Merge pull request #20 from pysat/develop

v0.1.2 release

.github/pull_request_template.md

@@ -32,7 +32,7 @@ your test configuration
 
 # Checklist:
 
-- [ ] Make sure you are merging into the ``develop`` (not ``master``) branch
+- [ ] Make sure you are merging into the ``develop`` (not ``main``) branch
 - [ ] My code follows the style guidelines of this project
 - [ ] I have performed a self-review of my own code
 - [ ] I have commented my code, particularly in hard-to-understand areas

.travis.yml

@@ -1,9 +1,11 @@
 language: python
 dist: xenial
-matrix:
+jobs:
   include:
-    - python: 2.7
-    - python: 3.5
+    - name: Minimum NEP 029 versions
+      python: 3.6
+      env: NUMPY_VER=1.15
+    # Versions with latest numpy
     - python: 3.6
     - python: 3.7
 
@@ -13,44 +15,56 @@ addons:
     packages:
     - gfortran
 
-before_install:
-  - pip install pytest-cov
-  - pip install coveralls
-  - pip install future
-  # Install version specific packages for 2.7 and 3.5
-  - pip install 'pandas<0.25'
-  - pip install xarray
-  - pip install matplotlib
-  # Install pysatCDF and dump output
+install:
+  - sudo apt-get update
+  # We do this conditionally because it saves us some downloading if the
+  # version is the same.
+  - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
+  - bash miniconda.sh -b -p $HOME/miniconda
+  - source "$HOME/miniconda/etc/profile.d/conda.sh"
+  - hash -r
+  - conda config --set always_yes True --set changeps1 False
+  - conda update -q conda
+  # Useful for debugging any issues with conda
+  - conda info -a
+  # Create conda test environment
+  - conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION numpy scipy requests beautifulsoup4 lxml netCDF4 nose pytest-cov pytest-ordering coveralls future
+  - conda activate test-environment
+  # check for custom version of numpy
+  - if [ -z ${NUMPY_VER} ]; then
+      echo 'Using latest numpy';
+    else
+      conda install numpy==$NUMPY_VER;
+    fi
+  # Enforce version limits due to use of Panel
+  - conda install pandas==0.24.2
+  - conda install 'xarray<0.15'
+  # Dependencies not available through conda, install through pip
+  - pip install PyForecastTools
   - pip install pysatCDF >/dev/null
-  # Prepare modified pysat install
-  - pip install numpy
+  # Get latest coveralls from pip, not conda
+  - pip install coveralls
+  - pip install pytest-flake8
+  # Prepare pysat install from git
   - cd ..
-  - echo 'cloning pysat'
   - git clone https://github.com/pysat/pysat.git >/dev/null
-  - echo 'installing pysat'
   - cd ./pysat
-  - git checkout no_sgp4
   # set up data directory
-  - mkdir /home/travis/build/pysat/pysatData
+  - mkdir /home/travis/build/pysatData
   # install pysat
-  - cd /home/travis/build/pysat/pysat
+  - git checkout develop-3
   - python setup.py install >/dev/null
-  - cd /home/travis/build/pysat/pysatSeasons
-
-install:
+  - export PYTHONPATH=$PYTHONPATH:$(pwd)
+  # install pysatSeasons
+  - cd ../pysatSeasons
   - python setup.py install
 
 before_script:
   # set up display screen
   - export DISPLAY=:99.0
-  - if [[ $TRAVIS_PYTHON_VERSION < "3.0" ]]; then
-      sh -e /etc/init.d/xvfb start;
-      sleep 3;
-    fi
 
 script:
-  - pytest -vs --cov=pysatSeasons/
+  - pytest -vs --cov=pysatSeasons/ --flake8
 
 after_success:
   - coveralls

CHANGELOG.md

@@ -2,6 +2,15 @@
 All notable changes to this project will be documented in this file.
 This project adheres to [Semantic Versioning](http://semver.org/).
 
