Merge branch 'dev' into features/wind_farms_and_clusters

Author: SabineHaas

VERSION

@@ -61,7 +61,7 @@
 # General information about the project.
 project = u'windpowerlib'
 copyright = u'2016, oemof developer group'
-author = u'Uwe Krien, oemof developing group'
+author = u'oemof developing group'
 
 import windpowerlib
 
@@ -285,7 +285,7 @@
 
 # Bibliographic Dublin Core info.
 epub_title = u'windpowerlib'
-epub_author = u'Uwe Krien, oemof developing group'
+epub_author = u'oemof developing group'
 epub_publisher = u'oemof developing group'
 epub_copyright = u'2016, oemof developing group'
 

doc/conf.py

@@ -182,7 +182,7 @@
     "    'hub_height': 105,  # in m\n",
     "    'rotor_diameter': 90,  # in m\n",
     "    'power_curve': pd.DataFrame(\n",
-    "            data={'values': [p * 1000 for p in [\n",
+    "            data={'power': [p * 1000 for p in [\n",
     "                      0.0, 26.0, 180.0, 1500.0, 3000.0, 3000.0]],  # in W\n",
     "                  'wind_speed': [0.0, 3.0, 5.0, 10.0, 15.0, 25.0]})  # in m/s\n",
     "    }  \n",
@@ -313,20 +313,20 @@
     "if plt:\n",
     "    if e126.power_coefficient_curve is not None:\n",
     "        e126.power_coefficient_curve.plot(\n",
-    "            x='wind_speed', y='values', style='*',\n",
+    "            x='wind_speed', y='power coefficient', style='*',\n",
     "            title='Enercon E126 power coefficient curve')\n",
     "        plt.show()\n",
     "    if e126.power_curve is not None:\n",
-    "        e126.power_curve.plot(x='wind_speed', y='values', style='*',\n",
+    "        e126.power_curve.plot(x='wind_speed', y='power', style='*',\n",
     "                              title='Enercon E126 power curve')\n",
     "        plt.show()\n",
     "    if my_turbine.power_coefficient_curve is not None:\n",
     "        my_turbine.power_coefficient_curve.plot(\n",
-    "            x='wind_speed', y='values', style='*',\n",
+    "            x='wind_speed', y='power coefficient', style='*',\n",
     "            title='myTurbine power coefficient curve')\n",
     "        plt.show()\n",
     "    if my_turbine.power_curve is not None:\n",
-    "        my_turbine.power_curve.plot(x='wind_speed', y='values', style='*',\n",
+    "        my_turbine.power_curve.plot(x='wind_speed', y='power', style='*',\n",
     "                                    title='myTurbine power curve')\n",
     "        plt.show()"
    ]

example/basic_example.ipynb

@@ -0,0 +1,49 @@
+import os
+import subprocess
+import tempfile
+import nbformat
+
+from example import basic_example as be
+from numpy.testing import assert_allclose
+
+
+class TestExamples:
+
+    def test_basic_example_flh(self):
+        # tests full load hours
+        weather = be.get_weather_data('weather.csv')
+        my_turbine, e126 = be.initialise_wind_turbines()
+        be.calculate_power_output(weather, my_turbine, e126)
+
+        assert_allclose(1766.6870, (e126.power_output.sum() /
+                                    e126.nominal_power), 0.01)
+        assert_allclose(1882.7567, (my_turbine.power_output.sum() /
+                                        my_turbine.nominal_power), 0.01)
+
+    def _notebook_run(self, path):
+        """
+        Execute a notebook via nbconvert and collect output.
+        Returns (parsed nb object, execution errors)
+        """
+        dirname, __ = os.path.split(path)
+        os.chdir(dirname)
+        with tempfile.NamedTemporaryFile(suffix=".ipynb") as fout:
+            args = ["jupyter", "nbconvert", "--to", "notebook", "--execute",
+                    "--ExecutePreprocessor.timeout=60",
+                    "--output", fout.name, path]
+            subprocess.check_call(args)
+
+            fout.seek(0)
+            nb = nbformat.read(fout, nbformat.current_nbformat)
+
+        errors = [output for cell in nb.cells if "outputs" in cell
+                  for output in cell["outputs"]
+                  if output.output_type == "error"]
+
+        return nb, errors
+
+    def test_basic_example_ipynb(self):
+        dir_path = os.path.dirname(os.path.realpath(__file__))
+        nb, errors = self._notebook_run(os.path.join(dir_path,
+                                                     'basic_example.ipynb'))
+        assert errors == []

