diff --git a/_proc/.gitignore b/_proc/.gitignore new file mode 100644 index 0000000..075b254 --- /dev/null +++ b/_proc/.gitignore @@ -0,0 +1 @@ +/.quarto/ diff --git a/_proc/docs/AttrDict.html b/_proc/docs/AttrDict.html new file mode 100644 index 0000000..5581685 --- /dev/null +++ b/_proc/docs/AttrDict.html @@ -0,0 +1,1035 @@ + + + + + + + + + +attrdict – torch_snippets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ + + + + +
+
from torch_snippets.loader import L
+
+
[07/01/24 11:12:47] WARNING  Unable to load torch and dependent libraries from torch-snippets.                                                        loader.py:<module>:108
+                             Functionalities might be limited. pip install lovely-tensors in case there are torch related errors                                            
+
+
+
+
+
class AD(object):
+    forbidden = set(":,'\"}{.")
+
+    def __init__(self, *args, given_input_to_ad=None, **kwargs):
+        given_input_to_ad = {} if given_input_to_ad is None else given_input_to_ad
+        if len(args) == 1 and isinstance(args[0], (Mapping, AttrDict)):
+            given_input_to_ad = args[0]
+            args = {}
+        else:
+            _args = dict(ic.io(*args)) if len(args) > 0 else {}
+            args = {}
+            for k, v in _args.items():
+                if any(c in self.forbidden for c in k):
+                    assert isinstance(
+                        v, (dict, AttrDict)
+                    ), f"Input `{v}` can't be a list"
+                    given_input_to_ad = {**v, **given_input_to_ad}
+                else:
+                    args = {**{k: v}, **args}
+
+        given_input_to_ad = {**kwargs, **given_input_to_ad, **args}
+        for name, value in given_input_to_ad.items():
+            setattr(self, str(name), self._wrap(value))
+
+    def items(self):
+        return self.__dict__.items()
+
+    def keys(self):
+        return self.__dict__.keys()
+
+    def values(self):
+        return self.__dict__.values()
+
+    def __json__(self):
+        return self.to_dict()
+
+    def _wrap(self, value):
+        if isinstance(value, (L, tuple, list, set, frozenset)):
+            value = type(value)([self._wrap(v) for v in value])
+            if isinstance(value, (list, L)):
+                value = L(value)
+            return value
+        else:
+            return (
+                AttrDict(given_input_to_ad=value) if isinstance(value, dict) else value
+            )
+
+    __getitem__ = lambda self, x: (
+        AttrDict({_x: self[_x] for _x in x})
+        if isinstance(x, (list, L))
+        else getattr(self, str(x))
+    )
+    __setitem__ = lambda self, k, v: setattr(self, str(k), self._wrap(v))
+
+    def update(self, dict):
+        for k, v in dict.items():
+            self[k] = v
+
+    def get(self, key, default=None):
+        key = str(key)
+        return self[key] if key in self else default
+
+    def __iter__(self):
+        return iter(self.keys())
+
+    def __len__(self):
+        return len(self.keys())
+
+    def __repr__(self):
+        return f"\n```↯ AttrDict ↯\n{self.summary()}\n```\n"
+
+    def __dir__(self):
+        return self.__dict__.keys()
+
+    def __contains__(self, key):
+        key = str(key)
+        if "." not in key:
+            return key in self.__dict__.keys()
+        else:
+            d = self
+            for _k in key.split("."):
+                try:
+                    d = d[_k]
+                except AttributeError:
+                    return False
+            return True
+
+    def __delitem__(self, key):
+        key = str(key)
+        del self.__dict__[key]
+
+    def map(self, func):
+        for k in dir(self):
+            v = self[k]
+            if isinstance(v, AttrDict):
+                v.map(func)
+            elif isinstance(v, (L, tuple, list, set, frozenset)):
+                v = [_v.map(func) if isinstance(_v, AttrDict) else func(_v) for _v in v]
+            else:
+                v = func(v)
+            self[k] = v
+
+    def drop(self, key):
+        if key in self:
+            del self[key]
+        for k in dir(self):
+            v = self[k]
+            if isinstance(v, AttrDict):
+                v.drop(key)
+            if isinstance(v, (L, tuple, list, set, frozenset)):
+                v = [_v.drop(key) for _v in v if isinstance(_v, AttrDict)]
+
+    def to_dict(self):
+        d = {}
+        for k in self.__dict__.keys():  # can't use dir here
+            v = self[k]
+            if isinstance(v, AttrDict):
+                v = v.to_dict()
+            if isinstance(v, (L, tuple, list, set, frozenset)):
+                v = [_v.to_dict() if isinstance(_v, AttrDict) else _v for _v in v]
+            d[k] = v
+        return d
+
+    dict = to_dict
+
+    def pretty(self, print_with_logger=False, *args, **kwargs):
+        pretty_json(
+            self.to_dict(), print_with_logger=print_with_logger, *args, **kwargs
+        )
+
+    def __eq__(self, other):
+        return AttrDict(given_input_to_ad=other).to_dict() == self.to_dict()
+
+    def find_address(self, key, current_path=""):
+        addresses = []
+        for k in self.keys():
+            if current_path:
+                new_path = f"{current_path}.{k}"
+            else:
+                new_path = k
+
+            if k == key:
+                addresses.append(new_path)
+
+            if isinstance(self[k], AttrDict):
+                addresses.extend(self[k].find_address(key, new_path))
+
+            elif isinstance(self[k], (L, tuple, list, set, frozenset)):
+                for i, item in enumerate(self[k]):
+                    if isinstance(item, AttrDict):
+                        addresses.extend(item.find_address(key, f"{new_path}.{i}"))
+        return addresses
+
+    def summary(self, current_path="", depth=0, sep="  ", max_items=10):
+        max_items = int(os.environ.get("AD_MAX_ITEMS", max_items))
+        sep = os.environ.get("AD_SEP", sep)
+
+        def format_path(path, key):
+            return f"{path}.{key}" if path else key
+
+        def format_item(key, item, path, depth, sep):
+            import numpy as np
+            import pandas as pd
+
+            try:
+                import torch
+            except ModuleNotFoundError:
+
+                class Torch:
+                    Tensor = type(None)
+
+                torch = Torch()
+
+            if isinstance(item, (pd.DataFrame,)):
+                return f"{sep * depth}{key} - {type(item).__name__} - shape {item.shape} - columns {item.columns} - {hash_pandas_dataframe(item)}\n"
+            if isinstance(item, AttrDict) or hasattr(item, "keys"):
+                item = AttrDict(**item)
+                return f"{sep*depth}{key}\n" + item.summary(path, depth + 1, sep)
+            elif isinstance(item, (list, tuple, set, frozenset, L)):
+                return summarize_collection(key, item, path, depth + 1, sep)
+            elif isinstance(item, (torch.Tensor, np.ndarray)):
+                is_np = False
+                if isinstance(item, np.ndarray):
+                    is_np = True
+                    item = torch.tensor(item)
+                is_np = "🔦" if not is_np else "np."
+                return f"{sep * depth}{key} - {is_np}{item} - {hash_tensor(item)}\n"
+
+            else:
+                is_multiline = False
+                ogitem = item
+                if isinstance(item, (str, P)):
+                    item = str(item)
+                    is_multiline = "\n" in item
+                    _sep = " ...\n...\n...\n...\n... " if is_multiline else "........."
+                    if len(item) > 100:
+                        item = item[:35] + _sep + item[-35:]
+                    if is_multiline:
+                        _item = item.split("\n")
+                        _item = "\n".join([f"{sep*(depth+1)}{l}" for l in _item])
+                        item = f"↓\n{sep*(depth+1)}```\n{_item}\n{sep*(depth+1)}```"
+                multiline = "" if not is_multiline else "Multiline "
+                return f"{sep * depth}{key} - {item} (🏷️ {multiline}{type(ogitem).__name__})\n"
+
+        def summarize_collection(key, collection, path, d, s):
+            summary_str = f"{s * (d - 1)}{key}\n"
+            for i, item in enumerate(collection):
+                item_path = format_path(path, i)
+                if i < max_items:
+                    summary_str += format_item(i, item, item_path, d, s)
+                else:
+                    summary_str += (
+                        f"{s*d}... {len(collection) - max_items} more items ...\n"
+                    )
+                    break
+            return summary_str
+
+        summary_str = ""
+        for ix, key in enumerate(self.keys()):
+            if ix >= max_items:
+                summary_str += (
+                    f"{sep*depth} ... {len(self.keys()) - max_items} more keys ...\n"
+                )
+                break
+            new_path = format_path(current_path, key)
+            summary_str += format_item(key, self[key], new_path, depth, sep)
+        return summary_str
+
+    def print_summary(self, **kwargs):
+        from builtins import print
+
+        print(self.summary(**kwargs))
+
+    def write_summary(self, to, **kwargs):
+        writelines(self.summary(**kwargs).split("\n"), to)
+
+    def fetch(self, addr):
+        if isinstance(addr, (list, L)):
+            return L([self.fetch(_addr) for _addr in addr])
+
+        o = self
+        for p in addr.split("."):
+            try:
+                o = o[int(p)]
+            except:
+                o = o[p]
+        return o
+
+
+
x = AD(x=1)
+x
+
+
+
---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+File ~/miniconda3/miniconda3/envs/ts/lib/python3.10/site-packages/IPython/core/formatters.py:708, in PlainTextFormatter.__call__(self, obj)
+    701 stream = StringIO()
+    702 printer = pretty.RepresentationPrinter(stream, self.verbose,
+    703     self.max_width, self.newline,
+    704     max_seq_length=self.max_seq_length,
+    705     singleton_pprinters=self.singleton_printers,
+    706     type_pprinters=self.type_printers,
+    707     deferred_pprinters=self.deferred_printers)
+--> 708 printer.pretty(obj)
+    709 printer.flush()
+    710 return stream.getvalue()
+
+File ~/miniconda3/miniconda3/envs/ts/lib/python3.10/site-packages/IPython/lib/pretty.py:410, in RepresentationPrinter.pretty(self, obj)
+    407                         return meth(obj, self, cycle)
+    408                 if cls is not object \
+    409                         and callable(cls.__dict__.get('__repr__')):
+--> 410                     return _repr_pprint(obj, self, cycle)
+    412     return _default_pprint(obj, self, cycle)
+    413 finally:
+
+File ~/miniconda3/miniconda3/envs/ts/lib/python3.10/site-packages/IPython/lib/pretty.py:778, in _repr_pprint(obj, p, cycle)
+    776 """A pprint that just redirects to the normal repr function."""
+    777 # Find newlines and replace them with p.break_()
+--> 778 output = repr(obj)
+    779 lines = output.splitlines()
+    780 with p.group():
+
+Cell In[8], line 69, in AD.__repr__(self)
+     68 def __repr__(self):
+---> 69     return f"\n```↯ AttrDict ↯\n{self.summary()}\n```\n"
+
+Cell In[8], line 154, in AD.summary(self, current_path, depth, sep, max_items)
+    153 def summary(self, current_path="", depth=0, sep="  ", max_items=10):
+--> 154     max_items = int(os.environ.get("AD_MAX_ITEMS", max_items))
+    155     sep = os.environ.get("AD_SEP", sep)
+    157     def format_path(path, key):
+
+NameError: name 'os' is not defined
+
+
+
+ + + +
+ +
+ + + + + \ No newline at end of file diff --git a/_proc/docs/adapters.html b/_proc/docs/adapters.html new file mode 100644 index 0000000..9c00617 --- /dev/null +++ b/_proc/docs/adapters.html @@ -0,0 +1,856 @@ + + + + + + + + + +adapters – torch_snippets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ + + + +
+

Adapters

+

To convert data from/to various formats

+ +
+
+

file_2_bytes

+
+
 file_2_bytes (fpath)
+
+
+
+
+

bytes_2_file

+
+
 bytes_2_file (input:bytes, fpath:Union[str,pathlib.Path],
+               silent:bool=False)
+
+

Save bytes input at given fpath

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
inputbytesbytes
fpathUnionPlace where you want to save the file
silentboolFalse
ReturnsNone
+
+
+
+

b64_2_file

+
+
 b64_2_file (input:str, fpath:Union[str,pathlib.Path])
+
+

Save a file encoded as a base64 input at given fpath

+ + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDetails
inputstrbase64 encoded string
fpathUnionPlace where you want to save the file
ReturnsNone
+
+
+
+

b64_2_np

+
+
 b64_2_np (input:str)
+
+

*Converts a base64 encoded image to a NumPy array.

+

Args: input (str): The base64 encoded image.

