Topic/ca timing

.gitignore

@@ -9,3 +9,9 @@ pip-selfcheck.json
 *.venv/*
 *.ipynb_checkpoints
 .idea/
+*.svg
+*.stat
+*.dot
+*.so
+
+

.travis.yml

@@ -1,12 +1,14 @@
 language: python
 
 python:
-- '3.4'
 - '3.5'
+- '3.6'
 
 install:
 - pip install --upgrade pip
+- pip install numpy
 - pip install .[test]
+- python setup.py build_ext --inplace
 
 script: pytest
 
@@ -20,7 +22,7 @@ deploy:
   user: klsmith-usgs
   password:
     secure: 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
+  distributions: sdist bdist_wheel
   on:
-    distributions: sdist bdist_wheel
     repo: USGS-EROS/lcmap-pyccd
     branch: master

MANIFEST.in

@@ -0,0 +1,4 @@
+include ccd/models/lasso.pxd
+include ccd/models/robust_fit.pxd
+include ccd/models/tmask.pxd
+include ccd/procedures.pxd

README.md

@@ -84,6 +84,9 @@ System requirements (Centos)
 * graphviz
 * python-virtualenv
 
+Python requirements
+* numpy (for install of C compiled extensions)
+
 It's highly recommended to do all your development & testing in a virtual environment.
 ```bash
 user@dev:/home/user/$ mkdir pyccd

ccd/__init__.py

@@ -168,7 +168,7 @@ def detect(dates, blues, greens, reds, nirs,
     # Determine which procedure to use for the detection
     procedure = __determine_fit_procedure(quality, proc_params)
 
-    results = procedure(dates, spectra, fitter_fn, quality, proc_params)
+    results = procedure(dates, spectra, fitter_fn, quality, dict(proc_params))
     log.debug('Total time for algorithm: %s', time.time() - t1)
 
     # call detect and return results as the detections namedtuple

ccd/change.py

@@ -14,37 +14,48 @@
 log = logging.getLogger(__name__)
 
 
-def stable(models, dates, variogram, t_cg, detection_bands):
-    """Determine if we have a stable model to start building with
+def stable(models, dates, variogram, t_cg):
+    """
+    Determine if we have a stable model to start building a time segment.
+
+    The basis of this is to ensure that the delta between the start and end of
+    the model are not too offset from each other.
+
+    Models and variogram must of be of the same shape and the representative
+    bands must be in the same order.
 
     Args:
         models: list of current representative/fitted models
         variogram: 1-d array of variogram values to compare against for the
             normalization factor
         dates: array of ordinal date values
         t_cg: change threshold
-        detection_bands: index locations of the spectral bands that are used
-            to determine stability
 
     Returns:
         Boolean on whether stable or not
     """
     # This could be written differently, or more performant using numpy in the
-    # future
+    # future.
     check_vals = []
-    for idx in detection_bands:
-        rmse_norm = max(variogram[idx], models[idx].rmse)
-        slope = models[idx].fitted_model.coef_[0] * (dates[-1] - dates[0])
+    for model, vario in zip(models, variogram):
+        # Our normalization factor.
+        rmse_norm = max(vario, model.rmse)
+
+        # Look at the difference between the predicted values for the beginning
+        # and end.
+        slope_diff = model.fitted_model.coef_[0] * (dates[-1] - dates[0])
 
-        check_val = (abs(slope) + abs(models[idx].residual[0]) +
-                     abs(models[idx].residual[-1])) / rmse_norm
+        # Look at the start and end residuals, while also looking at the
+        # predicted delta.
+        check_val = (abs(slope_diff) + abs(model.residual[0]) +
+                     abs(model.residual[-1])) / rmse_norm
 
         check_vals.append(check_val)
 
-    euc_norm = sum_of_squares(np.array(check_vals))
-    log.debug('Stability norm: %s, Check against: %s', euc_norm, t_cg)
+    check = sum_of_squares(np.array(check_vals))
+    log.debug('Stability check: %s against: %s', check, t_cg)
 
-    return euc_norm < t_cg
+    return check < t_cg
 
 
 def change_magnitude(residuals, variogram, comparison_rmse):