Issue with calling transform() on linear and glog

[dmoccia]

Not sure if I am calling this incorrectly but I tried the following two transforms:

tsample = sample.transform('glog',  channels=['Y2-A', 'B1-A', 'V2-A'], l=10)
tsample = sample.transform('linear',  channels=['Y2-A', 'B1-A', 'V2-A'], old_range=0.5, new_range=2.5)

but I am getting back (after stack trace attached) stacktrace.txt

linear() got an unexpected keyword argument 'd' I checked the code itself and looked like these were acceptable arguments, can you confirm?

Thank you

[eyurtsev]

thanks for reporting this looks like something is passing an extra 'd' argument down the road

in the meantime you can define your own custom transformations if you need a workaround

http://eyurtsev.github.io/FlowCytometryTools/gallery.html#custom-transformations

[dmoccia]

No problem on the report, nice looking library. I will try the custom transforms out.

[dmoccia]

I believe I figured out why this issue is happening. In your FCMeasurement.tranform method you have the following code

`if isinstance(transform, Transformation): transformer = transform else: if auto_range: # determine transformation range if 'd' in kwargs: warnings.warn( 'Encountered both auto_range=True and user-specified range value in ' 'parameter d.\n Range value specified in parameter d is used.') else: print "in else" channel_meta = self.channels

the -1 below because the channel numbers begin from 1 instead of 0

                # (this is fragile code)
                ranges = [float(r['$PnR']) for i, r in channel_meta.iterrows() if
                          self.channel_names[i - 1] in channels]
                if not np.allclose(ranges, ranges[0]):
                    raise Exception("""Not all specified channels have the same data range,
                        therefore they cannot be transformed together.\n
                        HINT: Try transforming one channel at a time.
                        You'll need to provide the name of the channel in the transform.""")
                kwargs['d'] = np.log10(ranges[0])`

Specifically the "isinstance" check is not working and therefore everything (linear, hlog, tlog, etc) is falling through to the final line kwargs['d'] = np.log10(ranges[0])

In this case d will always be 'd': 5.4185399219516617

and therefore for hlog, tlog this is not an issue (except if you want to use another d), and linear and glog it is an issue since neither take d as an argument. I do not use isinstance that often unless referring to built in types so I am not familiar with how to properly use it in a custom class. Probably not the most elegant solution but perhaps a simple function in the transforms.py code which allows you to check from a list?

Let me know if you need any more info

[eyurtsev]

Thanks for looking into this... This commit should fix the issue:

https://github.com/eyurtsev/FlowCytometryTools/commit/44e83526e975279bfd61aca58485e95869c4f47b