+

Returns: np.ndarray: The NumPy array representation of the image in RGB format.*

+
+
+
+

np_2_b64

+
+
 np_2_b64 (image:numpy.ndarray)
+
+

Convert a numpy image to base64 string

+
+
+
+

cvat_2_csvs

+
+
 cvat_2_csvs (xmlfile, csvs_folder)
+
+

*Convert CVAT XML annotations to CSV files.

+

Args: xmlfile (str): Path to the CVAT XML file. csvs_folder (str): Path to the folder where the CSV files will be saved.

+

Returns: None*

+
+
+
+

csvs_2_cvat

+
+
 csvs_2_cvat (images_folder, csvs_folder, xml_output_file, items=None,
+              parquet=False, relative_df=True, default_label='Background',
+              extension='jpg')
+
+

*Convert CSV annotations to CVAT XML format.

+

Args: images_folder (str): Path to the folder containing the images. csvs_folder (str): Path to the folder containing the CSV annotations. xml_output_file (str): Path to the output XML file. items (list, optional): List of items to process. If None, all items will be processed. Defaults to None. parquet (bool, optional): Whether the annotations are stored in Parquet format. Defaults to False. relative_df (bool, optional): Whether the bounding box coordinates in the CSV are relative to the image size. Defaults to True. default_label (str, optional): Default label for the bounding boxes. Defaults to “Background”. extension (str, optional): Image file extension. Defaults to “jpg”.

+

Returns: None*

+
+
+
+

yolo_2_df

+
+
 yolo_2_df (yolo, h, w, id2class, class_column)
+
+
+
+
+

df_2_yolo

+
+
 df_2_yolo (df, h, w, class2id, class_column)
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/_proc/docs/bokeh_plotting.html b/_proc/docs/bokeh_plotting.html new file mode 100644 index 0000000..003a150 --- /dev/null +++ b/_proc/docs/bokeh_plotting.html @@ -0,0 +1,756 @@ + + + + + + + + + + +Bokeh Plots – torch_snippets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Bokeh Plots

+
+ +
+
+ For rapid prototyping bokeh charts +
+
+ + +
+ + + + +
+ + + +
+ + + +
+
bplot = get_bplot()
+bplot.line(np.arange(100), np.cumsum(np.random.uniform(size=100)))
+bshow(bplot)
+
+ + + +
+ +
+ + + + + \ No newline at end of file diff --git a/_proc/docs/bounding_boxes.html b/_proc/docs/bounding_boxes.html new file mode 100644 index 0000000..283f615 --- /dev/null +++ b/_proc/docs/bounding_boxes.html @@ -0,0 +1,1114 @@ + + + + + + + + + +bounding_boxes – torch_snippets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ + + + +
+

Bounding Box

+ +

Create a box by mentioning the top-left (x, y) and bottom-right (X, Y) coordinates
+Say x, y, X, Y are 10, 20, 40, 50 respectively

+
+
bb = BB([10, 20, 40, 50])
+
+

You get the following attributes for free

+
+
+

+bb.x=10              (top left - x)
+bb.y=20              (top left - y)
+bb.X=40              (bottom right - x)
+bb.Y=50              (bottom right - y)
+bb.w=30              (width)
+bb.h=30              (height)
+bb.xc=25.0           (center x)
+bb.yc=35.0           (center y)
+bb.c=(25.0, 35.0)    (center)
+bb.area=900          (area)
+bb.shape=(30, 30)    (height, width)
+
+
+
+
+
from torch_snippets import show, read, P, pd
+
+
+
assets = P("/Users/yeshwanth.y/code/torch_snippets/assets/")
+im = read(assets / "Preamble.png")
+df = pd.read_csv(assets / "Preamble.csv")
+show(df.head())
+df = to_absolute(df, *im.shape[:2])
+show(df.head())
+df = to_relative(df, *im.shape[:2])
+show(df.head())
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
xyXYtextblock_id
0135181308218ConstITUtIO0
1156264217284NLTHE1
2218264276284PEOPLE1
3267265295282OF1
4297264341284INDIA,1
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
xyXYtextblock_id
0135181308218ConstITUtIO0
1156264217284NLTHE1
2218264276284PEOPLE1
3267265295282OF1
4297264341284INDIA,1
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
xyXYtextblock_id
00.2495380.2535010.5693160.305322ConstITUtIO0
10.2883550.3697480.4011090.397759NLTHE1
20.4029570.3697480.5101660.397759PEOPLE1
30.4935300.3711480.5452870.394958OF1
40.5489830.3697480.6303140.397759INDIA,1
+
+
+
+
show(im, df=df, sz=10)
+
+
+
+

+
+
+
+
+
+
show(im, df=to_absolute(df, *im.shape[:2]), sz=10)
+
+
+
+

+
+
+
+
+
+
_df = to_absolute(df, *im.shape[:2])
+show(im, df=to_relative(_df, *im.shape[:2]), sz=10)
+
+
+
+

+
+
+
+
+
+
_df = combine_xyXY_to_bb(_df)
+show(im, df=to_relative(_df, *im.shape[:2]), sz=10)
+
+
+
+

+
+
+
+
+
+
to_relative(_df, *im.shape[:2])
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
textblock_idbb
0ConstITUtIO0[0.24953789279112754, 0.2535014005602241, 0.56...
1NLTHE1[0.28835489833641403, 0.3697478991596639, 0.40...
2PEOPLE1[0.4029574861367837, 0.3697478991596639, 0.510...
3OF1[0.49353049907578556, 0.3711484593837535, 0.54...
4INDIA,1[0.5489833641404805, 0.3697478991596639, 0.630...
............
68GIVE13[0.4011090573012939, 0.7366946778711485, 0.478...
69TO13[0.4879852125693161, 0.7366946778711485, 0.536...
70oUrSELVES13[0.5508317929759704, 0.7394957983193278, 0.706...
71THIS13[0.7190388170055453, 0.7366946778711485, 0.783...
72CONSTITUTION.14[0.23290203327171904, 0.7689075630252101, 0.44...
+ +

73 rows × 3 columns

+
+
+
+
+
+

isin

+
+
 isin (bboxes1, bboxes2, return_matrix=True)
+
+

return indexes of those boxes from bboxes1 that are completely inside bboxes2

+
+
+
+

merge_by_bb

+
+
 merge_by_bb (df1, df2, suffixes=('_x', '_y'), iou_threshold=0.1)
+
+

Merge df1 columns to df2 by using iou Make sure both df1 & df2 are relative or both absolute

+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/_proc/docs/bounding_boxes_files/figure-html/cell-6-output-1.png b/_proc/docs/bounding_boxes_files/figure-html/cell-6-output-1.png new file mode 100644 index 0000000..df323fd Binary files /dev/null and b/_proc/docs/bounding_boxes_files/figure-html/cell-6-output-1.png differ diff --git a/_proc/docs/bounding_boxes_files/figure-html/cell-7-output-1.png b/_proc/docs/bounding_boxes_files/figure-html/cell-7-output-1.png new file mode 100644 index 0000000..2cc1ff9 Binary files /dev/null and b/_proc/docs/bounding_boxes_files/figure-html/cell-7-output-1.png differ diff --git a/_proc/docs/bounding_boxes_files/figure-html/cell-8-output-1.png b/_proc/docs/bounding_boxes_files/figure-html/cell-8-output-1.png new file mode 100644 index 0000000..2cc1ff9 Binary files /dev/null and b/_proc/docs/bounding_boxes_files/figure-html/cell-8-output-1.png differ diff --git a/_proc/docs/bounding_boxes_files/figure-html/cell-9-output-1.png b/_proc/docs/bounding_boxes_files/figure-html/cell-9-output-1.png new file mode 100644 index 0000000..2cc1ff9 Binary files /dev/null and b/_proc/docs/bounding_boxes_files/figure-html/cell-9-output-1.png differ diff --git a/_proc/docs/capsule.html b/_proc/docs/capsule.html new file mode 100644 index 0000000..1797b78 --- /dev/null +++ b/_proc/docs/capsule.html @@ -0,0 +1,873 @@ + + + + + + + + + +Capsule (Tutorial) – torch_snippets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Capsule (Tutorial)

+
+ + + +
+ + + + +
+ + + +
+ + + +

Let’s load the iris dataset first

+
+
from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+
+data = load_iris()
+X, y = data.data, data.target
+X_trn, X_val, y_trn, y_val = train_test_split(X, y, random_state=42)
+
+

… and create the data loaders

+
+
from torch_snippets.markup2 import AD
+from torch.utils.data import TensorDataset
+
+trn_ds = TensorDataset(*[torch.Tensor(i) for i in [X_trn, y_trn]])
+trn_dl = DataLoader(trn_ds, batch_size=32)
+
+val_ds = TensorDataset(*[torch.Tensor(i) for i in [X_val, y_val]])
+val_dl = DataLoader(val_ds, batch_size=32)
+
+AD(next(iter(val_dl)))
+
+

+```↯ AttrDict ↯
+next(iter(val_dl))[]
+  0 - 🔦tensor[32, 4] n=128 x∈[0.100, 7.900] μ=3.488 σ=2.034 - ID:#56f55949
+  1 - 🔦tensor[32] x∈[0., 2.000] μ=0.969 σ=0.861 - ID:#b365fa0a
+
+```
+
+
+

Next we’ll import Capsule and a few decorators that will tell the model to change it’s mode to train/test during the fit function

+
from torch_snippets.trainer.capsule import Capsule, train, validate, predict
+

Create the neural network and define it’s forward function as usual pytorch business. Only difference now is that you’ll also add self.loss_fn and self.optimizer attributes in the init

+
class IrisModel(Capsule):
+    def __init__(self):
+        super().__init__()
+        self.model = nn.Sequential(
+            nn.BatchNorm1d(4),
+            nn.Linear(4, 16),
+            nn.Dropout(0.2),
+            nn.BatchNorm1d(16),
+            nn.ReLU(inplace=True),
+            nn.Linear(16, 8),
+            nn.Dropout(0.2),
+            nn.BatchNorm1d(8),
+            nn.ReLU(inplace=True),
+            nn.Linear(8, 3),
+        )
+        self.loss_fn = nn.CrossEntropyLoss()
+        self.optimizer = optim.Adam(self.parameters())
+
+    def forward(self, x):
+        return self.model(x)
+

To fully describe the model’s behaviour we still need to define three functions
+1. train_batch
+2. validate_batch and,
+3. predict which is optional

+

like so

+

+    @train
+    def train_batch(self, batch):
+        x, y = batch
+        _y = self.forward(x)
+        loss = self.loss_fn(_y, y.long())
+        return {"loss": loss}
+
+    @validate
+    def validate_batch(self, batch=None, dl=None):
+        if dl is not None:
+            output = []
+            for batch in dl:
+                output.extend(self.predict(batch=batch)["val_acc"])
+            return np.mean(output)
+        x, y = batch
+        _y = self.forward(x)
+        loss = self.loss_fn(_y, y.long())
+        acc = (y == _y.max(-1)[1]).float().mean()
+        return {"val_loss": loss, "val_acc": acc}
+
+    @predict
+    def predict(self, batch=None, dl=None):
+        if dl is not None:
+            output = []
+            for batch in dl:
+                output.extend(self.predict(batch=batch))
+            return output
+        x, y = batch
+        _y = self.forward(x)
+        o = _y.max(-1)[1].cpu().detach().numpy().tolist()
+        return o
+ +

We could now create the model…

+
+
model = IrisModel()
+model.device = "cpu"
+
+

… and run model.fit with an optional number of logs to print to the console

+
+
model.fit(trn_dl, val_dl, num_epochs=100, print_total=2, device="cpu")
+
+
EPOCH: 1.000  val_acc: 0.281  loss: 1.118  val_loss: 1.159  (0.05s - 5.02s remaining)
+EPOCH: 50.000  val_acc: 1.000  loss: 0.490  val_loss: 0.406  (0.18s - 0.18s remaining)
+EPOCH: 100.000  val_acc: 1.000  loss: 0.238  val_loss: 0.162  (0.31s - 0.00s remaining)
+
+
+
+
+

+
+
+
+
+

model.evaluate accepts a validation data loader that will repeatedly call validate_batch and return aggregated metrics