example/test_basic_example.py

@@ -1,9 +1,8 @@
 import os
 from setuptools import setup
-from windpowerlib import __version__
 
 setup(name='windpowerlib',
-      version=__version__,
+      version='0.0.6',
       description='Creating time series from wind power plants.',
       url='http://github.com/wind-python/windpowerlib',
       author='oemof developing group',

example/test_examples.py

@@ -5,10 +5,14 @@
 from windpowerlib import density
 from windpowerlib import modelchain
 from windpowerlib import power_output
+from windpowerlib import power_curve
 from windpowerlib import temperature
 from windpowerlib import tools
 from windpowerlib import wind_speed
 from windpowerlib import wind_turbine
+from windpowerlib import wind_farm
+from windpowerlib import turbine_cluster_modelchain
+from windpowerlib import wind_turbine_cluster
 
 from windpowerlib.wind_turbine import WindTurbine
 from windpowerlib.modelchain import ModelChain

setup.py

@@ -10,8 +10,6 @@
 import numpy as np
 import pandas as pd
 from windpowerlib import tools
-from matplotlib import pyplot as plt
-import os
 
 
 def smooth_power_curve(power_curve_wind_speeds, power_curve_values,

windpowerlib/__init__.py

@@ -60,8 +60,8 @@ def linear_interpolation_extrapolation(df, target_height):
     ...                                              'wind_speed']),
     ...                                    np.array([10, 80])])
     >>> round(linear_interpolation_extrapolation(
-    ...     weather_df['wind_speed'], 100)[0], 10)
-    6.8571428571
+    ...     weather_df['wind_speed'], 100)[0], 2)
+    6.86
 
     """
     # find closest heights
@@ -71,53 +71,3 @@ def linear_interpolation_extrapolation(df, target_height):
     return ((df[heights_sorted[1]] - df[heights_sorted[0]]) /
             (heights_sorted[1] - heights_sorted[0]) *
             (target_height - heights_sorted[0]) + df[heights_sorted[0]])
-
-
-def gaussian_distribution(function_variable, standard_deviation, mean=0):
-    r"""
-    Normal distribution or gaussian distribution.
-
-    Parameters
-    ----------
-    function_variable : Float
-        Variable of the gaussian distribution.
-    standard_deviation : Float
-        Standard deviation of the gaussian distribution.
-    mean : Float
-        Defines the offset of the gaussian distribution function. Default: 0.
-
-    Returns
-    -------
-    pandas.Series or numpy.array
-        Wind speed at hub height. Data type depends on type of `wind_speed`.
-
-    Notes
-    -----
-    The following equation is used [1]_:
-
-    .. math:: f(x) = \frac{1}{\sigma \sqrt{2 \pi}} exp
-                     \left[ -\frac{(x-\mu)^2}{2 \sigma^2}  \right]
-
-    with:
-        # TODO: add variables
-
-    References
-    ----------
-    .. [1] Berendsen, H.: "A Student's Guide to Data and Error Analysis".
-             New York, Cambridge University Press, 2011, p. 37
-    
-    # TODO: add references
-
-    """
-    return (1 / (standard_deviation * np.sqrt(2 * np.pi)) *
-            np.exp(-(function_variable - mean)**2 /
-                   (2 * standard_deviation**2)))
-
-
-def estimate_turbulence_intensity(height, roughness_length):
-    """
-    Calculate turbulence intensity.
-
-    """
-    # TODO: Search other possibilities for TI.
-    return 1 / (np.log(height / roughness_length))

windpowerlib/power_curve.py

@@ -7,7 +7,8 @@
 __copyright__ = "Copyright oemof developer group"
 __license__ = "GPLv3"
 
-from . import modelchain, tools, wind_farm, power_curve, wind_turbine_cluster
+from windpowerlib import modelchain, tools, wind_farm, power_curve, \
+    wind_turbine_cluster
 import pandas as pd