_factor_from_forward=norm_factor,\n",
     "    prob_recombination=rho,\n",
     "    prob_mutation=mu,\n",