Merge pull request #9 from adjtomo/devel

General improvements for better compatibilities w/ external packages

.github/pull_request_template.md

@@ -0,0 +1,19 @@
+<!--
+Thank your for contributing to Pyatoa.
+Please fill out the following before submitting your PR.
+-->
+
+### What does this PR do?
+
+### Why was it initiated?  Any relevant Issues?
+
+### PR Checklist
+- [ ] `develop` base branch selected?
+- [ ] This PR is not directly related to an existing issue (which has no PR yet).
+- [ ] All tests still pass.
+- [ ] Any new features or fixed regressions covered by new tests.
+- [ ] Any new or changed features have been fully documented.
+- [ ] Significant changes have been added to `CHANGELOG.md`.
+- [ ] First time contributors have added your name to CONTRIBUTORS.txt .
+
+

CHANGELOG.md

@@ -1 +1,6 @@
-## Version 0.0.1
+# Current (moving to v0.2.0)
+
+- Replaced Basemap mapping dependency with Cartopy due to Basemap EOL
+- Refactored Gatherer class to simplify code structure. API remains the same.
+- New Executive class takes care of standalone parallel processing 
+- Pyaflowa per-station parallel processing using concurrent.futures

CONTRIBUTORS.txt

@@ -0,0 +1 @@
+Chow, Bryant

pyatoa/__init__.py

@@ -11,21 +11,12 @@
 ch.setFormatter(formatter)
 logger.addHandler(ch)
 
-# Actually this is annoying, don't do that
-# Redirect warning messages into the logger
-# logging.captureWarnings(True)
-# warnings_logger = logging.getLogger("py.warnings")
-# warnings_logger.addHandler(logger)
-
 from pyatoa.core.config import Config # NOQA
 from pyatoa.core.manager import Manager, ManagerError # NOQA
-from pyatoa.core.gatherer import (Gatherer, append_focal_mechanism,
-                                  get_gcmt_moment_tensor)  # NOQA
+from pyatoa.core.executive import Executive # NOQA
+from pyatoa.core.gatherer import Gatherer  # NOQA
 from pyatoa.core.inspector import Inspector  # NOQA
 from pyatoa.core.pyaflowa import Pyaflowa  # NOQA
 from pyatoa.utils.read import read_sem, read_stations # NOQA
 from pyatoa.utils.write import write_sem, write_stations # NOQA
 
-# Dont include this so that Mayavi is not a default requirement
-#from pyatoa.visuals.vtk_modeler import VTKModeler  # NOQA
-

pyatoa/core/config.py

@@ -7,6 +7,8 @@
     currently it's obscured behind some private functions
 """
 import yaml
+import numpy as np
+from copy import deepcopy
 from pyatoa import logger
 from pyatoa.utils.form import format_iter, format_step
 from pyatoa.plugins.pyflex_presets import pyflex_presets
@@ -260,8 +262,9 @@ def _check(self):
             assert(self.step_count >= 0), "Step count must start from 0"
 
         # Check period range is acceptable
-        assert(self.min_period < self.max_period), \
-            "min_period must be less than max_period"
+        if self.min_period and self.max_period:
+            assert(self.min_period < self.max_period), \
+                "min_period must be less than max_period"
 
         # Check if unit output properly set, dictated by ObsPy units
         acceptable_units = ['DISP', 'VEL', 'ACC']
@@ -389,6 +392,12 @@ def _check_io_format(fid, fmt=None):
         else:
             return fmt
 
+    def copy(self):
+        """
+        Simply convenience function to return a deep copy of the Config
+        """
+        return deepcopy(self)
+
     def write(self, write_to, fmt=None):
         """
         Wrapper for underlying low-level write functions
@@ -465,10 +474,6 @@ def _write_asdf(self, ds):
         :type ds: pyasdf.asdf_data_set.ASDFDataSet
         :param ds: dataset to save the config file to
         """
-        # Lazy imports because this function isn't always called
-        from numpy import array
-        from copy import deepcopy
-
         # Deep copy to ensure that we aren't editing the Config parameters
         attrs = vars(deepcopy(self))
 
@@ -497,7 +502,7 @@ def _write_asdf(self, ds):
             attrs.pop(key)
         attrs.update(add_attrs)
 
-        ds.add_auxiliary_data(data_type="Configs", data=array([True]),
+        ds.add_auxiliary_data(data_type="Configs", data=np.array([True]),
                               path=self.aux_path, parameters=attrs
                               )