+## [0.1.2] - 2020-07-29
+- Updates demo codes to import objects from datetime and pandas for pysat 3.0.0 compatibility
+- Fixed a bug where test routines used float where numpy 1.18 expects an int
+- Import objects from datetime and pandas for pysat 3.0.0 compatibility
+- Use conda to manage Travis CI
+- Rename default branch as `main`
+- Update to pysat documentation standards
+- Add flake8 testing for code
+
 ## [0.1.1] - 2019-10-09
 - Add demo code
 - Added DOI badge to documentation page

README.md

@@ -4,8 +4,8 @@
 </div>
 
 # pysatSeasons
-[![Build Status](https://travis-ci.org/pysat/pysatSeasons.svg?branch=master)](https://travis-ci.org/pysat/pysatSeasons)
-[![Coverage Status](https://coveralls.io/repos/github/pysat/pysatSeasons/badge.svg?branch=master)](https://coveralls.io/github/pysat/pysatSeasons?branch=master)
+[![Build Status](https://travis-ci.org/pysat/pysatSeasons.svg?branch=main)](https://travis-ci.org/pysat/pysatSeasons)
+[![Coverage Status](https://coveralls.io/repos/github/pysat/pysatSeasons/badge.svg?branch=main)](https://coveralls.io/github/pysat/pysatSeasons?branch=main)
 [![DOI](https://zenodo.org/badge/209365329.svg)](https://zenodo.org/badge/latestdoi/209365329)
 
 

demo/cosmic_and_ivm_demo.py

@@ -1,14 +1,16 @@
-import pysat
-import pysatSeasons
-import pandas as pds
+import datetime as dt
 import numpy as np
 import numpy.ma as ma
 import matplotlib.pyplot as plt
+import pandas as pds
+
+import pysat
+import pysatSeasons
 
 # dates for demo
 ssnDays = 67
-startDate = pds.datetime(2009, 12, 21) - pds.DateOffset(days=ssnDays)
-stopDate = pds.datetime(2009, 12, 21) + pds.DateOffset(days=ssnDays)
+startDate = dt.datetime(2009, 12, 21) - pds.DateOffset(days=ssnDays)
+stopDate = dt.datetime(2009, 12, 21) + pds.DateOffset(days=ssnDays)
 
 
 # define functions to customize data for application
@@ -65,7 +67,7 @@ def geo2mag(incoord):
     r = 1.0
 
     # convert first to radians
-    lon, lat = [x*pi/180 for x in [lon, lat]]
+    lon, lat = [x * pi / 180 for x in [lon, lat]]
 
     glat = incoord[0] * pi / 180.0
     glon = incoord[1] * pi / 180.0
@@ -96,7 +98,7 @@ def geo2mag(incoord):
     tomaglat[1, 1] = 1.
     out = dot(tomaglat, out)
 
-    mlat = arctan2(out[2], sqrt(out[0]*out[0] + out[1]*out[1]))
+    mlat = arctan2(out[2], sqrt((out[0] * out[0]) + (out[1] * out[1])))
     mlat = mlat * 180 / pi
     mlon = arctan2(out[1], out[0])
     mlon = mlon * 180 / pi
@@ -119,8 +121,8 @@ def restrictMLAT(inst, maxMLAT=None):
 
 def filterMLAT(inst, mlatRange=None):
     if mlatRange is not None:
-        inst.data = inst.data[(np.abs(inst['mlat']) >= mlatRange[0]) &
-                              (np.abs(inst['mlat']) <= mlatRange[1])]
+        inst.data = inst.data[(np.abs(inst['mlat']) >= mlatRange[0])
+                              & (np.abs(inst['mlat']) <= mlatRange[1])]
     return
 
 
@@ -137,9 +139,9 @@ def addTopsideScaleHeight(cosmic):
     output.name = 'thf2'
 
     for i, profile in enumerate(cosmic['profiles']):
-        profile = profile[(profile['ELEC_dens'] >=
-                          (1./np.e) * cosmic['edmax'].iloc[i]) &
-                          (profile.index >= cosmic['edmaxalt'].iloc[i])]
+        profile = profile[(profile['ELEC_dens']
+                          >= (1. / np.e) * cosmic['edmax'].iloc[i])
+                          & (profile.index >= cosmic['edmaxalt'].iloc[i])]
         # want the first altitude where density drops below NmF2/e
         # first, resample such that we know all altitudes in between samples
         # are there
@@ -157,8 +159,8 @@ def addTopsideScaleHeight(cosmic):
 