+
+
model.evaluate(val_dl, device="cpu")
+
+
EPOCH: 1.000  val_acc: 1.000  val_loss: 0.164  (0.00s - 0.00s remaining)
+
+
+
{'epoch_val_acc': np.float64(1.0),
+ 'epoch_val_loss': np.float64(0.16414348781108856)}
+
+
+ + + +
+ +
+ + + + + \ No newline at end of file diff --git a/_proc/docs/capsule_files/figure-html/cell-6-output-2.png b/_proc/docs/capsule_files/figure-html/cell-6-output-2.png new file mode 100644 index 0000000..df661f0 Binary files /dev/null and b/_proc/docs/capsule_files/figure-html/cell-6-output-2.png differ diff --git a/_proc/docs/charts.html b/_proc/docs/charts.html new file mode 100644 index 0000000..4cabdc0 --- /dev/null +++ b/_proc/docs/charts.html @@ -0,0 +1,1798 @@ + + + + + + + + + +charts – torch_snippets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ + + + +
+

Altair and Other Charts

+ +
+
from torch_snippets.loader import *
+from sklearn.datasets import make_moons
+
+np.random.seed(10)
+x, y = make_moons(1000, noise=0.1)
+df = pd.DataFrame({"x1": x[:, 0], "x2": x[:, 1], "y": y})
+
+Chart(df).mark_circle().encode(x="x1:Q", y="x2:Q", color="y:N").interactive()
+
+ + +
+ +
+
+

Refer to altair-viz.github.io for more awesome charts.
+torch-snippets exposes a confusion matrix function CM as an example

+
+

Method 1

+
+
n = 10
+a = "qwertyuiopasdfghjklzxcvbnm"
+truth = np.random.randint(4, size=1000000)
+pred = np.random.randint(4, size=1000000)
+show(CM(truth=truth, pred=pred, mapping={i: a for i, a in enumerate(a)}))
+# mapping is optional
+
+
              precision    recall  f1-score   support
+
+           0       0.25      0.25      0.25    250150
+           1       0.25      0.25      0.25    250245
+           2       0.25      0.25      0.25    249836
+           3       0.25      0.25      0.25    249769
+
+    accuracy                           0.25   1000000
+   macro avg       0.25      0.25      0.25   1000000
+weighted avg       0.25      0.25      0.25   1000000
+
+
+
+ + +
+ +
+
+
+
+

Method 2

+
+
df = pd.DataFrame(
+    {
+        "truth": [randint(n) for _ in range(1000)],
+        "pred": [randint(n) for _ in range(1000)],
+    }
+)
+show(CM(df, "truth", "pred", mapping={i: a for i, a in enumerate(a)}))
+# mapping is optional
+
+
              precision    recall  f1-score   support
+
+           0       0.13      0.14      0.13        92
+           1       0.08      0.09      0.08       101
+           2       0.13      0.12      0.13       107
+           3       0.06      0.06      0.06       105
+           4       0.12      0.11      0.11        94
+           5       0.12      0.09      0.10       115
+           6       0.08      0.10      0.09        88
+           7       0.08      0.07      0.08       113
+           8       0.09      0.09      0.09        99
+           9       0.12      0.15      0.13        86
+
+    accuracy                           0.10      1000
+   macro avg       0.10      0.10      0.10      1000
+weighted avg       0.10      0.10      0.10      1000
+
+
+
+ + +
+ +
+
+
+
+

Method 3

+
+
df = pd.DataFrame(
+    {
+        "truth": [choose("abcd") for _ in range(1000)],
+        "pred": [choose("abcd") for _ in range(1000)],
+    }
+)
+show(CM(df, "truth", "pred"))
+# mapping is optional
+
+
              precision    recall  f1-score   support
+
+           a       0.25      0.29      0.27       229
+           b       0.28      0.29      0.28       256
+           c       0.27      0.24      0.26       267
+           d       0.26      0.25      0.25       248
+
+    accuracy                           0.27      1000
+   macro avg       0.26      0.27      0.26      1000
+weighted avg       0.27      0.27      0.26      1000
+
+
+
+ + +
+ +
+
+
+
+
+

spider

+
+
 spider (df, id_column=None, title=None, max_values=None, padding=1.25,
+         global_scale=False, ax=None, sz=10)
+
+

*Plot a spider chart based on the given dataframe.

+

Parameters: - df: pandas DataFrame The input dataframe containing the data to be plotted. - id_column: str, optional The column name to be used as the identifier for each data point. If not provided, the index of the dataframe will be used. - title: str, optional The title of the spider chart. - max_values: dict, optional A dictionary specifying the maximum values for each category. If not provided, the maximum values will be calculated based on the data. - padding: float, optional The padding factor to be applied when calculating the maximum values. Default is 1.25. - global_scale: bool or float, optional If False, each category will have its own maximum value. If True, a single maximum value will be used for all categories. If a float value is provided, it will be used as the maximum value for all categories. - ax: matplotlib Axes, optional The axes on which to plot the spider chart. If not provided, a new figure and axes will be created. - sz: float, optional The size of the figure (both width and height) in inches. Default is 10.

+

Returns: - None

+

Example usage: spider(df, id_column=‘model’, title=‘Spider Chart’, max_values={‘category1’: 10, ‘category2’: 20}, padding=1.5)*

+
+
import pandas as pd
+
+spider(
+    pd.DataFrame(
+        {
+            "x": [*"abcde"],
+            "c1": [10, 11, 12, 13, 14],
+            "c2": [0.1, 0.3, 0.4, 0.1, 0.9],
+            "c3": [1e5, 2e5, 3.5e5, 8e4, 5e4],
+            "c4": [9, 12, 5, 2, 0.2],
+            "test": [1, 1, 1, 1, 5],
+        },
+        index=[*"abcde"],
+    ),
+    title="Sample Spider",
+    padding=1.1,
+)
+
+
+
+

+
+
+
+
+
+
+
+

UpSetAltair

+
+
 UpSetAltair (data=None, title='', subtitle='', sets=None, abbre=None,
+              sort_by='frequency', sort_order='ascending', width=1200,
+              height=700, height_ratio=0.6,
+              horizontal_bar_chart_width=300, color_range=['#55A8DB',
+              '#3070B5', '#30363F', '#F1AD60', '#DF6234', '#BDC6CA'],
+              highlight_color='#EA4667', glyph_size=200,
+              set_label_bg_size=1000, line_connection_size=2,
+              horizontal_bar_size=20, vertical_bar_label_size=16,
+              vertical_bar_padding=20)
+
+

*This function generates Altair-based interactive UpSet plots.

+

Parameters: - data (pandas.DataFrame): Tabular data containing the membership of each element (row) in exclusive intersecting sets (column). - sets (list): List of set names of interest to show in the UpSet plots. This list reflects the order of sets to be shown in the plots as well. - abbre (list): Abbreviated set names. - sort_by (str): “frequency” or “degree” - sort_order (str): “ascending” or “descending” - width (int): Vertical size of the UpSet plot. - height (int): Horizontal size of the UpSet plot. - height_ratio (float): Ratio of height between upper and under views, ranges from 0 to 1. - horizontal_bar_chart_width (int): Width of horizontal bar chart on the bottom-right. - color_range (list): Color to encode sets. - highlight_color (str): Color to encode intersecting sets upon mouse hover. - glyph_size (int): Size of UpSet glyph (⬤). - set_label_bg_size (int): Size of label background in the horizontal bar chart. - line_connection_size (int): width of lines in matrix view. - horizontal_bar_size (int): Height of bars in the horizontal bar chart. - vertical_bar_label_size (int): Font size of texts in the vertical bar chart on the top. - vertical_bar_padding (int): Gap between a pair of bars in the vertical bar charts.*

+
+
+
+

upsetaltair_top_level_configuration

+
+
 upsetaltair_top_level_configuration (base, legend_orient='top-left',
+                                      legend_symbol_size=30)
+
+

*Configure the top-level settings for an UpSet plot in Altair.

+

Parameters: - base: The base chart to configure. - legend_orient: The orientation of the legend. Default is “top-left”. - legend_symbol_size: The size of the legend symbols. Default is 30.

+

Returns: - The configured chart.*

+
+
df
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
truthpred
0cd
1cc
2dd
3ca
4dc
.........
995cc
996ac
997ba
998bc
999ad
+ +

1000 rows × 2 columns

+
+
+
+
+
# import numpy as np
+
+# i = np.random.randn(300, 7) > 0.33
+# df = pd.DataFrame(i.astype(int))
+# df.columns = [rand() for _ in range(len(df.columns))]
+# show(df)
+
+# UpSetAltair(
+#     df,
+#     sets=list(df.columns),
+#     abbre=list(df.columns),
+#     sort_by="frequencey",
+#     sort_order="ascending",
+# )
+
+

