v1.8.0

Author: seanlaw

CHANGELOG.md

@@ -1,3 +1,24 @@
+# 2021-02-04    [ 1.8.0 ]:
+--------------------------
+* bugfixes
+  - Fixed chunk size for `scrump` and `scraamp` when time series are short
+* features
+  - Added `maamp` and `maamped` functions
+  - Added `scraamp` function
+  - Added a new `core.non_normalized` decorator that re-routes normalized functions to non-normalized functions
+  - All z-normalized functions now accept a `normalize` parameter
+* tasks
+  - Renamed `main` branch
+  - Removed Azure pipelines badge
+  - Refactored `subspace`
+  - Refactored non-normalized functions
+  - Added non-normalized support to `floss`
+* documentation
+  - Updated README with `if __name__ == "__main__"` for Dask and Jupyter notebooks
+  - Removed all `aamp` references as `normalize=False` should be used instead
+  - Fixed function docstrings and typos in API docs
+
+
 # 2021-01-20    [ 1.7.2 ]:
 --------------------------
 * bugfixes
@@ -7,7 +28,7 @@
 * tasks
   - Added CI for minimum version dependencies
 * documentation
-  - Updated README to convery NEP 29
+  - Updated README to conver NEP 29
 
 
 # 2021-01-19    [ 1.7.1 ]:

README.rst

@@ -74,10 +74,11 @@ Typical usage (1-dimensional time series data) with `STUMP <https://stumpy.readt
     import stumpy
     import numpy as np
     
-    your_time_series = np.random.rand(10000)
-    window_size = 50  # Approximately, how many data points might be found in a pattern 
+    if __name__ == "__main__":
+        your_time_series = np.random.rand(10000)
+        window_size = 50  # Approximately, how many data points might be found in a pattern 
     
-    matrix_profile = stumpy.stump(your_time_series, m=window_size)
+        matrix_profile = stumpy.stump(your_time_series, m=window_size)
 
 Distributed usage for 1-dimensional time series data with Dask Distributed via `STUMPED <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.stumped>`__:
 
@@ -117,10 +118,11 @@ Multi-dimensional time series data with `MSTUMP <https://stumpy.readthedocs.io/e
     import stumpy
     import numpy as np
 
-    your_time_series = np.random.rand(3, 1000)  # Each row represents data from a different dimension while each column represents data from the same dimension
-    window_size = 50  # Approximately, how many data points might be found in a pattern
+    if __name__ == "__main__":
+        your_time_series = np.random.rand(3, 1000)  # Each row represents data from a different dimension while each column represents data from the same dimension
+        window_size = 50  # Approximately, how many data points might be found in a pattern
 
-    matrix_profile, matrix_profile_indices = stumpy.mstump(your_time_series, m=window_size)
+        matrix_profile, matrix_profile_indices = stumpy.mstump(your_time_series, m=window_size)
 
 Distributed multi-dimensional time series data analysis with Dask Distributed `MSTUMPED <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.mstumped>`__:
 
@@ -145,18 +147,19 @@ Time Series Chains with `Anchored Time Series Chains (ATSC) <https://stumpy.read
     import stumpy
     import numpy as np
     
-    your_time_series = np.random.rand(10000)
-    window_size = 50  # Approximately, how many data points might be found in a pattern 
-    
-    matrix_profile = stumpy.stump(your_time_series, m=window_size)
+    if __name__ == "__main__":
+        your_time_series = np.random.rand(10000)
+        window_size = 50  # Approximately, how many data points might be found in a pattern 
+        
+        matrix_profile = stumpy.stump(your_time_series, m=window_size)
 
-    left_matrix_profile_index = matrix_profile[:, 2]
-    right_matrix_profile_index = matrix_profile[:, 3]
-    idx = 10  # Subsequence index for which to retrieve the anchored time series chain for
+        left_matrix_profile_index = matrix_profile[:, 2]
+        right_matrix_profile_index = matrix_profile[:, 3]
+        idx = 10  # Subsequence index for which to retrieve the anchored time series chain for
 
-    anchored_chain = stumpy.atsc(left_matrix_profile_index, right_matrix_profile_index, idx)
+        anchored_chain = stumpy.atsc(left_matrix_profile_index, right_matrix_profile_index, idx)
 
-    all_chain_set, longest_unanchored_chain = stumpy.allc(left_matrix_profile_index, right_matrix_profile_index)
+        all_chain_set, longest_unanchored_chain = stumpy.allc(left_matrix_profile_index, right_matrix_profile_index)
 
 Semantic Segmentation with `Fast Low-cost Unipotent Semantic Segmentation (FLUSS) <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.fluss>`__:
 
@@ -165,17 +168,18 @@ Semantic Segmentation with `Fast Low-cost Unipotent Semantic Segmentation (FLUSS
     import stumpy
     import numpy as np
 
-    your_time_series = np.random.rand(10000)
-    window_size = 50  # Approximately, how many data points might be found in a pattern
+    if __name__ == "__main__":
+        your_time_series = np.random.rand(10000)
+        window_size = 50  # Approximately, how many data points might be found in a pattern
 
-    matrix_profile = stumpy.stump(your_time_series, m=window_size)
+        matrix_profile = stumpy.stump(your_time_series, m=window_size)
 
-    subseq_len = 50
-    correct_arc_curve, regime_locations = stumpy.fluss(matrix_profile[:, 1], 
-                                                       L=subseq_len, 
-                                                       n_regimes=2, 
-                                                       excl_factor=1
-                                                      )
+        subseq_len = 50
+        correct_arc_curve, regime_locations = stumpy.fluss(matrix_profile[:, 1], 
+                                                        L=subseq_len, 
+                                                        n_regimes=2, 
+                                                        excl_factor=1
+                                                        )
 
 ------------
 Dependencies

pypi.sh

@@ -7,7 +7,7 @@
 #    a) Find the minimum Python and NumPy version you want to support: https://numpy.org/neps/nep-0029-deprecation_policy.html#support-table
 #    b) Then find the SciPy version that has a "Python" version and "Minimum NumPy version" that is supported: https://docs.scipy.org/doc/scipy/reference/toolchain.html#numpy
 #    c) Check Numba release notes for mimumum Python and NumPy versions supported https://numba.pydata.org/numba-doc/dev/release-notes.html
-# 5. Bumpy minimum dependencies
+# 5. Bump minimum dependencies
 #    a) setup.py
 #    b) requirements.txt
 #    d) environment.yml

setup.py

@@ -28,7 +28,7 @@ def get_extras_require():
 
 configuration = {
     "name": "stumpy",
-    "version": "1.7.2",
+    "version": "1.8.0",
     "python_requires=": ">=3.6",
     "author": "Sean M. Law",
     "author_email": "seanmylaw@gmail.com",