change np.int/float/bool to int/float/bool

change np.int/float/bool to int/float/bool

examples/Clustering-Comparison.ipynb

@@ -1,13 +1,15 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Clustering comparison"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -97,6 +99,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -123,6 +126,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -148,7 +152,7 @@
     "        t1 = time.time()\n",
     "        \n",
     "        if hasattr(algorithm, 'labels_'):\n",
-    "            y_pred = algorithm.labels_[0].astype(np.int)\n",
+    "            y_pred = algorithm.labels_[0].astype(int)\n",
     "        else:\n",
     "            y_pred = algorithm.transform([X])[0]\n",
     "            \n",
@@ -161,6 +165,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [

msmbuilder/commands/featurizer.py

@@ -153,7 +153,7 @@ def _atom_indices_type(self, fn):
         return np.loadtxt(fn, dtype=int, ndmin=1)
 
     def _sigma_type(self, val):
-        return np.float(val)
+        return float(val)
 
 class SuperposeFeaturizerCommand(FeaturizerCommand):
     klass = SuperposeFeaturizer

msmbuilder/featurizer/multichain.py

@@ -194,7 +194,7 @@ def _get_contact_pairs(self, contacts):
 
         else:
             self.residue_pairs = ensure_type(np.asarray(contacts),
-                                        dtype=np.int, ndim=2, name='contacts',
+                                        dtype=int, ndim=2, name='contacts',
                                         shape=(None, 2), warn_on_cast=False)
             if not np.all((self.residue_pairs >= 0) *
                           (self.residue_pairs < self.reference_frame.n_residues)): 

msmbuilder/msm/_metzner_mcmc_slow.py

@@ -67,7 +67,7 @@ def metzner_mcmc_slow(Z, n_samples, n_thin=1, random_state=None):
         # them like this. This ensures that this function and
         # `metzner_mcmc_fast` give _exactly_ the same sequence of transition
         # matricies, given the same random seed.
-        i, j = (random.rand(2) * n_states).astype(np.int)
+        i, j = (random.rand(2) * n_states).astype(int)
 
         sc = np.sum(K)
         if i == j:

msmbuilder/msm/core.py

@@ -70,7 +70,7 @@ def partial_transform(self, sequence, mode='clip'):
             raise ValueError("Each sequence must be 1D")
 
         f = np.vectorize(lambda k: self.mapping_.get(k, np.nan),
-                         otypes=[np.float])
+                         otypes=[float])
 
         a = f(sequence)
         if mode == 'fill':
@@ -557,9 +557,9 @@ def _transition_counts(sequences, lag_time=1, sliding_window=True):
                            and not contains_none
                            and classes.dtype.kind == 'i'
                            and np.all(classes == np.arange(n_states)))
-    mapping_fn = np.vectorize(mapping.get, otypes=[np.int])
+    mapping_fn = np.vectorize(mapping.get, otypes=[int])
     none_to_nan = np.vectorize(lambda x: np.nan if x is None else x,
-                               otypes=[np.float])
+                               otypes=[float])
 
     counts = np.zeros((n_states, n_states), dtype=float)
     _transitions = []

msmbuilder/msm/msm.py

@@ -272,7 +272,7 @@ def eigtransform(self, sequences, right=True, mode='clip'):
             is_finite = np.isfinite(y)
             if not np.all(is_finite):
                 value = np.empty((y.shape[0], op.shape[1]))
-                value[is_finite, :] = np.take(op, y[is_finite].astype(np.int), axis=0)
+                value[is_finite, :] = np.take(op, y[is_finite].astype(int), axis=0)
                 value[~is_finite, :] = np.nan
             else:
                 value = np.take(op, y, axis=0)

msmbuilder/tests/test_bayes_ratematrix.py

@@ -83,7 +83,7 @@ def test_4():
     except ImportError:
         n_params = scipy.misc.comb(n + 1, 2, exact=True)
     alpha = np.ones(n)
-    beta = (np.arange(n_params - n) + 1).astype(np.float)
+    beta = (np.arange(n_params - n) + 1).astype(float)
     counts = np.array([
         [10, 2, 0, 0],
         [1, 8, 2, 0],

