Merge 8ec3620 into 098c236

.travis.yml

@@ -9,8 +9,10 @@ env:
     - USE_NUMBA=true
     - STATSMODELS_MASTER=false
     - DOCBUILD=false
+    - PYTEST_PATTERN="(not slow)"
     - secure: "dCvSzHmiqumrvQDQJXaPukd2TEituoUJTRJeBrLfEg0f1ZtAkoEcCudXuissODo1s/e+zClEhn3GRMGZc9QxKI0w8VHWeyMDHjFhyl4wubi35biUpXijYgs6IMqqdIo5oKcnMR6jG8uzuzPGDCpZh49+DgGU5E7rGHBgJ5Tdo8s="
 
+
 matrix:
   fast_finish: true
   include:
@@ -51,11 +53,12 @@ matrix:
       - PYTHON=3.6
       - DOCBUILD=true
       - MATPLOTLIB=3
+      - PYTEST_PATTERN="(slow or not slow)"
 
 before_install:
   - git fetch --tags
   - sudo apt-get -y install pandoc
-  - wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda3.sh
+  - wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda3.sh -nv
   - chmod +x miniconda3.sh
   - ./miniconda3.sh -b
   - export PATH=/home/travis/miniconda3/bin:$PATH
@@ -73,13 +76,13 @@ before_install:
   - PKGS="${PKGS} patsy"; if [[ -n ${PATSY} ]]; then PKGS="${PKGS}=${PATSY}"; fi;
   - PKGS="${PKGS} pandas"; if [[ -n ${PANDAS} ]]; then PKGS="${PKGS}=${PANDAS}"; fi;
   - PKGS="${PKGS} Cython"; if [[ -n ${CYTHON} ]]; then PKGS="${PKGS}=${CYTHON}"; fi;
-  - if [[  -n ${MATPLOTLIB} ]]; then PKGS="${PKGS} matplotlib=${MATPLOTLIB}"; fi;
+  - if [[  -n ${MATPLOTLIB} ]]; then PKGS="${PKGS} matplotlib=${MATPLOTLIB} seaborn"; fi;
   - PKGS="${PKGS} statsmodels"; if [[ -n ${STATSMODELS} ]]; then PKGS="${PKGS}=${STATSMODELS}"; fi;
   - if [[ ${USE_NUMBA} = true ]]; then PKGS="${PKGS} numba"; if [[ -n ${NUMBA} ]]; then PKGS="${PKGS}=${NUMBA}";  fi; fi;
   - echo conda create --yes --quiet -n arch-test ${PKGS}
   - conda create --yes --quiet -n arch-test ${PKGS}
   - source activate arch-test
-  - pip install flake8 nbconvert nbformat pytest coverage coveralls pytest-cov codecov pytest-xdist cached_property
+  - pip install cached_property flake8 "pytest<4.1" pytest-xdist pytest-cov coverage coveralls codecov nbformat nbconvert!=5.4 jupyter_client ipython jupyter -q
   - if [[ "$STATSMODELS_MASTER" == true ]]; then sh ./ci/statsmodels-master.sh; fi;
   - |
     if [[ "$DOCBUILD" == true ]]; then
@@ -101,7 +104,8 @@ install:
 script:
   - set -e
   - python ci/performance.py
-  - pytest -r a ${COVERAGE_OPTS} arch --durations=25
+  - echo pytest -r a -m "$PYTEST_PATTERN" ${COVERAGE_OPTS} --durations=25 arch
+  - pytest -r a -m "$PYTEST_PATTERN" ${COVERAGE_OPTS} --durations=25 arch
   - |
     if [[ "$DOCBUILD" = true ]]; then
       python setup.py install

appveyor.yml

@@ -33,4 +33,4 @@ build_script:
   - cmd: python setup.py develop
 
 test_script:
-  - cmd: py.test -n 2 arch --durations=25
+  - cmd: py.test -n 2 arch -m "(not slow)" --durations=25

