adjust usage file

Author: cpdmulde

_sources/usage.rst.txt

@@ -10,7 +10,7 @@ As the package was developed with the help of Jupyter Notebook (version 4.4.1) a
 
 Once the package is imported, the suggested workflow is as follows:
 
-1. Read data and convert it to a pandas DataFrame (see [the pandas documentation](https://pandas.pydata.org) for more information); format it in the way you like.
+1. Read data and convert it to a pandas DataFrame (see (the pandas documentation)[https://pandas.pydata.org) for more information]; format it in the way you like.
 
 2. Create an object of any of the three classes in the wwdata package, e.g. the OnlineSensorBased class::
 
@@ -21,14 +21,12 @@ Once the package is imported, the suggested workflow is as follows:
     data.to_datetime(time_column="time",time_format="%dd-%mm-%yy")
     data.get_avg()
 
-4. Tag non-valid data points. The way to do this depends on the data you are working with, but the general approach would be::
+4. Tag non-valid data points. The way to do this depends on the data you are working with, but the general approach would be to simply apply any of the filtering functions to the class object::
 
     data.tag_nan()
     data.moving_slope_filter(xdata="time",data_name="series1",cutoff=3,arange=['1/1/2017','1/2/2017'])
 
-i.e. to simply apply any of the filtering functions to the class object.
-
-5. Apply any other functionalities to the object. In the below example, this is the filling of the gaps introduced by filtering data in the previous step (for details on the meaning of the arguments, please refer to the documentation provided within the source code)::
+5. Apply any other functionalities to the object. In the below example, this is the filling of the gaps introduced by filtering data in the previous step (for details on the meaning of the arguments, please refer to the specific documentation provided)::
 
     data.fill_missing_interpolation("series1",range_=12,arange=['1/1/2017','1/2/2017'])
     data.fill_missing_model("series1","model_data_series",arange=['1/1/2017','1/2/2017'])