- README update

README.md

@@ -4,7 +4,7 @@ Take any normal serial but parallelizable for-loop and execute it in parallel us
 Don't worry about the technical details of using the multiprocessing module, race conditions, queues,
 parfor handles all that. 
 
-Tested on linux on python 2.7 and 3.8 and on Windows and OSX on python 3.8.
+Tested on linux on python 3.8 and 3.10 and on Windows and OSX on python 3.8.
 
 ## Why is parfor better than just using multiprocessing?
 - Easy to use
@@ -12,19 +12,20 @@ Tested on linux on python 2.7 and 3.8 and on Windows and OSX on python 3.8.
 - Progress bars are built-in
 
 ## Installation
-pip install parfor
+`pip install parfor`
 
 ## Usage
 Parfor decorates a functions and returns the result of that function evaluated in parallel for each iteration of
 an iterator.
 
 ## Requires
-tqdm, dill
+tqdm, dill, psutil
 
 ## Limitations
 Objects passed to the pool need to be dillable (dill needs to serialize them). Generators and SwigPyObjects are examples
 of objects that cannot be used. They can be used however, for the iterator argument when using parfor, but its
-iterations need to be dillable. You might be able to make objects dillable anyhow using dill.register.
+iterations need to be dillable. You might be able to make objects dillable anyhow using `dill.register` or with
+`__reduce__`, `__getstate__`, etc.
 
 The function evaluated in parallel needs to terminate. If parfor hangs after seeming to complete the task, it probably
 is because the individual processes cannot terminate. Importing javabridge (used in python-bioformats) and starting the
@@ -36,24 +37,26 @@ On OSX the buffer bar does not work due to limitations of the OS.
 ## Arguments
 ### Required:
     fun:      function taking arguments: iteration from  iterable, other arguments defined in args & kwargs
-    iterable: iterable from which an item is given to fun as a first argument
+    iterable: iterable or iterator from which an item is given to fun as a first argument
 
 ### Optional:
     args:   tuple with other unnamed arguments to fun
     kwargs: dict with other named arguments to fun
-    length: give the length of the iterator in cases where len(iterator) results in an error
+    total:  give the length of the iterator in cases where len(iterator) results in an error
     desc:   string with description of the progress bar
-    bar:    bool enable progress bar
-    pbar:   bool enable buffer indicator bar
+    bar:    bool enable progress bar,
+                or a callback function taking the number of passed iterations as an argument
+    pbar:   bool enable buffer indicator bar, or a callback function taking the queue size as an argument
+    terminator: function which is executed in each worker after all the work is done
     rP:     ratio workers to cpu cores, default: 1
     nP:     number of workers, default, None, overrides rP if not None
-        number of workers will always be at least 2
-    serial: switch to serial if number of tasks less than serial, default: 4
-    debug:  if an error occurs in an iteration, return the erorr instead of retrying in the main process
+    serial: execute in series instead of parallel if True, None (default): let pmap decide
+    qsize:  maximum size of the task queue
+    length: deprecated alias for total
+    **bar_kwargs: keywords arguments for tqdm.tqdm
 
 ### Return
-    list with results from applying the decorated function to each iteration of the iterator
-    specified as the first argument to the function
+    list with results from applying the function 'fun' to each iteration of the iterable / iterator
 
 ## Examples
 ### Normal serial for loop
@@ -139,28 +142,28 @@ pmap maps an iterator to a function like map does, but in parallel
     
 ### Using generators
 If iterators like lists and tuples are too big for the memory, use generators instead.
-Since generators don't have a predefined length, give parfor the length as an argument (optional). 
+Since generators don't have a predefined length, give parfor the length (total) as an argument (optional). 
     
     <<
     import numpy as np
     c = (im for im in imagereader)
-    @parfor(c, length=len(imagereader))
+    @parfor(c, total=len(imagereader))
     def fun(im):
         return np.mean(im)
         
     >> [list with means of the images]
     
 # Extra's
-## Pmap
-The function parfor decorates, use it like map.
+## `pmap`
+The function parfor decorates, use it like `map`.
 
-## Chunks
+## `Chunks`
 Split a long iterator in bite-sized chunks to parallelize
 
-## Parpool
+## `Parpool`
 More low-level accessibility to parallel execution. Submit tasks and request the result at any time,
 (although necessarily submit first, then request a specific task), use different functions and function
 arguments for different tasks.
 
-## TqdmMeter
+## `TqdmMeter`
 Meter bar, inherited from tqdm, used for displaying buffers.