arch/conftest.py

@@ -0,0 +1,16 @@
+import pytest
+
+
+def pytest_addoption(parser):
+    parser.addoption("--skip-slow", action="store_true",
+                     help="skip slow tests")
+    parser.addoption("--only-slow", action="store_true",
+                     help="run only slow tests")
+
+
+def pytest_runtest_setup(item):
+    if 'slow' in item.keywords and item.config.getoption("--skip-slow"):  # pragma: no cover
+        pytest.skip("skipping due to --skip-slow")  # pragma: no cover
+
+    if 'slow' not in item.keywords and item.config.getoption("--only-slow"):  # pragma: no cover
+        pytest.skip("skipping due to --only-slow")  # pragma: no cover

arch/data/binary/__init__.py

@@ -0,0 +1,13 @@
+from arch.data.utility import load_file
+
+
+def load():
+    """
+    Load the graduate school admissions dataused in the examples
+
+    Returns
+    -------
+    data : DataFrame
+        Dataset containing GRE, GPA and class rank, and admission decision
+    """
+    return load_file(__file__, 'binary.csv.gz')

arch/data/core_cpi/__init__.py

@@ -0,0 +1,17 @@
+from arch.data.utility import load_file
+
+
+def load():
+    """
+    Load the Core CPI data used in the examples
+
+    Returns
+    -------
+    data : DataFrame
+        Data set containing the CPI less Food and Energy
+
+    Notes
+    -----
+    From the FRED database
+    """
+    return load_file(__file__, 'core-cpi.csv.gz')

arch/data/default/__init__.py

@@ -0,0 +1,13 @@
+from arch.data.utility import load_file
+
+
+def load():
+    """
+    Load the AAA and BAA rates used in the examples
+
+    Returns
+    -------
+    data : DataFrame
+        Data set containing the rates on AAA and BAA rated bonds.
+    """
+    return load_file(__file__, 'default.csv.gz')

arch/data/frenchdata/__init__.py

@@ -0,0 +1,19 @@
+from arch.data.utility import load_file
+
+
+def load():
+    """
+    Load the Fama-French factor data used in the examples
+
+    Returns
+    -------
+    data : DataFrame
+        Data set containing excess market, size and value factors and the
+        risk-free rate
+
+    Notes
+    -----
+    Provided by Ken French,
+    http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
+    """
+    return load_file(__file__, 'frenchdata.csv.gz')

arch/data/nasdaq/__init__.py

@@ -0,0 +1,13 @@
+from arch.data.utility import load_file
+
+
+def load():
+    """
+    Load the NASDAQ Composite data used in the examples
+
+    Returns
+    -------
+    data : DataFrame
+        Data set containing OHLC, adjusted close and the trading volume.
+    """
+    return load_file(__file__, 'nasdaq.csv.gz')

arch/data/sp500/__init__.py

@@ -0,0 +1,13 @@
+from arch.data.utility import load_file
+
+
+def load():
+    """
+    Load the S&P 500 data used in the examples
+
+    Returns
+    -------
+    data : DataFrame
+        Data set containing OHLC, adjusted close and the trading volume.
+    """
+    return load_file(__file__, 'sp500.csv.gz')

arch/data/utility.py

@@ -0,0 +1,25 @@
+import os
+
+import pandas as pd
+
+
+def load_file(file_base, filename):
+    """
+    Parameters
+    ----------
+    filename : str
+        Name of csv.gz to load
+
+    Returns
+    -------
+    data : DataFrame
+        Dataframe containing the loaded data
+    """
+    curr_dir = os.path.split(os.path.abspath(file_base))[0]
+    data = pd.read_csv(os.path.join(curr_dir, filename))
+    if 'Date' in data:
+        data.Date = pd.to_datetime(data.Date)
+        data = data.set_index('Date')
+    for col in data:
+        data[col] = pd.to_numeric(data[col], errors='coerce')
+    return data

arch/data/vix/__init__.py

@@ -0,0 +1,13 @@
+from arch.data.utility import load_file
+
+
+def load():
+    """
+    Load the VIX Index data used in the examples
+
+    Returns
+    -------
+    data : DataFrame
+        Data set containing historical VIX
+    """
+    return load_file(__file__, 'vix.csv.gz')