df

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
4wST3lv6Vv72WKSGX4LBlidvg0LDKaxpK2f5pW4oKO
00101001
11001010
21010010
31000001
40010101
50001000
61001101
71111011
81001011
91001000
101000001
111011000
120101001
131001011
141101000
........................
2851000000
2861001111
2870011010
2880111000
2890111000
2900111100
2910010100
2920011001
2931000010
2940000001
2950111000
2960001010
2971010001
2980110100
2990010000
+ +
+
+
/opt/miniconda3/lib/python3.12/site-packages/altair/utils/deprecation.py:65: AltairDeprecationWarning: 'selection_multi' is deprecated.  Use 'selection_point'
+  warnings.warn(message, AltairDeprecationWarning, stacklevel=1)
+/opt/miniconda3/lib/python3.12/site-packages/altair/utils/deprecation.py:65: AltairDeprecationWarning: 'selection_single' is deprecated.  Use 'selection_point'
+  warnings.warn(message, AltairDeprecationWarning, stacklevel=1)
+
+
+
+
---------------------------------------------------------------------------
+SchemaValidationError                     Traceback (most recent call last)
+Cell In[14], line 8
+      5 df.columns = [rand() for _ in range(len(df.columns))]
+      6 show(df)
+----> 8 UpSetAltair(
+      9     df,
+     10     sets=list(df.columns),
+     11     abbre=list(df.columns),
+     12     sort_by="frequencey",
+     13     sort_order="ascending",
+     14 )
+
+Cell In[10], line 239, in UpSetAltair(data, title, subtitle, sets, abbre, sort_by, sort_order, width, height, height_ratio, horizontal_bar_chart_width, color_range, highlight_color, glyph_size, set_label_bg_size, line_connection_size, horizontal_bar_size, vertical_bar_label_size, vertical_bar_padding)
+    177 base = (
+    178     alt.Chart(data)
+    179     .transform_filter(legend_selection)
+   (...)
+    232     )
+    233 )
+    234 # Now, we have data in the following format:
+    235 # count, set, is_intersect, degree, intersection_id, set_abbre
+    236 
+    237 # Cardinality by intersecting sets (vertical bar chart)
+    238 vertical_bar = (
+--> 239     base.mark_bar(color=main_color, size=vertical_bar_size)
+    240     .encode(
+    241         x=alt.X(
+    242             "intersection_id:N",
+    243             axis=alt.Axis(grid=False, labels=False, ticks=False, domain=True),
+    244             sort=x_sort,
+    245             title=None,
+    246         ),
+    247         y=alt.Y(
+    248             "max(count):Q",
+    249             axis=alt.Axis(grid=False, tickCount=3, orient="right"),
+    250             title="Intersection Size",
+    251         ),
+    252         color=brush_color,
+    253         tooltip=tooltip,
+    254     )
+    255     .properties(width=matrix_width, height=vertical_bar_chart_height)
+    256 )
+    258 vertical_bar_text = vertical_bar.mark_text(
+    259     color=main_color, dy=-10, size=vertical_bar_label_size
+    260 ).encode(text=alt.Text("count:Q", format=".0f"))
+    262 vertical_bar_chart = (vertical_bar + vertical_bar_text).add_selection(
+    263     color_selection
+    264 )
+
+File /opt/miniconda3/lib/python3.12/site-packages/altair/vegalite/v5/schema/mixins.py:2786, in MarkMethodMixin.mark_bar(self, align, angle, aria, ariaRole, ariaRoleDescription, aspect, bandSize, baseline, binSpacing, blend, clip, color, continuousBandSize, cornerRadius, cornerRadiusBottomLeft, cornerRadiusBottomRight, cornerRadiusEnd, cornerRadiusTopLeft, cornerRadiusTopRight, cursor, description, dir, discreteBandSize, dx, dy, ellipsis, fill, fillOpacity, filled, font, fontSize, fontStyle, fontWeight, height, href, innerRadius, interpolate, invalid, limit, line, lineBreak, lineHeight, minBandSize, opacity, order, orient, outerRadius, padAngle, point, radius, radius2, radius2Offset, radiusOffset, shape, size, smooth, stroke, strokeCap, strokeDash, strokeDashOffset, strokeJoin, strokeMiterLimit, strokeOffset, strokeOpacity, strokeWidth, style, tension, text, theta, theta2, theta2Offset, thetaOffset, thickness, timeUnitBandPosition, timeUnitBandSize, tooltip, url, width, x, x2, x2Offset, xOffset, y, y2, y2Offset, yOffset, **kwds)
+   2784 copy = self.copy(deep=False)  # type: ignore[attr-defined]
+   2785 if any(val is not Undefined for val in kwds.values()):
+-> 2786     copy.mark = core.MarkDef(type="bar", **kwds)
+   2787 else:
+   2788     copy.mark = "bar"
+
+File /opt/miniconda3/lib/python3.12/site-packages/altair/vegalite/v5/schema/core.py:23720, in MarkDef.__init__(self, type, align, angle, aria, ariaRole, ariaRoleDescription, aspect, bandSize, baseline, binSpacing, blend, clip, color, continuousBandSize, cornerRadius, cornerRadiusBottomLeft, cornerRadiusBottomRight, cornerRadiusEnd, cornerRadiusTopLeft, cornerRadiusTopRight, cursor, description, dir, discreteBandSize, dx, dy, ellipsis, fill, fillOpacity, filled, font, fontSize, fontStyle, fontWeight, height, href, innerRadius, interpolate, invalid, limit, line, lineBreak, lineHeight, minBandSize, opacity, order, orient, outerRadius, padAngle, point, radius, radius2, radius2Offset, radiusOffset, shape, size, smooth, stroke, strokeCap, strokeDash, strokeDashOffset, strokeJoin, strokeMiterLimit, strokeOffset, strokeOpacity, strokeWidth, style, tension, text, theta, theta2, theta2Offset, thetaOffset, thickness, timeUnitBandPosition, timeUnitBandSize, tooltip, url, width, x, x2, x2Offset, xOffset, y, y2, y2Offset, yOffset, **kwds)
+  22901 def __init__(
+  22902     self,
+  22903     type: Union[
+   (...)
+  23718     **kwds,
+  23719 ):
+> 23720     super(MarkDef, self).__init__(
+  23721         type=type,
+  23722         align=align,
+  23723         angle=angle,
+  23724         aria=aria,
+  23725         ariaRole=ariaRole,
+  23726         ariaRoleDescription=ariaRoleDescription,
+  23727         aspect=aspect,
+  23728         bandSize=bandSize,
+  23729         baseline=baseline,
+  23730         binSpacing=binSpacing,
+  23731         blend=blend,
+  23732         clip=clip,
+  23733         color=color,
+  23734         continuousBandSize=continuousBandSize,
+  23735         cornerRadius=cornerRadius,
+  23736         cornerRadiusBottomLeft=cornerRadiusBottomLeft,
+  23737         cornerRadiusBottomRight=cornerRadiusBottomRight,
+  23738         cornerRadiusEnd=cornerRadiusEnd,
+  23739         cornerRadiusTopLeft=cornerRadiusTopLeft,
+  23740         cornerRadiusTopRight=cornerRadiusTopRight,
+  23741         cursor=cursor,
+  23742         description=description,
+  23743         dir=dir,
+  23744         discreteBandSize=discreteBandSize,
+  23745         dx=dx,
+  23746         dy=dy,
+  23747         ellipsis=ellipsis,
+  23748         fill=fill,
+  23749         fillOpacity=fillOpacity,
+  23750         filled=filled,
+  23751         font=font,
+  23752         fontSize=fontSize,
+  23753         fontStyle=fontStyle,
+  23754         fontWeight=fontWeight,
+  23755         height=height,
+  23756         href=href,
+  23757         innerRadius=innerRadius,
+  23758         interpolate=interpolate,
+  23759         invalid=invalid,
+  23760         limit=limit,
+  23761         line=line,
+  23762         lineBreak=lineBreak,
+  23763         lineHeight=lineHeight,
+  23764         minBandSize=minBandSize,
+  23765         opacity=opacity,
+  23766         order=order,
+  23767         orient=orient,
+  23768         outerRadius=outerRadius,
+  23769         padAngle=padAngle,
+  23770         point=point,
+  23771         radius=radius,
+  23772         radius2=radius2,
+  23773         radius2Offset=radius2Offset,
+  23774         radiusOffset=radiusOffset,
+  23775         shape=shape,
+  23776         size=size,
+  23777         smooth=smooth,
+  23778         stroke=stroke,
+  23779         strokeCap=strokeCap,
+  23780         strokeDash=strokeDash,
+  23781         strokeDashOffset=strokeDashOffset,
+  23782         strokeJoin=strokeJoin,
+  23783         strokeMiterLimit=strokeMiterLimit,
+  23784         strokeOffset=strokeOffset,
+  23785         strokeOpacity=strokeOpacity,
+  23786         strokeWidth=strokeWidth,
+  23787         style=style,
+  23788         tension=tension,
+  23789         text=text,
+  23790         theta=theta,
+  23791         theta2=theta2,
+  23792         theta2Offset=theta2Offset,
+  23793         thetaOffset=thetaOffset,
+  23794         thickness=thickness,
+  23795         timeUnitBandPosition=timeUnitBandPosition,
+  23796         timeUnitBandSize=timeUnitBandSize,
+  23797         tooltip=tooltip,
+  23798         url=url,
+  23799         width=width,
+  23800         x=x,
+  23801         x2=x2,
+  23802         x2Offset=x2Offset,
+  23803         xOffset=xOffset,
+  23804         y=y,
+  23805         y2=y2,
+  23806         y2Offset=y2Offset,
+  23807         yOffset=yOffset,
+  23808         **kwds,
+  23809     )
+
+File /opt/miniconda3/lib/python3.12/site-packages/altair/vegalite/v5/schema/core.py:149, in AnyMark.__init__(self, *args, **kwds)
+    148 def __init__(self, *args, **kwds):
+--> 149     super(AnyMark, self).__init__(*args, **kwds)
+
+File /opt/miniconda3/lib/python3.12/site-packages/altair/utils/schemapi.py:771, in SchemaBase.__init__(self, *args, **kwds)
+    768 object.__setattr__(self, "_kwds", kwds)
+    770 if DEBUG_MODE and self._class_is_valid_at_instantiation:
+--> 771     self.to_dict(validate=True)
+
+File /opt/miniconda3/lib/python3.12/site-packages/altair/utils/schemapi.py:978, in SchemaBase.to_dict(self, validate, ignore, context)
+    971         self.validate(result)
+    972     except jsonschema.ValidationError as err:
+    973         # We do not raise `from err` as else the resulting
+    974         # traceback is very long as it contains part
+    975         # of the Vega-Lite schema. It would also first
+    976         # show the less helpful ValidationError instead of
+    977         # the more user friendly SchemaValidationError
+--> 978         raise SchemaValidationError(self, err) from None
+    979 return result
+
+SchemaValidationError: '-7.628865979381443' is an invalid value for `size`.
+
+-7.628865979381443 is less than the minimum of 0
+
+
+
+
+
+
+
ERROR:root:No traceback has been produced, nothing to debug.
+
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/_proc/docs/charts_files/figure-html/cell-7-output-1.png b/_proc/docs/charts_files/figure-html/cell-7-output-1.png new file mode 100644 index 0000000..8a23c5a Binary files /dev/null and b/_proc/docs/charts_files/figure-html/cell-7-output-1.png differ diff --git a/_proc/docs/config.html b/_proc/docs/config.html new file mode 100644 index 0000000..6d05e9d --- /dev/null +++ b/_proc/docs/config.html @@ -0,0 +1,922 @@ + + + + + + + + + +config – torch_snippets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ + + + + +
+
+

DeepLearningConfig

+
+
 DeepLearningConfig ()
+
+

*A configuration class for deep learning models.

+

This class provides methods to access and manipulate configuration settings.

+

Attributes: input_variables (list): List of input variables defined in the class constructor.

+

Methods: keys(): Returns the list of input variables. getitem(key): Returns the value of the specified key. contains(key): Checks if the specified key is present in the input variables. from_ini_file(filepath, config_root=None): Creates an instance of the class from an INI file. repr(): Returns a string representation of the class.

+

Example usage: config = DeepLearningConfig() config.from_ini_file(‘config.ini’) print(config.keys()) print(config[‘learning_rate’])*

+
+
from torch_snippets.registry import parse_string
+from torch_snippets.torch_loader import *
+from torch_snippets import writelines
+
+
+
config_str = """
+[META]
+experiment = mnist.v1
+description = Training MLP with 
+    mnist data on 10k images only
+    using huggingface trainer and 
+    cosine annealing
+
+[ModelConfig]
+n_layers = 3
+n_hidden = 256
+n_classes = 10
+
+[DatasetConfig]
+root = /home/datasets/mnist
+train = ${root}/train
+val = ${root}/val
+train_subset = 10000
+val_subest = ${train_subset}//10
+
+[TrainingConfig]
+max_steps = ${DatasetConfig.train_subset} * 5
+learning_rate = 3e-4
+output_dir = ./results/${META.experiment}
+per_device_train_batch_size = 256
+per_device_eval_batch_size = ${per_device_train_batch_size}
+evaluation_strategy = "steps"
+eval_steps = 500
+logging_strategy = ${evaluation_strategy}
+logging_steps = ${eval_steps}//100
+save_strategy = ${evaluation_strategy}
+save_steps = ${eval_steps}
+save_total_limit = 1
+seed = 1234
+label_names = ['targets']
+lr_scheduler_type = cosine
+""".strip()
+
+config = parse_string(config_str)
+
+
+class MNIST(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.model = nn.Sequential(
+            nn.Linear(768, config.n_hidden),
+            *[
+                nn.Sequential(nn.Linear(config.n_hidden, config.n_hidden), nn.ReLU())
+                for _ in range(config.n_layers - 1)
+            ],
+            nn.Linear(config.n_hidden, config.n_classes)
+        )
+
+    def forward(self, images): ...
+
+
+model = MNIST(config.ModelConfig)
+print(model)
+
+
MNIST(
+  (model): Sequential(
+    (0): Linear(in_features=768, out_features=256, bias=True)
+    (1): Sequential(
+      (0): Linear(in_features=256, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (2): Sequential(
+      (0): Linear(in_features=256, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (3): Linear(in_features=256, out_features=10, bias=True)
+  )
+)
+
+
+

If needed, configs can be unpacked like a dictionary too

+
+
class MNIST(nn.Module):
+    """
+    A PyTorch module for a multi-layer perceptron (MLP) model for MNIST classification.
+
+    Args:
+        n_hidden (int): The number of hidden units in each hidden layer.
+        n_classes (int): The number of output classes.
+        n_layers (int): The number of hidden layers in the model.
+
+    Attributes:
+        model (nn.Sequential): The sequential model that represents the MLP.
+
+    """
+
+    def __init__(self, *, n_hidden, n_classes, n_layers):
+        super().__init__()
+        self.model = nn.Sequential(
+            nn.Linear(768, n_hidden),
+            *[
+                nn.Sequential(nn.Linear(n_hidden, n_hidden), nn.ReLU())
+                for _ in range(n_layers - 1)
+            ],
+            nn.Linear(n_hidden, n_classes)
+        )
+
+    def forward(self, images): ...
+
+
+model = MNIST(**config.ModelConfig)
+print(model)
+
+
MNIST(
+  (model): Sequential(
+    (0): Linear(in_features=768, out_features=256, bias=True)
+    (1): Sequential(
+      (0): Linear(in_features=256, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (2): Sequential(
+      (0): Linear(in_features=256, out_features=256, bias=True)
+      (1): ReLU()
+    )
+    (3): Linear(in_features=256, out_features=10, bias=True)
+  )
+)
+
+
+
+
+
+

GenericConfig

+
+
 GenericConfig (**kwargs)
+
+

*A configuration class for deep learning models.

+

This class provides methods to access and manipulate configuration settings.

+

Attributes: input_variables (list): List of input variables defined in the class constructor.

+

Methods: keys(): Returns the list of input variables. getitem(key): Returns the value of the specified key. contains(key): Checks if the specified key is present in the input variables. from_ini_file(filepath, config_root=None): Creates an instance of the class from an INI file. repr(): Returns a string representation of the class.

