fixed failing tests

.pylintrc

@@ -0,0 +1,21 @@
+[MESSAGES CONTROL]
+
+disable=
+    bad-continuation,
+    bad-whitespace,
+    invalid-name,
+    missing-module-docstring,
+    missing-class-docstring,
+    missing-function-docstring,
+    redefined-outer-name,
+    c-extension-no-member,
+    too-many-arguments,
+    too-many-instance-attributes,
+    too-many-locals,
+    invalid-unary-operand-type,
+    not-context-manager,
+    redefined-builtin
+
+[format]
+
+max-line-length=88

README.md

@@ -23,6 +23,23 @@ You may see performance benefits from `fastmath` by installing Intel's short vec
 conda install -c numba icc_rt
 ```
 
+## Debugging
+
+Debugging is often simpler without `jit`ting. To disable `numba`,
+
+```bash
+export NUMBA_DISABLE_JIT=1
+```
+
+and re-enable with
+
+```bash
+export NUMBA_DISABLE_JIT=0
+```
+
+Be wary of using `os.environ["NUMBA_DISABLE_JIT"] = "1"` from python code - this must be set above imports.
+
+
 ## Differences compared to Scikit-learn
 
 1. All operations are done using reduced distances. E.g. provided `KDTree` implementations use squared distances rather than actual distances both for inputs and outputs.

example/compare_ifp.py

@@ -1,13 +1,8 @@
-import os
-
 import numpy as np
 
 from numba_neighbors import binary_tree as bt
 from numba_neighbors import kd_tree as kd
 
-os.environ["NUMBA_DISABLE_JIT"] = "1"
-
-
 N = 100
 n = 50
 D = 1
@@ -20,7 +15,7 @@
 
 np.random.seed(124)
 data = np.random.uniform(size=(N, D)).astype(kd.FLOAT_TYPE)
-# data.sort(axis=0)
+data.sort(axis=0)
 print(data)
 
 tree = kd.KDTree(data, leaf_size=leaf_size)

example/ifp_sample.py

@@ -1,15 +1,10 @@
-import os
-
 import matplotlib.pyplot as plt
 import numpy as np
 
 from numba_neighbors import kd_tree as kd
 
-os.environ["NUMBA_DISABLE_JIT"] = "1"
-
-
 N = 1024
-n = 256
+n = 70
 D = 2
 # rejection_r = 0.1
 query_r = 0.1
@@ -22,7 +17,7 @@
 data = np.random.uniform(size=(N, D)).astype(kd.FLOAT_TYPE)
 
 tree = kd.KDTree(data, leaf_size=leaf_size)
-sample_result, query_result = tree.ifp_sample_query(
+sample_result0, query_result0 = tree.ifp_sample_query(
     r2, tree.get_node_indices(), n, max_neighbors
 )
 sample_result, query_result = tree.rejection_ifp_sample_query(
@@ -31,13 +26,7 @@
 
 
 def vis(
-    x0,
-    sample_indices,
-    query_result,
-    small_balls=True,
-    big_balls=False,
-    labels=False,
-    aspect=1,
+    x0, sample_indices, query_result, small_balls=True, big_balls=False, labels=False,
 ):
     x1 = x0[sample_indices]
     xn = x0[query_result.indices[0, : query_result.counts[0]]]
@@ -69,5 +58,5 @@ def vis(
     ax.set_aspect(1)
 
 
-vis(data, sample_result.indices, query_result)
+vis(data, sample_result.indices, query_result, big_balls=False)
 plt.show()

example/index_heap.py

@@ -1,12 +1,7 @@
-import os
-
 import numpy as np
 
 from numba_neighbors.index_heap import padded_index_heap
 
-os.environ["NUMBA_DISABLE_JIT"] = "1"
-
-
 heap = padded_index_heap(np.zeros((10,)), np.arange(10), 20)
 print(heap.pop())
 print(heap.pop())

example/rejection_ifp_sample.py

@@ -1,12 +1,8 @@
-import os
-
 import matplotlib.pyplot as plt
 import numpy as np
 
 from numba_neighbors import kd_tree as kd
 
-os.environ["NUMBA_DISABLE_JIT"] = "1"
-
 N = 1024
 n = 128
 D = 2

example/rejection_sample.py

@@ -1,13 +1,8 @@
-import os
-
 import matplotlib.pyplot as plt
 import numpy as np
 
 from numba_neighbors import kd_tree as kd
 
-os.environ["NUMBA_DISABLE_JIT"] = "1"
-
-
 N = 1024
 n = N
 D = 2