improve docs

docs/conf.py

@@ -79,7 +79,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = 'en'
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.

docs/content/punpy_digital_effects_table.rst

@@ -104,7 +104,7 @@ Once this kind of measurement function class is defined, we can initialise an ob
 In principle there are no required arguments when creating an object of this class (all arguments have a default).
 However, in practise we will almost always provide at least some arguments.
 The first argument `prop` allows to pass a MCPropagation or LPUpropagaion object. It thus specifies whether the Monte Carlo (MC) method (see Section :ref:`Monte Carlo Method`)
-or Law of Propagation of Uncertainties (LPU) method (see Section :ref:`LPU Method`) should be used. These prop objects can be created with any of their options (such as parallel_cores)::
+or Law of Propagation of Uncertainties (LPU) method (see Section :ref:`LPUMethod`) should be used. These prop objects can be created with any of their options (such as parallel_cores)::
 
    prop = MCPropagation(1000, dtype="float32", verbose=False, parallel_cores=4)
 
@@ -205,7 +205,9 @@ This behaviour could also be obtained by removing the unc_comps in the temperatu
 n_moles variables in their respective datasets, but the solution shown above is easier.
 If no uncxvariables are provided, the uncertainties on all input quantities are propagated.
 
+
 .. _MeasurementFunctionOptions:
+
 Options when creating MeasurementFunction object
 ##################################################
 A number of additional options are available when creating the MeasurementFunction object, and when running one of the propagate_ds functions.

docs/content/punpy_memory_and_speed.rst

@@ -32,7 +32,7 @@ be correlated, this error correlation w.r.t x does not change for different valu
 In that case, the error correlation for x can be separated and stored as a matrix of shape (x,x).
 
 One way to reduce the memory load is by separately storing the error-correlation matrices in the output dataset.
-In the :ref:`_punpy_standalone` section, we showed that the `corr_dims` keyword can be used to output the error correlation matrix for a single dimension rather than the full error correlation matrix.
+In the :ref:`punpy_standalone` section, we showed that the `corr_dims` keyword can be used to output the error correlation matrix for a single dimension rather than the full error correlation matrix.
 This can also be used to separately store the error correlation for each dimension by passing a list of all dimension indices for `corr_dims`::
 
    ur_y, corr_y = prop.propagate_random(measurement_function,

docs/content/punpy_standalone.rst

@@ -285,7 +285,7 @@ This is done by setting the `separate_corr_dims` keyword to True, and passing a
                             [x1, x2, x3], [us_x1, us_x2, us_x3],
                             return_corr=True, corr_dims=[0,1],separate_corr_dims=True, output_vars=2)
 
-It is also possible to set one of the separate `corr_dims' to None if you do not want the error correlation to be calculated for that measurand. In that case None will be returned (as corr_y[1] in below example)::
+It is also possible to set one of the separate `corr_dims` to None if you do not want the error correlation to be calculated for that measurand. In that case None will be returned (as corr_y[1] in below example)::
 
    us_y, corr_y, corr_out = prop.propagate_systematic(measurement_function,
                             [x1, x2, x3], [us_x1, us_x2, us_x3],

docs/content/user_guide.rst

@@ -8,6 +8,5 @@ In this user guide, you will find detailed descriptions and examples that descri
    :maxdepth: 2
 
    punpy_standalone
-   punpy_difital_effects_table
+   punpy_digital_effects_table
    punpy_memory_and_speed
-   args_and_kwargs

punpy/_version.py

@@ -1 +1 @@
-__version__ = "0.43.3"
+__version__ = "0.43.4"