+

Example usage: config = DeepLearningConfig() config.from_ini_file(‘config.ini’) print(config.keys()) print(config[‘learning_rate’])*

+

GenericConfig is a special class that can have attributes solely based on the config file, i.e., when we are unsure what are the arguments in the config going to be

+
+
writelines(config_str.split("\n"), "/tmp/tmp.ini", "w")
+training_config = GenericConfig.from_ini_file(
+    "/tmp/tmp.ini", config_root="TrainingConfig"
+)
+
+
+def train(**kwargs):
+    for k, v in kwargs.items():
+        print(k, v)
+
+
+train(**training_config)
+
+
max_steps 50000
+learning_rate 0.00030000000000000003
+output_dir ./results/mnist.v1
+per_device_train_batch_size 256
+per_device_eval_batch_size 256
+evaluation_strategy steps
+eval_steps 500
+logging_strategy steps
+logging_steps 5
+save_strategy steps
+save_steps 500
+save_total_limit 1
+seed 1234
+label_names ['targets']
+lr_scheduler_type cosine
+
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/_proc/docs/decorators.html b/_proc/docs/decorators.html new file mode 100644 index 0000000..4cfcb8f --- /dev/null +++ b/_proc/docs/decorators.html @@ -0,0 +1,874 @@ + + + + + + + + + +Decorator Utilites – torch_snippets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Decorator Utilites

+
+ + + +
+ + + + +
+ + + +
+ + + +
+
+

check_kwargs_not_none

+
+
 check_kwargs_not_none (func)
+
+

*A decorator that checks if any keyword argument is None. Raises a ValueError if any argument is None.

+

Args: func: The function to be decorated.

+

Returns: The decorated function.

+

Raises: ValueError: If any keyword argument is None.*

+
+
+
+

io

+
+
 io (func)
+
+

*A decorator that inspects the inputs and outputs of a function.

+

Args: func: The function to be decorated.

+

Returns: The decorated function.*

+
+
+
+

timeit

+
+
 timeit (func)
+
+

*A decorator that measures the execution time of a function.

+

Args: func (callable): The function to be timed.

+

Returns: callable: The wrapped function.

+

Example: @timeit def my_function(): # code to be timed pass

+
my_function()  # prints the execution time of my_function*
+
+
+
+

warn_on_fail

+
+
 warn_on_fail (func)
+
+
+
+
+

format

+
+
 format (input)
+
+
+
@timeit
+@io
+def foo(a, b):
+    """
+    This function takes two arguments, `a` and `b`, and returns their sum.
+
+    Parameters:
+    a (int): The first number.
+    b (int): The second number.
+
+    Returns:
+    int: The sum of `a` and `b`.
+    """
+    import time
+
+    time.sleep(1)
+    return a + b
+
+
+foo(10, 11)
+
+
══════════════════════════════════════════════════════════════════
+INPUTS:ARGS:
+tuple of 2 items
+    int: 10
+    int: 11
+══════════════════════════════════════════════════════════════════
+══════════════════════════════════════════════════════════════════
+OUTPUTS:
+int: 21
+══════════════════════════════════════════════════════════════════
+[08/24/24 12:50:36] INFO     foo took 1.00 seconds to execute                                                                      d=152002;file://<ipython-input-1-6ac2073623b5>:47\<ipython-input-1-6ac2073623b5>;;\:d=283174;file://<ipython-input-1-6ac2073623b5>:47#wrapper:47\wrapper:47;;\
+
+
+
21
+
+
+
+
@check_kwargs_not_none
+@io
+def foo(*, a=None, b=None):
+    return a + b
+
+
+foo(a=None, b=10)
+
+
+
---------------------------------------------------------------------------
+ValueError                                Traceback (most recent call last)
+Cell In[34], line 6
+      1 @check_kwargs_not_none
+      2 @io
+      3 def foo(*, a=None, b=None):
+      4     return a + b
+----> 6 foo(a=None, b=10)
+
+Cell In[32], line 32, in check_kwargs_not_none.<locals>.wrapper(*args, **kwargs)
+     30 for key, value in kwargs.items():
+     31     if value is None:
+---> 32         raise ValueError(f"Input argument '{key}' cannot be None")
+     33 return func(*args, **kwargs)
+
+ValueError: Input argument 'a' cannot be None
+
+
+
+
+
import nbdev
+
+nbdev.nbdev_export()
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/_proc/docs/imgaug_loader.html b/_proc/docs/imgaug_loader.html new file mode 100644 index 0000000..a0680bd --- /dev/null +++ b/_proc/docs/imgaug_loader.html @@ -0,0 +1,1355 @@ + + + + + + + + + +imgaug_loader – torch_snippets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ + + + +
+

Imgaug

+

wrappers for common imgaug functions, that accept image and ocr data frame of bounding boxes

+ +
+
from torch_snippets.loader import read, pd, show
+
+IM = read("/Users/yeshwanth.y/code/torch_snippets/assets/Preamble.png")
+DF = pd.read_csv("/Users/yeshwanth.y/code/torch_snippets/assets/Preamble.csv")
+DF = to_relative(DF, *IM.shape[:2])
+DF.head()
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
xyXYtextblock_id
00.2495380.2535010.5693160.305322ConstITUtIO0
10.2883550.3697480.4011090.397759NLTHE1
20.4029570.3697480.5101660.397759PEOPLE1
30.4935300.3711480.5452870.394958OF1
40.5489830.3697480.6303140.397759INDIA,1
+ +
+
+
+
+

Rescale

+
+
im = IM.copy()
+im = rescale(im, sz=(400, 600))
+show(im, sz=5)
+
+
+
+

+
+
+
+
+
+
df = DF.copy()
+im = IM.copy()
+im, df = rescale(im, df, (400, 600))
+show(im, df=df, sz=5)
+df.head()
+
+
+
---------------------------------------------------------------------------
+ValueError                                Traceback (most recent call last)
+/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb Cell 8 in <cell line: 3>()
+      <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=0'>1</a> df = DF.copy()
+      <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=1'>2</a> im = IM.copy()
+----> <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=2'>3</a> im, df = rescale(im, df, (400, 600))
+      <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=3'>4</a> show(im, df=df, sz=5)
+      <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=4'>5</a> df.head()
+
+/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb Cell 8 in rescale(im, bbs, sz)
+    <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=110'>111</a> H, W = get_size(sz, h, w)
+    <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=111'>112</a> aug = iaa.Resize({"height": H, "width": W})
+--> <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=112'>113</a> im, bbs = do(im, bbs, aug)
+    <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=113'>114</a> if to_pil:
+    <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=114'>115</a>     im = PIL.Image.fromarray(im)
+
+/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb Cell 8 in do(img, bbs, aug, cval)
+     <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=41'>42</a>         __df = combine_xyXY_to_bb(__df)
+     <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=42'>43</a>     bbs = __df
+---> <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=43'>44</a> if bbs == []:
+     <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=44'>45</a>     return img, []
+     <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=45'>46</a> return img, bbs
+
+File ~/miniconda3/lib/python3.9/site-packages/pandas/core/ops/__init__.py:705, in _comp_method_FRAME.<locals>.f(self, other)
+    701 @Appender(f"Wrapper for comparison method {op_name}")
+    702 def f(self, other):
+    703     axis = 1  # only relevant for Series other case
+--> 705     self, other = _align_method_FRAME(self, other, axis, level=None, flex=False)
+    707     # See GH#4537 for discussion of scalar op behavior
+    708     new_data = dispatch_to_series(self, other, op, axis=axis)
+
+File ~/miniconda3/lib/python3.9/site-packages/pandas/core/ops/__init__.py:503, in _align_method_FRAME(left, right, axis, flex, level)
+    496         raise ValueError(
+    497             "Unable to coerce to Series/DataFrame, "
+    498             f"dimension must be <= 2: {right.shape}"
+    499         )
+    501 elif is_list_like(right) and not isinstance(right, (ABCSeries, ABCDataFrame)):
+    502     # GH17901
+--> 503     right = to_series(right)
+    505 if flex is not None and isinstance(right, ABCDataFrame):
+    506     if not left._indexed_same(right):
+
+File ~/miniconda3/lib/python3.9/site-packages/pandas/core/ops/__init__.py:465, in _align_method_FRAME.<locals>.to_series(right)
+    463 else:
+    464     if len(left.columns) != len(right):
+--> 465         raise ValueError(
+    466             msg.format(req_len=len(left.columns), given_len=len(right))
+    467         )
+    468     right = left._constructor_sliced(right, index=left.columns)
+    469 return right
+
+ValueError: Unable to coerce to Series, length must be 6: given 0
+
+
+
+
+
+

Rotate

+
+
for i in range(11):
+    angle = (i - 5) * 5
+    print(angle)
+    im = rotate(IM, angle=angle)
+    show(im, sz=1)
+
+
-25
+
+
+
+
+

+
+
+
+
+
-20
+
+
+
+
+

+
+
+
+
+
-15
+
+
+
+
+

+
+
+
+
+
-10
+
+
+
+
+

+
+
+
+
+
-5
+
+
+
+
+

+
+
+
+
+
0
+
+
+
+
+

+
+
+
+
+
5
+
+
+
+
+

+
+
+
+
+
10
+
+
+
+
+

+
+
+
+
+
15
+
+
+
+
+

+
+
+
+
+
20
+
+
+
+
+

+
+
+
+
+
25
+
+
+
+
+

+
+
+
+
+
+
df = DF.copy()
+im = IM.copy()
+im, df = rotate(im, df, 90)
+show(im, df=df, sz=20)
+print(type(im))
+df.head()
+
+
+
+

+
+
+
+
+
<class 'numpy.ndarray'>
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
textblock_idxyXY
0ConstITUtIO00.6932770.2495380.7450980.569316
1NLTHE10.6008400.2883550.6288520.401109
2PEOPLE10.6008400.4029570.6288520.510166
3OF10.6036410.4935300.6274510.545287
4INDIA,10.6008400.5489830.6288520.630314
+ +
+
+
+
+
+

Pad

+
+
df = DF.copy()
+im = IM.copy()
+im, df = pad(im, df, deltas=(90, 90), cval=0)
+show(im, df=df, sz=20)
+print(type(im))
+df.head()
+
+
+
+

+
+
+
+
+
<class 'numpy.ndarray'>
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
textblock_idxyXY
0ConstITUtIO00.3105360.3025210.5508320.343137
1NLTHE10.3401110.3949580.4251390.417367
2PEOPLE10.4269870.3949580.5064700.417367
3OF10.4935300.3963590.5323480.415966
4INDIA,10.5360440.3949580.5970430.417367
+ +
+
+
+
+
+

Augmentations as Monads

+

All functions will work with data frames that contain either of absolute/relative coordinates, and will preserve the image type (np.ndarray or PIL.Image.Image) too

+
+
df = DF.copy()
+im = IM.copy()
+height, width = im.shape
+show(df.head())
+im = PIL.Image.fromarray(im)
+im, df = rotate(im, df, 45, cval=127)
+im, df = pad(im, df, deltas=(200, 200), cval=0)
+im, df = rescale(im, df, sz=(300, 300))
+show(im, df=df, sz=20)
+print(type(im))
+df.head()
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
xyXYtextblock_id
00.2495380.2535010.5693160.305322ConstITUtIO0
10.2883550.3697480.4011090.397759NLTHE1
20.4029570.3697480.5101660.397759PEOPLE1
30.4935300.3711480.5452870.394958OF1
40.5489830.3697480.6303140.397759INDIA,1
+ +
+
+
+
+
+

+
+
+
+
+
<class 'PIL.Image.Image'>
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
textblock_idxyXY
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2PEOPLE10.5066670.4166670.5500000.460000
3OF10.5366670.4466670.5600000.470000
4INDIA,10.5500000.4600000.5866670.493333
+ +
+
+
+ + +
+
+ +
+ +
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+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Utilities for simple needs

+
+ + + +
+ + + + +
+ + + +
+ + + +
+
# Time it
+from torch_snippets import *
+
+
CPU times: user 263 ms, sys: 53.9 ms, total: 317 ms
+Wall time: 407 ms
+
+
+

Below we are trying to extract the __all__ list from all Python files of the torch_snippets directory.
+Through the code, you can already see some of the elements of torch-snippets in action.