msmbuilder/tests/test_msm.py

@@ -161,26 +161,26 @@ def test_transform():
     v = model.transform([['a', 'b', 'c']])
     assert isinstance(v, list)
     assert len(v) == 1
-    assert v[0].dtype == np.int
+    assert v[0].dtype == int
     np.testing.assert_array_equal(v[0], [0, 1, 2])
 
     v = model.transform([['a', 'b', 'c', 'd']], 'clip')
     assert isinstance(v, list)
     assert len(v) == 1
-    assert v[0].dtype == np.int
+    assert v[0].dtype == int
     np.testing.assert_array_equal(v[0], [0, 1, 2])
 
     v = model.transform([['a', 'b', 'c', 'd']], 'fill')
     assert isinstance(v, list)
     assert len(v) == 1
-    assert v[0].dtype == np.float
+    assert v[0].dtype == float
     np.testing.assert_array_equal(v[0], [0, 1, 2, np.nan])
 
     v = model.transform([['a', 'a', 'SPLIT', 'b', 'b', 'b']], 'clip')
     assert isinstance(v, list)
     assert len(v) == 2
-    assert v[0].dtype == np.int
-    assert v[1].dtype == np.int
+    assert v[0].dtype == int
+    assert v[1].dtype == int
     np.testing.assert_array_equal(v[0], [0, 0])
     np.testing.assert_array_equal(v[1], [1, 1, 1])
 
@@ -193,26 +193,26 @@ def test_partial_transform():
     v = model.partial_transform(['a', 'b', 'c'])
     assert isinstance(v, list)
     assert len(v) == 1
-    assert v[0].dtype == np.int
+    assert v[0].dtype == int
     np.testing.assert_array_equal(v[0], [0, 1, 2])
 
     v = model.partial_transform(['a', 'b', 'c', 'd'], 'clip')
     assert isinstance(v, list)
     assert len(v) == 1
-    assert v[0].dtype == np.int
+    assert v[0].dtype == int
     np.testing.assert_array_equal(v[0], [0, 1, 2])
 
     v = model.partial_transform(['a', 'b', 'c', 'd'], 'fill')
     assert isinstance(v, np.ndarray)
     assert len(v) == 4
-    assert v.dtype == np.float
+    assert v.dtype == float
     np.testing.assert_array_equal(v, [0, 1, 2, np.nan])
 
     v = model.partial_transform(['a', 'a', 'SPLIT', 'b', 'b', 'b'], 'clip')
     assert isinstance(v, list)
     assert len(v) == 2
-    assert v[0].dtype == np.int
-    assert v[1].dtype == np.int
+    assert v[0].dtype == int
+    assert v[1].dtype == int
     np.testing.assert_array_equal(v[0], [0, 0])
     np.testing.assert_array_equal(v[1], [1, 1, 1])
 

msmbuilder/tpt/hub.py

@@ -186,7 +186,7 @@ def _fraction_visited(source, sink, waypoint, tprob, for_committors,
     .. [1] Dickson & Brooks (2012), J. Chem. Theory Comput., 8, 3044-3052.
     """
 
-    fraction_visited = (np.float(tprob[source, :].dot(cond_committors)) /
-                        np.float(tprob[source, :].dot(for_committors)))
+    fraction_visited = (float(tprob[source, :].dot(cond_committors)) /
+                        float(tprob[source, :].dot(for_committors)))
 
     return fraction_visited

msmbuilder/tpt/path.py

@@ -86,19 +86,19 @@ def top_path(sources, sinks, net_flux):
            pathways from short off-equilibrium simulations." PNAS 106.45 (2009):
            19011-19016.
     """
-    sources = np.array(sources, dtype=np.int).reshape((-1,))
-    sinks = np.array(sinks, dtype=np.int).reshape((-1,))
+    sources = np.array(sources, dtype=int).reshape((-1,))
+    sinks = np.array(sinks, dtype=int).reshape((-1,))
 
     n_states = net_flux.shape[0]
 
     queue = list(sources)
     # nodes to check (the "queue")
     # going to use list.pop method so I can't keep it as an array
 
-    visited = np.zeros(n_states).astype(np.bool)
+    visited = np.zeros(n_states).astype(bool)
     # have we already checked this node?
 
-    previous_node = np.ones(n_states).astype(np.int) * -1
+    previous_node = np.ones(n_states).astype(int) * -1
     # what node was found before finding this one
 
     min_fluxes = np.ones(n_states) * -1 * np.inf