Merge branch 'dev' into features/wind_farms_and_clusters

Author: SabineHaas

doc/requirements.txt

@@ -1,3 +1,5 @@
 sphinx>=1.4
 ipykernel
-nbsphinx
+nbsphinx
+pandas
+numpy

example/basic_example.ipynb

@@ -177,7 +177,7 @@
     "# specification of own wind turbine (Note: power coefficient values and\n",
     "# nominal power have to be in Watt)\n",
     "myTurbine = {\n",
-    "    'turbine_name': 'myTurbine',\n",
+    "    'object_name': 'myTurbine',\n",
     "    'nominal_power': 3e6,  # in W\n",
     "    'hub_height': 105,  # in m\n",
     "    'rotor_diameter': 90,  # in m\n",
@@ -202,7 +202,7 @@
     "# if you want to use the power coefficient curve change the value of\n",
     "# 'fetch_curve' to 'power_coefficient_curve'\n",
     "enerconE126 = {\n",
-    "    'turbine_name': 'ENERCON E 126 7500',  # turbine name as in register\n",
+    "    'object_name': 'ENERCON E 126 7500',  # turbine name as in register\n",
     "    'hub_height': 135,  # in m\n",
     "    'rotor_diameter': 127,  # in m\n",
     "    'fetch_curve': 'power_curve'  # fetch power curve\n",
@@ -357,7 +357,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.2"
+   "version": "3.5.3"
   }
  },
  "nbformat": 4,

windpowerlib/modelchain.py

@@ -9,7 +9,7 @@
 __license__ = "GPLv3"
 
 import logging
-from . import wind_speed, density, temperature, power_output, tools
+from windpowerlib import wind_speed, density, temperature, power_output, tools
 
 
 class ModelChain(object):
@@ -88,7 +88,8 @@ class ModelChain(object):
     >>> enerconE126 = {
     ...    'hub_height': 135,
     ...    'rotor_diameter': 127,
-    ...    'object_name': 'ENERCON E 126 7500'}
+    ...    'object_name': 'ENERCON E 126 7500',
+    ...    'fetch_curve': 'power_curve'}
     >>> e126 = wind_turbine.WindTurbine(**enerconE126)
     >>> modelchain_data = {'density_model': 'ideal_gas'}
     >>> e126_mc = modelchain.ModelChain(e126, **modelchain_data)

windpowerlib/tools.py

@@ -59,9 +59,9 @@ def linear_interpolation_extrapolation(df, target_height):
     ...                           columns=[np.array(['wind_speed',
     ...                                              'wind_speed']),
     ...                                    np.array([10, 80])])
-    >>> linear_interpolation_extrapolation(
-    ...     weather_df['wind_speed'], 100)[0]
-    6.8571428571428577
+    >>> round(linear_interpolation_extrapolation(
+    ...     weather_df['wind_speed'], 100)[0], 10)
+    6.8571428571
 
     """
     # find closest heights