+
+
import ast
+
+os.environ[
+    "AD_MAX_ITEMS"
+] = (  # os is already imported by torch_snippets, along with many other useful libraries
+    "1000"  # Set the maximum number of items to display in the AD object
+)
+
+
+@tryy  # This is a decorator that catches exceptions
+def extract_all_list(file_path):
+    file = readfile(file_path, silent=True)  # Read the file
+    tree = ast.parse(file, filename=file_path)
+
+    for node in tree.body:
+        if isinstance(node, ast.Assign):
+            for target in node.targets:
+                if isinstance(target, ast.Name) and target.id == "__all__":
+                    if isinstance(node.value, ast.List):
+                        all_list = [
+                            elt.value
+                            for elt in node.value.elts
+                            if isinstance(elt, ast.Constant)
+                        ]
+                        return all_list
+    return None
+
+
+def print_all_lists_in_directory(directory):
+    dir = P(directory)  # Create a pathlib.Path object
+    for f in dir.ls():  # Iterate over all files in the directory
+        if f.extn == "py" and f.stem not in [
+            "__init__",
+            "_nbdev",
+        ]:  # If it's a Python file and not __init__.py
+            all_list = extract_all_list(f)
+            if all_list is not None and len(all_list) > 0:
+                h2(f.stem)  # Print the name of the file as a heading in jupyter
+                print(
+                    AD({"items": all_list})
+                )  # AD is an intelligent dictionary that can display itself nicely
+
+
+
print(P().resolve())
+
+
/Users/yeshwanth/Code/Personal/torch_snippets/nbs
+
+
+
+
# Specify the directory containing the Python files
+directory_path = "../torch_snippets"
+print_all_lists_in_directory(directory_path)
+
+

misc

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - Timer (🏷️ str)
+  1 - track2 (🏷️ str)
+  2 - summarize_input (🏷️ str)
+  3 - timeit (🏷️ str)
+  4 - io (🏷️ str)
+  5 - tryy (🏷️ str)
+
+```
+
+
+
+

load_defaults

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - ifnone (🏷️ str)
+  1 - exists (🏷️ str)
+  2 - loadifexists (🏷️ str)
+
+```
+
+
+
+

text_utils

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - textify (🏷️ str)
+  1 - find_lines (🏷️ str)
+  2 - find_blocks (🏷️ str)
+  3 - find_substring (🏷️ str)
+  4 - get_line_data_from_word_data (🏷️ str)
+  5 - edit_distance_path (🏷️ str)
+  6 - group_blocks (🏷️ str)
+
+```
+
+
+
+

paths

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - valid_methods (🏷️ str)
+  1 - P (🏷️ str)
+  2 - ls (🏷️ str)
+  3 - print_folder_summary (🏷️ str)
+  4 - dill (🏷️ str)
+  5 - input_to_str (🏷️ str)
+  6 - output_to_path (🏷️ str)
+  7 - process_f (🏷️ str)
+  8 - get_fs (🏷️ str)
+  9 - P0 (🏷️ str)
+  10 - stem (🏷️ str)
+  11 - stems (🏷️ str)
+  12 - extn (🏷️ str)
+  13 - remove_file (🏷️ str)
+  14 - isdir (🏷️ str)
+  15 - makedir (🏷️ str)
+  16 - fname (🏷️ str)
+  17 - fname2 (🏷️ str)
+  18 - parent (🏷️ str)
+  19 - Glob (🏷️ str)
+  20 - find (🏷️ str)
+  21 - zip_files (🏷️ str)
+  22 - unzip_file (🏷️ str)
+  23 - list_zip (🏷️ str)
+  24 - md5 (🏷️ str)
+  25 - remove_duplicates (🏷️ str)
+  26 - common_items (🏷️ str)
+  27 - folder_summary (🏷️ str)
+  28 - readlines (🏷️ str)
+  29 - readfile (🏷️ str)
+  30 - writelines (🏷️ str)
+  31 - tree (🏷️ str)
+  32 - folder_structure_to_dict (🏷️ str)
+  33 - folder_structure_to_json (🏷️ str)
+  34 - rename_batch (🏷️ str)
+  35 - dumpdill (🏷️ str)
+  36 - loaddill (🏷️ str)
+
+```
+
+
+
+

charts

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - alt (🏷️ str)
+  1 - Chart (🏷️ str)
+  2 - CM (🏷️ str)
+  3 - radar (🏷️ str)
+  4 - confusion_matrix (🏷️ str)
+  5 - spider (🏷️ str)
+  6 - upsetaltair_top_level_configuration (🏷️ str)
+  7 - UpSetAltair (🏷️ str)
+
+```
+
+
+
+

pdf_loader

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - PDF (🏷️ str)
+
+```
+
+
+
+

interactive_show

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - COLORS (🏷️ str)
+  1 - to_networkx (🏷️ str)
+  2 - plot_image (🏷️ str)
+  3 - plot_graph (🏷️ str)
+  4 - tonp (🏷️ str)
+  5 - tolist (🏷️ str)
+  6 - convert_to_nx (🏷️ str)
+  7 - viz2 (🏷️ str)
+  8 - df2graph_nodes (🏷️ str)
+  9 - ishow (🏷️ str)
+
+```
+
+
+
+

registry

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - Config (🏷️ str)
+  1 - AttrDict (🏷️ str)
+  2 - registry (🏷️ str)
+  3 - tryeval (🏷️ str)
+  4 - parse_base (🏷️ str)
+  5 - parse (🏷️ str)
+  6 - parse_and_resolve (🏷️ str)
+  7 - parse_string (🏷️ str)
+
+```
+
+
+
+

markup2

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - AD (🏷️ str)
+  1 - Config (🏷️ str)
+  2 - isnamedtupleinstance (🏷️ str)
+  3 - unpack (🏷️ str)
+  4 - AttrDict (🏷️ str)
+  5 - pretty_json (🏷️ str)
+  6 - read_json (🏷️ str)
+  7 - write_json (🏷️ str)
+  8 - write_jsonl (🏷️ str)
+  9 - read_jsonl (🏷️ str)
+  10 - read_yaml (🏷️ str)
+  11 - write_yaml (🏷️ str)
+  12 - read_xml (🏷️ str)
+  13 - write_xml (🏷️ str)
+
+```
+
+
+
+

inspector

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - inspect (🏷️ str)
+
+```
+
+
+
+

torch_loader

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - torch (🏷️ str)
+  1 - th (🏷️ str)
+  2 - torchvision (🏷️ str)
+  3 - T (🏷️ str)
+  4 - transforms (🏷️ str)
+  5 - nn (🏷️ str)
+  6 - np (🏷️ str)
+  7 - F (🏷️ str)
+  8 - Dataset (🏷️ str)
+  9 - DataLoader (🏷️ str)
+  10 - optim (🏷️ str)
+  11 - Report (🏷️ str)
+  12 - Reshape (🏷️ str)
+  13 - Permute (🏷️ str)
+  14 - device (🏷️ str)
+  15 - save_torch_model_weights_from (🏷️ str)
+  16 - load_torch_model_weights_to (🏷️ str)
+  17 - detach (🏷️ str)
+  18 - cat_with_padding (🏷️ str)
+
+```
+
+
+
+

logger

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - console (🏷️ str)
+  1 - reset_logger_width (🏷️ str)
+  2 - logger (🏷️ str)
+  3 - Trace (🏷️ str)
+  4 - Debug (🏷️ str)
+  5 - Info (🏷️ str)
+  6 - Warn (🏷️ str)
+  7 - Excep (🏷️ str)
+  8 - warn_mode (🏷️ str)
+  9 - info_mode (🏷️ str)
+  10 - debug_mode (🏷️ str)
+  11 - trace_mode (🏷️ str)
+  12 - excep_mode (🏷️ str)
+  13 - in_warn_mode (🏷️ str)
+  14 - in_info_mode (🏷️ str)
+  15 - in_debug_mode (🏷️ str)
+  16 - in_trace_mode (🏷️ str)
+  17 - in_excep_mode (🏷️ str)
+  18 - frames (🏷️ str)
+  19 - get_console (🏷️ str)
+  20 - reset_logger (🏷️ str)
+  21 - get_logger_level (🏷️ str)
+  22 - logger_mode (🏷️ str)
+  23 - in_logger_mode (🏷️ str)
+  24 - notify_waiting (🏷️ str)
+
+```
+
+
+
+

markup

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - AttrDict (🏷️ str)
+  1 - Config (🏷️ str)
+  2 - isnamedtupleinstance (🏷️ str)
+  3 - unpack (🏷️ str)
+  4 - hash_tensor (🏷️ str)
+  5 - hash_pandas_dataframe (🏷️ str)
+  6 - AttrDictDeprecated (🏷️ str)
+  7 - decompose (🏷️ str)
+  8 - pretty_json (🏷️ str)
+  9 - read_json (🏷️ str)
+  10 - write_json (🏷️ str)
+  11 - write_jsonl (🏷️ str)
+  12 - read_jsonl (🏷️ str)
+  13 - read_yaml (🏷️ str)
+  14 - write_yaml (🏷️ str)
+  15 - read_xml (🏷️ str)
+  16 - write_xml (🏷️ str)
+
+```
+
+
+
+

sklegos

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - ColumnSelector (🏷️ str)
+  1 - GroupedPredictor (🏷️ str)
+  2 - EstimatorTransformer (🏷️ str)
+  3 - train_test_split (🏷️ str)
+  4 - MakeFrame (🏷️ str)
+  5 - ImputeMissingValues (🏷️ str)
+  6 - LambdaTransformer (🏷️ str)
+  7 - Cat2Num (🏷️ str)
+  8 - SplitDateColumn (🏷️ str)
+
+```
+
+
+
+

ipython

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - save_notebook (🏷️ str)
+  1 - backup_this_notebook (🏷️ str)
+  2 - backup_all_notebooks (🏷️ str)
+  3 - backup_folders_of_nbs (🏷️ str)
+  4 - display_dfs_side_by_side (🏷️ str)
+  5 - show_big_dataframe (🏷️ str)
+  6 - h1 (🏷️ str)
+  7 - h2 (🏷️ str)
+  8 - h3 (🏷️ str)
+  9 - h4 (🏷️ str)
+  10 - h5 (🏷️ str)
+  11 - h6 (🏷️ str)
+  12 - store_scrap (🏷️ str)
+  13 - shutdown_current_notebook (🏷️ str)
+
+```
+
+
+
+
../torch_snippets/loader.py:532: SyntaxWarning: invalid escape sequence '\$'
+  puttext(ax, text.replace("$", "\$"), tuple(bbs[ix][:2]), size=text_sz)
+
+
+

loader

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - B (🏷️ str)
+  1 - Blank (🏷️ str)
+  2 - batchify (🏷️ str)
+  3 - C (🏷️ str)
+  4 - choose (🏷️ str)
+  5 - common (🏷️ str)
+  6 - crop_from_bb (🏷️ str)
+  7 - diff (🏷️ str)
+  8 - E (🏷️ str)
+  9 - flatten (🏷️ str)
+  10 - Image (🏷️ str)
+  11 - jitter (🏷️ str)
+  12 - L (🏷️ str)
+  13 - lzip (🏷️ str)
+  14 - line (🏷️ str)
+  15 - lines (🏷️ str)
+  16 - to_absolute (🏷️ str)
+  17 - to_relative (🏷️ str)
+  18 - enlarge_bbs (🏷️ str)
+  19 - shrink_bbs (🏷️ str)
+  20 - logger (🏷️ str)
+  21 - np (🏷️ str)
+  22 - now (🏷️ str)
+  23 - nunique (🏷️ str)
+  24 - os (🏷️ str)
+  25 - pad (🏷️ str)
+  26 - pd (🏷️ str)
+  27 - pdfilter (🏷️ str)
+  28 - pdb (🏷️ str)
+  29 - PIL (🏷️ str)
+  30 - print (🏷️ str)
+  31 - puttext (🏷️ str)
+  32 - randint (🏷️ str)
+  33 - rand (🏷️ str)
+  34 - re (🏷️ str)
+  35 - read (🏷️ str)
+  36 - readPIL (🏷️ str)
+  37 - rect (🏷️ str)
+  38 - resize (🏷️ str)
+  39 - rotate (🏷️ str)
+  40 - see (🏷️ str)
+  41 - show (🏷️ str)
+  42 - store_attr (🏷️ str)
+  43 - subplots (🏷️ str)
+  44 - sys (🏷️ str)
+  45 - toss (🏷️ str)
+  46 - track (🏷️ str)
+  47 - tqdm (🏷️ str)
+  48 - Tqdm (🏷️ str)
+  49 - trange (🏷️ str)
+  50 - unique (🏷️ str)
+  51 - uint (🏷️ str)
+  52 - write (🏷️ str)
+  53 - BB (🏷️ str)
+  54 - bbfy (🏷️ str)
+  55 - xywh2xyXY (🏷️ str)
+  56 - df2bbs (🏷️ str)
+  57 - bbs2df (🏷️ str)
+  58 - Info (🏷️ str)
+  59 - Warn (🏷️ str)
+  60 - Debug (🏷️ str)
+  61 - Excep (🏷️ str)
+  62 - reset_logger (🏷️ str)
+  63 - get_logger_level (🏷️ str)
+  64 - in_debug_mode (🏷️ str)
+  65 - debug_mode (🏷️ str)
+  66 - typedispatch (🏷️ str)
+  67 - defaultdict (🏷️ str)
+  68 - Counter (🏷️ str)
+  69 - dcopy (🏷️ str)
+  70 - patch_to (🏷️ str)
+  71 - split (🏷️ str)
+  72 - train_test_split (🏷️ str)
+  73 - init_plt (🏷️ str)
+  74 - init_cv2 (🏷️ str)
+
+```
+
+
+
+