         if len(profile) > 10:
             # make sure density at highest altitude is near Nm/e
-            if (profile['ELEC_dens'].iloc[-1]/profile['ELEC_dens'].iloc[0] <
-                    0.4):
+            if (profile['ELEC_dens'].iloc[-1] / profile['ELEC_dens'].iloc[0]
+                    < 0.4):
                 altDiff = profile.index.values[-1] - profile.index.values[0]
                 if altDiff >= 500:
                     altDiff = np.nan
@@ -176,7 +178,7 @@ def addTopsideScaleHeight(cosmic):
                        name='ivm', tag='',
                        clean_level='clean')
 # restrict meausurements to those near geomagnetic equator
-ivm.custom.add(restrictMLAT, 'modify', maxMLAT=25.)
+ivm.custom.attach(restrictMLAT, 'modify', maxMLAT=25.)
 # perform seasonal average
 ivm.bounds = (startDate, stopDate)
 ivmResults = pysatSeasons.avg.median2D(ivm, [0, 360, 24], 'alon',
@@ -189,13 +191,13 @@ def addTopsideScaleHeight(cosmic):
                           clean_level='clean',
                           altitude_bin=3)
 # apply custom functions to all data that is loaded through cosmic
-cosmic.custom.add(addApexLong, 'add')
+cosmic.custom.attach(addApexLong, 'add')
 # select locations near the magnetic equator
-cosmic.custom.add(filterMLAT, 'modify', mlatRange=(0., 10.))
+cosmic.custom.attach(filterMLAT, 'modify', mlatRange=(0., 10.))
 # take the log of NmF2 and add to the dataframe
-cosmic.custom.add(addlogNm, 'add')
+cosmic.custom.attach(addlogNm, 'add')
 # calculates the height above hmF2 to reach Ne < NmF2/e
-cosmic.custom.add(addTopsideScaleHeight, 'add')
+cosmic.custom.attach(addTopsideScaleHeight, 'add')
 
 # do an average of multiple COSMIC data products
 # from startDate through stopDate
@@ -294,7 +296,7 @@ def addTopsideScaleHeight(cosmic):
                 temp = pds.DataFrame(ltview['ELEC_dens'])
                 # produce a grid covering plot region
                 # (y values determined by profile)
-                xx, yy = np.meshgrid(np.array([i, i+1]), temp.index.values)
+                xx, yy = np.meshgrid(np.array([i, i + 1]), temp.index.values)
                 filtered = ma.array(np.log10(temp.values),
                                     mask=pds.isnull(temp))
                 graph = axarr[j].pcolormesh(xx, yy, filtered,

demo/ssnl_occurrence_by_orbit.py

@@ -3,11 +3,14 @@
 the occurrent probability of an event occurring.
 """
 
+import datetime as dt
+import numpy as np
 import os
+
+import matplotlib.pyplot as plt
+
 import pysat
 import pysatSeasons
-import matplotlib.pyplot as plt
-import numpy as np
 
 # set the directory to save plots to
 results_dir = ''
@@ -26,10 +29,10 @@ def filter_vefi(inst):
     return
 
 
-vefi.custom.add(filter_vefi, 'modify')
+vefi.custom.attach(filter_vefi, 'modify')
 # set limits on dates analysis will cover, inclusive
-start = pysat.datetime(2010, 5, 9)
-stop = pysat.datetime(2010, 5, 15)
+start = dt.datetime(2010, 5, 9)
+stop = dt.datetime(2010, 5, 15)
 
 # if there is no vefi dc magnetometer data on your system, then run command
 # below where start and stop are pandas datetimes (from above)

pysatSeasons/__init__.py

@@ -15,10 +15,11 @@
 
 import os
 
-from pysatSeasons import occur_prob
-from pysatSeasons import avg
-from pysatSeasons import plot
-from pysatSeasons._core import computational_form
+# Import key modules and skip F401 testing in flake8
+from pysatSeasons import occur_prob  # noqa: F401
+from pysatSeasons import avg  # noqa: F401
+from pysatSeasons import plot  # noqa: F401
+from pysatSeasons._core import computational_form  # noqa: F401
 
 # set version
 here = os.path.abspath(os.path.dirname(__file__))

pysatSeasons/_core.py

@@ -1,3 +1,6 @@
+import pandas as pds
+
+
 def computational_form(data):
     """
     Repackages numbers, Series, or DataFrames
@@ -21,15 +24,14 @@ def computational_form(data):
     -------
     pandas.Series, DataFrame, or Panel
         repacked data, aligned by indices, ready for calculation
-    """
 
-    from pysat import DataFrame, Series, Panel
+    """
 
-    if isinstance(data.iloc[0], DataFrame):
-        dslice = Panel.from_dict(dict([(i, data.iloc[i])
-                                       for i in range(len(data))]))
-    elif isinstance(data.iloc[0], Series):
-        dslice = DataFrame(data.tolist())
+    if isinstance(data.iloc[0], pds.DataFrame):
+        dslice = pds.Panel.from_dict(dict([(i, data.iloc[i])
+                                     for i in range(len(data))]))
+    elif isinstance(data.iloc[0], pds.Series):
+        dslice = pds.DataFrame(data.tolist())
         dslice.index = data.index
     else:
         dslice = data