Updated docs and some static files

carculator_truck/driving_cycles.py

@@ -3,6 +3,13 @@
 
 
 def get_driving_cycle(size: list, name: str) -> np.ndarray:
+    """
+    Get driving cycle.
+
+    :param size: List of vehicle sizes.
+    :param name: The name of the driving cycle.
+    :return: :meth:`ndarray` object
+    """
     return get_standard_driving_cycle_and_gradient(
         vehicle_type="truck",
         vehicle_sizes=size,
@@ -11,6 +18,13 @@ def get_driving_cycle(size: list, name: str) -> np.ndarray:
 
 
 def get_road_gradient(size: list, name: str) -> np.ndarray:
+    """
+    Get road gradient data.
+
+    :param size: List of vehicle sizes.
+    :param name: The name of the driving cycle.
+    :return: :meth:`ndarray` object
+    """
     return get_standard_driving_cycle_and_gradient(
         vehicle_type="truck",
         vehicle_sizes=size,

docs/structure.rst

@@ -1,14 +1,16 @@
+.. _structure:
+
 Structure
 =========
 
-.. image:: https://github.com/romainsacchi/carculator/raw/master/docs/coarse.png
+.. image:: https://github.com/romainsacchi/carculator/blob/master/docs/_static/img/coarse.png?raw=true
     :width: 900
     :alt: Alternative text
 
 Modules
 -------
 
-Composed of eight modules to build the car models:
+Composed of eight modules to build the truck models:
 
 * Driving cycle module
 * Mass module
@@ -24,31 +26,31 @@ Additionally, three modules are used to:
 * configure energy systems for the background model (background systems module)
 * build and solve the life cycle inventory of cars (inventory module)
 * export the life cycle inventory of cars (export module)
-    
+
 Driving cycle module
 --------------------
 
-.. image:: https://github.com/romainsacchi/carculator/raw/master/docs/driving_cycle.png
+.. image:: https://github.com/romainsacchi/carculator/blob/master/docs/_static/img/driving_cycle.png?raw=true
     :width: 400
     :alt: Alternative text
-    
+
 Mass module
 -----------
 
-.. image:: https://github.com/romainsacchi/carculator/raw/master/docs/mass_module.png
+.. image:: https://github.com/romainsacchi/carculator/blob/master/docs/_static/img/mass_module.png?raw=true
     :width: 900
     :alt: Alternative text
-    
+
 Auxiliary energy module
 -----------------------
 
-.. image:: https://github.com/romainsacchi/carculator/raw/master/docs/aux_energy.png
+.. image:: https://github.com/romainsacchi/carculator/blob/master/docs/_static/img/aux_energy.png?raw=true
     :width: 900
     :alt: Alternative text
-    
+
 Motive energy module
 --------------------
 
-.. image:: https://github.com/romainsacchi/carculator/raw/master/docs/motive_energy.png
+.. image:: https://github.com/romainsacchi/carculator/blob/master/docs/_static/img/motive_energy.png?raw=true
     :width: 900
     :alt: Alternative text

docs/usage.rst

@@ -1,14 +1,15 @@
 .. _usage:
 
-Using Carculator truck
+Using Carculator Truck
 ======================
 
 Static vs. Stochastic mode
 --------------------------
 
 .. note:: 
 
-   Many examples are given in this `notebook <https://github.com/romainsacchi/carculator_truck/blob/master/examples/Examples.ipynb>`_ which you can run directly on your computer.
+   Many examples are given in this :download:`examples.zip file <_static/resources/examples.zip>` which contains a Jupyter notebook
+    you can run directly on your computer.
 
 The inventories can be calculated using the most likely value of the given input parameters ("static" mode), but also using
 randomly-generated values based on a probability distribution for those ("stochastic" mode).

docs/validity.rst

@@ -1,3 +1,5 @@
+.. _validity:
+
 Validity tests
 ==============
 
@@ -46,7 +48,7 @@ Manually, such parameters can be obtained the following way:
     acceleration = np.zeros_like(velocity)
     acceleration[1:-1] = (velocity[2:] - velocity[:-2])/2
 
-Using `carculator`, these parameters can be obtained the following way:
+Using ``carculator``, these parameters can be obtained the following way:
 
 .. code-block:: python
 
@@ -84,7 +86,9 @@ And the acceleration returned by carculator should equal the values given by the
     
     True
     
-Which can be also be verified visually::
+Which can be also be verified visually:
+
+.. code-block:: python
 
     plt.plot(driving_cycle['m/s²'].values, label='UNECE')
     plt.plot(acceleration, label='Manually calculated')
@@ -95,9 +99,10 @@ Which can be also be verified visually::
     plt.savefig('comparison_driving_cycle.png')
     plt.show()
 
-.. image:: https://github.com/romainsacchi/carculator/raw/master/docs/comparison_driving_cycle.png
+.. image:: https://github.com/romainsacchi/carculator/blob/master/docs/_static/img/comparison_driving_cycle.png?raw=true
     :width: 400
-    :alt: Alternative text
+    :alt: Comparison driving cycle
+    :align: center
 
 Car and components masses
 -------------------------
@@ -142,9 +147,10 @@ However, most of the driving mass is explained by the curb mass:
         parameter=['total cargo mass', 'curb mass']).values).tolist(), labels=['Total cargo mass', 'Curb mass'])
     plt.show()
 
-.. image:: https://github.com/romainsacchi/carculator/raw/master/docs/pie_total_mass.png
+.. image:: https://github.com/romainsacchi/carculator/blob/master/docs/_static/img/pie_total_mass.png?raw=true
     :width: 400
-    :alt: Alternative text
+    :alt: Pie Total Mass
+    :align: center
 
 Here is a split between the components making up for the curb mass.
 One can see that, in the case of a battery electric SUV, most of the weight comes from the glider as well as the battery cells.
@@ -204,17 +210,18 @@ On an equivalent diesel powertrain, the mass of the glider base is comparatively
     plt.subplots_adjust(wspace=1)
     plt.show()
   
-.. image:: https://github.com/romainsacchi/carculator/raw/master/docs/pie_mass_components.png
+.. image:: https://github.com/romainsacchi/carculator/blob/master/docs/_static/img/pie_mass_components.png?raw=true
     :width: 900
-    :alt: Alternative text
-
+    :alt: Pie Mass Components
+    :align: center
 
 
 The `curb mass` returned by ``carculator`` for the year 2010 and 2020 is further calibrated against manufacturers' data, per vehicle size class and powertrain technology.
 For example, we use the car database `Car2db <https://car2db.com/>`_ and load all the vehicles produced between 2015 and 2019 (11,500+ vehicles) to do the curb mass calibration for 2020 vehicles.
 The same exercise is done with vehicles between 2008 and 2012 to calibrate the curb mass of given by carculator for vehicles in 2010.
 
 
-.. image:: https://github.com/romainsacchi/carculator/raw/master/docs/mass_comparison.png
+.. image:: https://github.com/romainsacchi/carculator/blob/master/docs/_static/img/mass_comparison.png?raw=true
     :width: 900
-    :alt: Alternative text
+    :alt: Mass Comparison
+    :align: center