imgaug_loader

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - do (🏷️ str)
+  1 - bw (🏷️ str)
+  2 - rotate (🏷️ str)
+  3 - pad (🏷️ str)
+  4 - get_size (🏷️ str)
+  5 - rescale (🏷️ str)
+  6 - crop (🏷️ str)
+  7 - imgaugbbs2bbs (🏷️ str)
+  8 - bbs2imgaugbbs (🏷️ str)
+
+```
+
+
+
+

dates

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - make_uniform_date_format (🏷️ str)
+  1 - ALL_DATE_FORMATS (🏷️ str)
+  2 - are_dates_equal (🏷️ str)
+  3 - today (🏷️ str)
+
+```
+
+
+
+

bokeh_loader

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - parse_sz (🏷️ str)
+  1 - get_bplot (🏷️ str)
+
+```
+
+
+
+

bb_utils

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - randint (🏷️ str)
+  1 - BB (🏷️ str)
+  2 - df2bbs (🏷️ str)
+  3 - bbs2df (🏷️ str)
+  4 - bbfy (🏷️ str)
+  5 - jitter (🏷️ str)
+  6 - compute_eps (🏷️ str)
+  7 - enlarge_bbs (🏷️ str)
+  8 - shrink_bbs (🏷️ str)
+  9 - iou (🏷️ str)
+  10 - compute_distance_matrix (🏷️ str)
+  11 - compute_distances (🏷️ str)
+  12 - split_bb_to_xyXY (🏷️ str)
+  13 - combine_xyXY_to_bb (🏷️ str)
+  14 - is_absolute (🏷️ str)
+  15 - is_relative (🏷️ str)
+  16 - to_relative (🏷️ str)
+  17 - to_absolute (🏷️ str)
+  18 - merge_by_bb (🏷️ str)
+  19 - isin (🏷️ str)
+
+```
+
+
+
+

adapters

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - np_2_b64 (🏷️ str)
+  1 - b64_2_np (🏷️ str)
+  2 - b64_2_file (🏷️ str)
+  3 - bytes_2_file (🏷️ str)
+  4 - file_2_bytes (🏷️ str)
+  5 - csvs_2_cvat (🏷️ str)
+  6 - cvat_2_csvs (🏷️ str)
+  7 - df_2_yolo (🏷️ str)
+  8 - yolo_2_df (🏷️ str)
+
+```
+
+
+
+

decorators

+
+
+

+```↯ AttrDict ↯
+items[]
+  0 - format (🏷️ str)
+  1 - warn_on_fail (🏷️ str)
+  2 - timeit (🏷️ str)
+  3 - io (🏷️ str)
+  4 - check_kwargs_not_none (🏷️ str)
+
+```
+
+
+
+ + + +
+ +
+ + + + + \ No newline at end of file diff --git a/_proc/docs/inspector.html b/_proc/docs/inspector.html new file mode 100644 index 0000000..ad04295 --- /dev/null +++ b/_proc/docs/inspector.html @@ -0,0 +1,889 @@ + + + + + + + + + + +Inspect – torch_snippets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Inspect

+
+ +
+
+ View statistics of tensors and other python containers +
+
+ + +
+ + + + +
+ + + +
+ + + +
+
import torch, numpy as np
+from torch_snippets import inspect
+
+inspect(torch.randint(0, 100, size=(4, 3, 5)), np.random.randint(-10, 10, (9, 19, 1)))
+
+
══════════════════════════════════════════════════════════════════
+
+
+
+
Tensor  Shape: torch.Size([4, 3, 5])    Min: 2.000      Max: 97.000     Mean: 46.317    dtype: torch.int64 @ cpu
+
+
+
+
══════════════════════════════════════════════════════════════════
+
+
+
+
ndarray Shape: (9, 19, 1)       Min: -10.000    Max: 9.000      Mean: -0.345    dtype: int64
+
+
+
+
══════════════════════════════════════════════════════════════════
+
+
+
+
+
x = {
+    "a": [0, 1, 2, 3],
+    "b": torch.rand(10, 10),
+    "c": {
+        "d": np.arange(10),
+        "e": [
+            "np.arange",
+            {},
+            tuple(
+                [
+                    1,
+                    2,
+                ]
+            ),
+            set([1, 2, 3]),
+            [],
+            [11, 10],
+        ],
+    },
+}
+inspect(x)
+
+
══════════════════════════════════════════════════════════════════
+
+
+
+
dict of 3 items
+
+
+
+
        A:
+        list of 4 items
+
+
+
+
                int: 0
+
+
+
+
                int: 1
+
+
+
+
                int: 2
+
+
+
+
                int: 3
+
+
+
+
        B:
+        Tensor  Shape: torch.Size([10, 10])     Min: 0.000      Max: 0.989      Mean: 0.463     dtype: 
+torch.float32 @ cpu
+
+
+
+
        C:
+        dict of 2 items
+
+
+
+
                D:
+                ndarray Shape: (10,)    Min: 0.000      Max: 9.000      Mean: 4.500     dtype: int64
+
+
+
+
                E:
+                list of 6 items
+
+
+
+
                        str `np.arange`
+
+
+
+
                        dict of 0 items
+
+
+
+
                        tuple of 2 items
+
+
+
+
                                int: 1
+
+
+
+
                                int: 2
+
+
+
+
                        set Length: 3
+
+
+
+
                        list of 0 items
+
+
+
+
                        and ... ... 1 more item(s)
+
+
+
+
══════════════════════════════════════════════════════════════════
+
+
+
+ + + +
+ +
+ + + + + \ No newline at end of file diff --git a/_proc/docs/interactive_show.html b/_proc/docs/interactive_show.html new file mode 100644 index 0000000..d5fc663 --- /dev/null +++ b/_proc/docs/interactive_show.html @@ -0,0 +1,774 @@ + + + + + + + + + +interactive_show – torch_snippets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ + + + + +
+
+

ishow

+
+
 ishow (im, df, additional_attrs=None, **kwargs)
+
+
+
+
+

df2graph_nodes

+
+
 df2graph_nodes (df, text_attr='text', additional_attrs=None)
+
+
+
+
+

viz2

+
+
 viz2 (graph, node_attrs=None, undirected=True, **kwargs)
+
+
+
+
+

convert_to_nx

+
+
 convert_to_nx (g, node_attrs=None, undirected=True)
+
+
+
+
+

tolist

+
+
 tolist (i)
+
+
+
+
+

tonp

+
+
 tonp (i)
+
+
+
+
+

plot_graph

+
+
 plot_graph (g, output, im=None, **kwargs)
+
+
+
+
+

plot_image

+
+
 plot_image (p, image, sz)
+
+
+
+
+

to_networkx

+
+
 to_networkx (data, node_attrs:Optional[Iterable[str]]=None,
+              edge_attrs:Optional[Iterable[str]]=None,
+              graph_attrs:Optional[Iterable[str]]=None,
+              to_undirected:Union[bool,str,NoneType]=False,
+              remove_self_loops:bool=False)
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/_proc/docs/jupyter_notebook.html b/_proc/docs/jupyter_notebook.html new file mode 100644 index 0000000..7f07368 --- /dev/null +++ b/_proc/docs/jupyter_notebook.html @@ -0,0 +1,874 @@ + + + + + + + + + + +Jupyter Notebooks – torch_snippets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Jupyter Notebooks

+
+ +
+
+ Utilities specific to jupyter +
+
+ + +
+ + + + +
+ + + +
+ + + +
+
+

backup_folders_of_nbs

+
+
 backup_folders_of_nbs (src, dest)
+
+
+
+
+

backup_all_notebooks

+
+
 backup_all_notebooks (folder)
+
+
+
+
+

backup_this_notebook

+
+
 backup_this_notebook (this_file_path, save_html_to_dir=None,
+                       override_previous_backup=False, changelog=None,
+                       exclude_input=False, force_save_notebook=True)
+
+
+
+
+

save_notebook

+
+
 save_notebook (file_path)
+
+
+
backup_this_notebook("jupyter_notebook.ipynb")
+
+
+
+
+

show_big_dataframe

+
+
 show_big_dataframe (df, max_rows=30)
+
+
+
+
+

display_dfs_side_by_side

+
+
 display_dfs_side_by_side (*args, titles=<itertools.cycle object at
+                           0x11b22ea00>, max_rows=50)
+
+
+
+
+

h6

+
+
 h6 (text)
+
+
+
+
+

h5

+
+
 h5 (text)
+
+
+
+
+

h4

+
+
 h4 (text)
+
+
+
+
+

h3

+
+
 h3 (text)
+
+
+
+
+

h2

+
+
 h2 (text)
+
+
+
+
+

h1

+
+
 h1 (text)
+
+
+
+
+

store_scrap

+
+
 store_scrap (at)
+
+
+
+
+

shutdown_current_notebook

+
+
 shutdown_current_notebook (delay:int=None)
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/_proc/docs/load_defautls.html b/_proc/docs/load_defautls.html new file mode 100644 index 0000000..9abb8bb --- /dev/null +++ b/_proc/docs/load_defautls.html @@ -0,0 +1,725 @@ + + + + + + + + + +load_defautls – torch_snippets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ + + + +
+

File Exists

+

and what to do if it doesn’t

+ +
+
+

loadifexists

+
+
 loadifexists (fpath, default)
+
+

Load data from a dill file if it exists, else return default value

+
+
+
+

exists

+
+
 exists (fpath)
+
+

Alias for os.path.exists

+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/_proc/docs/logging.html b/_proc/docs/logging.html new file mode 100644 index 0000000..a811d32 --- /dev/null +++ b/_proc/docs/logging.html @@ -0,0 +1,1191 @@ + + + + + + + + + +Rich Logging and printing – torch_snippets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Rich Logging and printing

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

Ez logging

+

Logging is packed in these 5 functions

+
+
reset_logger("trace", console_width=80)
+Trace("Testing {1,2,3}")
+Debug("TESTING {1,2,3}")
+Info("TESTING {1,2,3}")
+Warn("TESTING {1,2,3}")
+Excep("TESTING {1,2,3}")
+
+
[08/24/24 12:16:05] TRACE    Testing {1,2,3}            1838145784.py:<module>:2
+
+
+
+
                    DEBUG    TESTING {1,2,3}            1838145784.py:<module>:3
+
+
+
+
                    INFO     TESTING {1,2,3}            1838145784.py:<module>:4
+
+
+
+
                    WARNING  TESTING {1,2,3}            1838145784.py:<module>:5
+
+
+
+
                    ERROR    TESTING {1,2,3}            1838145784.py:<module>:6
+                             NoneType: None                                     
+                                                                                
+                                                                                
+
+
+
+

Excep will also print a traceback

+
+
def do():
+    try:
+        1 / 0
+    except Exception as e:
+        Excep(e)
+
+def do2():
+    do()
+
+do2()
+
+
[08/24/24 12:16:07] ERROR    division by zero                 1923207781.py:do:5
+                             ZeroDivisionError: division by                     
+                             zero                                               
+                                                                                
+                             ╭─ Traceback (most recent call─╮                   
+                             │ in do:3                      │                   
+                             │                              │                   
+                             │    1 def do():               │                   
+                             │    2 │   try:                │                   
+                             │ ❱  3 │   │   1 / 0           │                   
+                             │    4 │   except Exception as │                   
+                             │    5 │   │   Excep(e)        │                   
+                             │    6                         │                   
+                             ╰──────────────────────────────╯                   
+                             ZeroDivisionError: division by                     
+                             zero                                               
+
+
+
+

All logging functions have an optional depth that will be helpful to raise the context level to outer functions

+
+
def do():
+    Info('In do')
+
+do()
+
+
[08/24/24 12:16:15] INFO     In do                             809811040.py:do:2
+
+
+
+
+
def do2():
+    Info('Log will still say it is from `do2` now, to the right of log print')
+
+def do():
+    do2()
+
+do()
+
+
[08/24/24 12:17:13] INFO     Log will still say it is from   2099333876.py:do2:2
+                             `do2` now, to the right of log                     
+                             print                                              
+
+
+
+
+
def do2():
+    Info('But now, log will still say it is from do', depth=1)
+
+def do():
+    do2()
+
+do()
+
+
[08/24/24 12:17:25] INFO     But now, log will still say it   4054019042.py:do:5
+                             is from do                                         
+
+
+
+
+
+