arch/data/wti/__init__.py

@@ -0,0 +1,17 @@
+from arch.data.utility import load_file
+
+
+def load():
+    """
+    Load the West Texas Intermediate crude oil price data used in the examples
+
+    Returns
+    -------
+    data : DataFrame
+        Data set containing the price of WTI
+
+    Notes
+    -----
+    From the FRED database
+    """
+    return load_file(__file__, 'wti.csv.gz')

arch/tests/test_data.py

@@ -0,0 +1,19 @@
+import pytest
+import os
+import glob
+import pandas as pd
+
+CURR_DIR = os.path.split(os.path.abspath(__file__))[0]
+FILES = glob.glob(os.path.join(CURR_DIR, '..', 'data', '*'))
+DATASETS = [os.path.split(f)[-1] for f in FILES if ('.py' not in f and '__' not in f)]
+
+
+@pytest.fixture(params=DATASETS)
+def dataset(request):
+    return request.param
+
+
+def test_dataset(dataset):
+    mod = __import__('arch.data.{0}'.format(dataset), fromlist=[dataset])
+    data = mod.load()
+    assert isinstance(data, pd.DataFrame)

arch/tests/test_examples.py

@@ -0,0 +1,45 @@
+import glob
+import os
+import sys
+
+import pytest
+
+try:
+    import jupyter_client
+    import nbformat
+    from nbconvert.preprocessors import ExecutePreprocessor
+    # matplotlib is required for most notebooks
+    import matplotlib  # noqa: F401
+
+    kernels = jupyter_client.kernelspec.find_kernel_specs()
+except ImportError:  # pragma: no cover
+    pytestmark = pytest.mark.skip(reason='Required packages not available')
+
+SLOW_NOTEBOOKS = ['multiple-comparison_examples.ipynb']
+
+kernel_name = 'python%s' % sys.version_info.major
+
+head, _ = os.path.split(__file__)
+NOTEBOOK_DIR = os.path.abspath(os.path.join(head, '..', '..', 'examples'))
+
+nbs = sorted(glob.glob(os.path.join(NOTEBOOK_DIR, '*.ipynb')))
+ids = list(map(lambda s: os.path.split(s)[-1].split('.')[0], nbs))
+if not nbs:  # pragma: no cover
+    pytest.mark.skip(reason='No notebooks found and so no tests run')
+
+
+@pytest.fixture(params=nbs, ids=ids)
+def notebook(request):
+    return request.param
+
+
+@pytest.mark.slow
+def test_notebook(notebook):
+    nb_name = os.path.split(notebook)[-1]
+    if nb_name in SLOW_NOTEBOOKS:
+        pytest.skip('Notebook is too slow to test')
+    nb = nbformat.read(notebook, as_version=4)
+    ep = ExecutePreprocessor(allow_errors=False,
+                             timeout=240,
+                             kernel_name=kernel_name)
+    ep.preprocess(nb, {'metadata': {'path': NOTEBOOK_DIR}})

arch/univariate/base.py

@@ -1078,12 +1078,20 @@ def plot(self, annualize=None, scale=None):
         >>> fig = res.plot(scale=360)
         """
         from matplotlib.pyplot import figure
+
+        def _set_tight_x(axis, index):
+            try:
+                axis.set_xlim(index[0], index[-1])
+            except ValueError:
+                pass
+
         fig = figure()
 
         ax = fig.add_subplot(2, 1, 1)
         ax.plot(self._index, self.resid / self.conditional_volatility)
         ax.set_title('Standardized Residuals')
         ax.axes.xaxis.set_ticklabels([])
+        _set_tight_x(ax, self._index)
 
         ax = fig.add_subplot(2, 1, 2)
         vol = self.conditional_volatility
@@ -1100,6 +1108,7 @@ def plot(self, annualize=None, scale=None):
             title = 'Conditional Volatility'
 
         ax.plot(self._index, vol)
+        _set_tight_x(ax, self._index)
         ax.set_title(title)
 
         return fig

ci/statsmodels-master.sh

@@ -6,3 +6,4 @@ git clone --branch=master --depth=10 https://github.com/statsmodels/statsmodels.
 cd statsmodels
 python setup.py install
 cd $GITDIR
+pip install seaborn