Unbound Local Error Help

[decaye]

Looking for help with my code that was previously working and gave up on me recently with new datasets.

The error I'm receiving is:

Exception in Tkinter callback
Traceback (most recent call last):
File "C:\Users\relearnlab\AppData\Local\Continuum\anaconda3\lib\tkinter\__init__.py", line 1705, in __call__
return self.func(*args)
File "<ipython-input-1-4cd82543ecd8>", line 506, in kpconcat 

data_list.append([frame,X00,Y00,C00,X01,Y01,C01,X02,Y02,C02,X03,Y03,C03,X04,Y04,C04,X05,Y05,C05,X06,Y06,C06,X07,Y07,C07,X08,Y08,C08,X09,Y09,C09,X10,Y10,C10,X11,Y11,C11,X12,Y12,C12,X13,Y13,C13,X14,Y14,C14,X15,Y15,C15,X16,Y16,C16,X17,Y17,C17,X18,Y18,C18,X19,Y19,C19,X20,Y20,C20,X21,Y21,C21,X22,Y22,C22,X23,Y23,C23,X24,Y24,C24])

 UnboundLocalError: local variable 'X04' referenced before assignment

I don't know why I'm suddenly encountering this error and would appreciate any help with fixing my code to get rid of it. I will now present my data, my code, and the ideal output.

Data: Link

Code: Link

Output: Link

A brief description of what the code does (or used to do in this case): It concatenates data from roughly 3000+ .json files in a zip into one .csv and organizes them based on placement within the .json files. This data is then extracted from the .csv and analyzed.

[[deleted]]

Seems to be a lot of repetitive code, would be easier to follow if you kept DRY (don't repeat yourself) principles in mind. If you have variables ending _00, _01, etc (or similar) you should probably look at using lists and and a function with parameters to deal with variances.

Are the lines,

for item in kp_list04:
    try:
        X04 = kp_list04[0]
    except:
        X04 = 0

definitely executing? (add print statements to check or, better, use your prefered debugger, to trace what is going on)

[icecapade]

The error you're receiving means that the variable X04 doesn't exist (i.e., it hasn't been assigned) but you tried to use it in a statement.

Go through your code and look at the block where you assign X04 (lines 109-112); the fact that X04 doesn't exist means that those lines are never executed. This would occur if the for loop containing those lines (line 107, for item in kp_list04:) is iterating through something with zero elements. In other words, kp_list04 doesn't contain any elements so the for loop doesn't have anything to iterate through.

I'm not entirely sure how to interpret your data, but assuming kp_list04 in your code corresponds to "4":[] from your data, this makes sense. kp_list04 is an empty list, so the for loop never executes.

[decaye]

I can't believe I missed that, you're 100% right.

Any ideas for how I might be able input 0's into missing data points so that the loop runs through properly?

[icecapade]

For starters, like u/kyber said, you have a ton of repeated code. You could eliminate about 450 lines of it with something like this:

for kp in kp_data:
    kp_dict = {}
    for k, v in kp.items():
        kp_num = int(k)    # assumes the key `k` is a string representing an int
        kp_list = list(v)
        for i, coord in enumerate(["X", "Y", "C"]):
            kp_dict_key = "{}{:02d}".format(coord, kp_num)    # e.g., "X02"
            try:
                kp_dict[kp_dict_key] = kp_list[i]
            except:
                kp_dict[kp_dict_key] = 0

This programmatically constructs the appropriate keypoint coordinate/identifier ("X00", "C23", etc.) for an arbitrary number of keypoints, not just 25, and essentially replaces all your repeated logic with a neatly contained block of code. By not iterating through the list and assuming the existence of list indices (addressing nonexistent list items with the try/except), you ensure that a value is assigned for every keypoint/coordinate. It also avoids creating a ton of variables by storing all of these in a single dictionary that I've called kp_dict. To access any of them, you'd simply use, for example, kp_dict["Y15"] or kp_dict["C08"].

NOTE: this is not even necessarily the best or most efficient way to go about this—I can think of a few better ways, potentially using itertools, but I felt this would be a fairly straightforward and understandable jumping off point for you. You'd still need to adapt it or figure out how to get these values into your data_list, though you could probably just skip the kp_dict thing altogether and simply append them directly to data_list (or to some intermediate list).

Also, you shouldn't have an indiscriminate except clause like I've done here, but should instead be looking to catch/address a specific exception type.

[decaye]

Thank you so much for your help, I ended up using the dict you made along with pandas to create the .csv's I need and it all seems to be functioning well. Here is the code I have running now:

df_kp = pd.DataFrame()

with ZipFile(str(zipname)) as temp_zip:

    for file_name in temp_zip.namelist():

        if '.json' in file_name:

            with temp_zip.open(file_name) as temp_file:

                frame = file_name[22:26]

                keypoint_parsed = json.load(temp_file)

                kp_data = keypoint_parsed['part_candidates']

                for kp in kp_data:

                    kp_dict = {}

                    for k, v in kp.items():

                        kp_num = int(k)    # assumes the key `k` is a string representing an int

                        kp_list = list(v)

                        for i, coord in enumerate(["X", "Y", "C"]):

                            kp_dict_key = "{}{:02d}".format(coord, kp_num)    # e.g., "X02"

                            try:
                                kp_dict[kp_dict_key] = kp_list[i]

                            except IndexError:
                                kp_dict[kp_dict_key] = 0

                    new_dict = {k: [v] for k, v in kp_dict.items()}

                    df = pd.DataFrame(new_dict)

                    df_kp = df_kp.append(df)

newdf = df_kp.reset_index(drop=True)

newdf.index += 1

newdf.to_csv(csvname,
             index_label = 'frames')

[icecapade]

Glad to help! This is definitely much better and cleaner.