Logging Level and context

+
+
+

in_logger_mode

+
+
 in_logger_mode (level:str)
+
+

return’s T/F, checking if logger is in a specific mode or not

+
+
+
+

logger_mode

+
+
 logger_mode (level)
+
+

temporarily, using with context, set the level to something else

+
+
+
+

get_logger_level

+
+
 get_logger_level ()
+
+

get the current logger’s level

+

Let’s log every level in the do function below. We can control what we need to log from outside the function’s context by
+using with <level>_model():

+
+
def do():
+    Trace(0)
+    Debug(1)
+    Info(2)
+    Warn(3)
+    Excep(4)
+
+
+def line(x):
+    sep = "=" * 20
+    print(f"{sep}{x}{sep}")
+    print(f"{in_excep_mode()=}")
+    print(f"{in_warn_mode()=}")
+    print(f"{in_info_mode()=}")
+    print(f"{in_debug_mode()=}")
+    print(f"{in_trace_mode()=}")
+
+
+reset_logger()
+
+with excep_mode():
+    line("Excep mode")
+    do()
+
+with warn_mode():
+    line("Warn mode")
+    do()
+
+with info_mode():
+    line("Info mode")
+    do()
+
+with debug_mode():
+    line("Debug mode")
+    do()
+
+with trace_mode():
+    line("Trace mode")
+    do()
+
+
====================Excep mode====================
+in_excep_mode()=True
+in_warn_mode()=False
+in_info_mode()=False
+in_debug_mode()=False
+in_trace_mode()=False
+
+
+
[08/24/24 12:13:08] ERROR    4                                                                                                                            3432048509.py:do:6
+                             NoneType: None                                                                                                                                 
+                                                                                                                                                                            
+                                                                                                                                                                            
+
+
+
+
====================Warn mode====================
+in_excep_mode()=False
+in_warn_mode()=True
+in_info_mode()=False
+in_debug_mode()=False
+in_trace_mode()=False
+
+
+
[08/24/24 12:13:08] WARNING  3                                                                                                                            3432048509.py:do:5
+
+
+
+
                    ERROR    4                                                                                                                            3432048509.py:do:6
+                             NoneType: None                                                                                                                                 
+                                                                                                                                                                            
+                                                                                                                                                                            
+
+
+
+
====================Info mode====================
+in_excep_mode()=False
+in_warn_mode()=False
+in_info_mode()=True
+in_debug_mode()=False
+in_trace_mode()=False
+
+
+
[08/24/24 12:13:08] INFO     2                                                                                                                            3432048509.py:do:4
+
+
+
+
                    WARNING  3                                                                                                                            3432048509.py:do:5
+
+
+
+
                    ERROR    4                                                                                                                            3432048509.py:do:6
+                             NoneType: None                                                                                                                                 
+                                                                                                                                                                            
+                                                                                                                                                                            
+
+
+
+
====================Debug mode====================
+in_excep_mode()=False
+in_warn_mode()=False
+in_info_mode()=False
+in_debug_mode()=True
+in_trace_mode()=False
+
+
+
[08/24/24 12:13:08] DEBUG    1                                                                                                                            3432048509.py:do:3
+
+
+
+
                    INFO     2                                                                                                                            3432048509.py:do:4
+
+
+
+
                    WARNING  3                                                                                                                            3432048509.py:do:5
+
+
+
+
                    ERROR    4                                                                                                                            3432048509.py:do:6
+                             NoneType: None                                                                                                                                 
+                                                                                                                                                                            
+                                                                                                                                                                            
+
+
+
+
====================Trace mode====================
+in_excep_mode()=False
+in_warn_mode()=False
+in_info_mode()=False
+in_debug_mode()=False
+in_trace_mode()=True
+
+
+
[08/24/24 12:13:08] TRACE    0                                                                                                                            3432048509.py:do:2
+
+
+
+
                    DEBUG    1                                                                                                                            3432048509.py:do:3
+
+
+
+
                    INFO     2                                                                                                                            3432048509.py:do:4
+
+
+
+
                    WARNING  3                                                                                                                            3432048509.py:do:5
+
+
+
+
                    ERROR    4                                                                                                                            3432048509.py:do:6
+                             NoneType: None                                                                                                                                 
+                                                                                                                                                                            
+                                                                                                                                                                            
+
+
+
+

The in_<level>_mode gives an additional layer of control, to be used for debugging dynamically. Let’s say, you want to show an image (for the sake of debugging)

+
+
def do(im_path):
+    from torch_snippets import show, read
+    im = read(im_path)
+    show(im, sz=3)
+    print(im.mean())
+
+do('assets/Preamble.png')
+
+
+
+

+
+
+
+
+
145.5542982442515
+
+
+

But now you are happy with your code and don’t want the show, say the code is going to production. A common way out is to just comment that line

+
+
def do(im_path):
+    from torch_snippets import show, read
+    im = read(im_path)
+    # show(im, sz=3) # line is commented, but will need to be re-uncommented any time it needs debugging
+    print(im.mean())
+
+do('assets/Preamble.png')
+
+
145.5542982442515
+
+
+

But if you want to re-check, it’s a pain to again uncomment. Not to mention this method is not scalable to 100s of lines of code. The simple way to deal with such transient code that needs to activate only when you want it to, is to enclose in an if in_<level>_mode conditional like so

+
+
def do(im_path):
+    from torch_snippets import show, read
+    im = read(im_path)
+    if in_debug_mode():
+        show(im, sz=3)
+    print(im.mean())
+
+do('assets/Preamble.png')
+
+
145.5542982442515
+
+
+

This way, you can always activate the show by calling do with a temporary with_debug_mode context

+
+
with debug_mode():
+    do('assets/Preamble.png')
+
+
+
+

+
+
+
+
+
145.5542982442515
+
+
+
+
+
+

Notify Waiting

+
+
reset_logger(console_width=100)
+
+with trace_mode():
+    with notify_waiting("Downloading"):
+        time.sleep(10)
+
+with notify_waiting("Downloading", spinner="guess_the_movie"):
+    time.sleep(10)
+
+with notify_waiting("One more message", spinner="earth"):
+    time.sleep(3)
+
+
[08/24/24 12:36:51] TRACE    Available Spinners: dict_keys(['dots', 'dots2', 'dots3', 'dots4', 'dots5', 'dots6', 'dots7', 'dots8', 'dots9',  3340474699.py:notify_waiting:41
+                             'dots10', 'dots11', 'dots12', 'dots8Bit', 'line', 'line2', 'pipe', 'simpleDots', 'simpleDotsScrolling', 'star',                                
+                             'star2', 'flip', 'hamburger', 'growVertical', 'growHorizontal', 'balloon', 'balloon2', 'noise', 'bounce',                                      
+                             'boxBounce', 'boxBounce2', 'triangle', 'arc', 'circle', 'squareCorners', 'circleQuarters', 'circleHalves',                                     
+                             'squish', 'toggle', 'toggle2', 'toggle3', 'toggle4', 'toggle5', 'toggle6', 'toggle7', 'toggle8', 'toggle9',                                    
+                             'toggle10', 'toggle11', 'toggle12', 'toggle13', 'arrow', 'arrow2', 'arrow3', 'bouncingBar', 'bouncingBall',                                    
+                             'smiley', 'monkey', 'hearts', 'clock', 'earth', 'material', 'moon', 'runner', 'pong', 'shark', 'dqpb',                                         
+                             'weather', 'christmas', 'grenade', 'point', 'layer', 'betaWave', 'aesthetic', 'guess', 'guess_the_movie'])                                     
+                                                                                                                                                                            
+                             Using: clock                                                                                                                                   
+
+
+
+ +
+
+

+

+

+
[08/24/24 12:37:01] INFO     Downloading - Completed in 10.01 s                                                                                   contextlib.py:__exit__:144
+

+

+

+
+

+

+

+

+

+
[08/24/24 12:37:11] INFO     Downloading - Completed in 10.01 s                                                                                   contextlib.py:__exit__:144
+

+

+

+
+

+

+

+

+

+
[08/24/24 12:37:14] INFO     One more message - Completed in 3.00 s                                                                               contextlib.py:__exit__:144
+

+

+

+

+

+
+        
+        
+

+

+
+
+

+
+
 
+
+
+
 
+
+
+
+
+
\ No newline at end of file
diff --git a/_proc/docs/logging_files/figure-html/cell-11-output-1.png b/_proc/docs/logging_files/figure-html/cell-11-output-1.png
new file mode 100644
index 0000000..7a75f15
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diff --git a/_proc/docs/logging_files/figure-html/cell-14-output-1.png b/_proc/docs/logging_files/figure-html/cell-14-output-1.png
new file mode 100644
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diff --git a/_proc/docs/markups.html b/_proc/docs/markups.html
new file mode 100644
index 0000000..38bfc55
--- /dev/null
+++ b/_proc/docs/markups.html
@@ -0,0 +1,988 @@
+
+
+
+
+
+
+
+
+
+Markups – torch_snippets
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+

+  

+    
+  
+

+
+

+
+  
+

+
+    
+
+

+
+

+

+
Markups

+

+
+
+
+

+
+    
+  
+    
+  

+  
+
+
+

+
+
+
+

+

+
AttrDict

+

+
 AttrDict (*args, given_input_to_ad=None, **kwargs)

+

+
*Utility class to interact with a dictionary as if it were an object. AD is an alias to this class

+
FEATURES: 0. Access and modify keys (including nested keys) as if they were object attributes, supporting tab-completion. Example: self.key1.key2[0].key3 1. Keys and values are recursively converted to AttrDict instances. 2. Pretty-print the dictionary using print. 3. Convert the entire structure to a regular dictionary at any time using self.to_dict() / self.dict(). 3. Recursively remove keys using self.drop(key) from a JSON object. 4. Apply a function to all values at all levels using map.

+
GOTCHAS: 1. All integer keys are implicitly converted to strings due to the enforced self.key format. 2. You can still use self[int], but this internally converts the integer to a string.

+
METHODS: - items(): Return the items of the AttrDict as key-value pairs. - keys(): Return the keys of the AttrDict. - values(): Return the values of the AttrDict. - update(dict): Update the AttrDict with key-value pairs from another dictionary. - get(key, default=None): Get the value associated with a key, with an optional default value. - __iter__(): Allow iteration over the keys of the AttrDict. - __len__(): Return the number of keys in the AttrDict. - __repr__(): Return a string representation of the AttrDict. - __dir__(): List the keys of the AttrDict as attributes. - __contains__(key): Check if a key exists in the AttrDict, use ‘a.b.c’ notation to directly check for a nested attribute. - __delitem__(key): Delete a key from the AttrDict. - map(func): Apply a function to all values in the AttrDict. - drop(key): Recursively remove a key and its values from the AttrDict. - to_dict(): Convert the AttrDict and its nested structure to a regular dictionary. - pretty(print_with_logger=False, *args, **kwargs): Pretty-print the AttrDict as JSON. - __eq__(other): Compare the AttrDict with another dictionary for equality. - find_address(key, current_path=""): Find and return all addresses (paths) of a given key in the AttrDict. - summary(current_path='', summary_str='', depth=0, sep='  '): Generate a summary of the structure and values in the AttrDict. - write_summary(to, **kwargs): Write the summary to a file or stream. - fetch(addr): Retrieve a value at a specified address (path).

+
PARAMETERS: - data (dict, optional): Initial data to populate the AttrDict.

+
USAGE: - Create an AttrDict instance by providing an optional initial dictionary, and then access and manipulate its contents as if they were object attributes.

+
EXAMPLE:

+
my_dict = {'name': 'John', 'age': 30, 'address': {'city': 'New York', 'zip': '10001'}}
+attr_dict = AttrDict(my_dict)
+print(attr_dict.name)  # Access values like attributes
+attr_dict.address.city = 'Los Angeles'  # Modify nested values
+```*
+
+
+::: {#bfb4146f-90bf-42bc-bf40-69fb6d612ea2 .cell tags='[]' execution_count=5}
+``` {.python .cell-code}
+xx = AttrDict({"a": 1, "b": [{"c": 2, "d": 4}, {"e": 3}], "f": {"g": {"h": 20}}})
+print(type(xx.b[0]))
+print(type(xx.to_dict()["b"][0]))
+print("f.g.h" in xx)
+xx.pretty()

+

+
<class 'torch_snippets.markup2.AttrDict'>
+<class 'dict'>
+True
+{
+    "a": 1,
+    "b": [
+        {
+            "c": 2,
+            "d": 4
+        },
+        {
+            "e": 3
+        }
+    ],
+    "f": {
+        "g": {
+            "h": 20
+        }
+    }
+}

+

+
:::

+

+
x = {"abc": {"b": 10, "c": 11}, "d": {"e": {"f": [2, {"abc": "pqrs"}, 2.234]}}}
+
+y = AttrDict(x)
+
+assert y.abc.b == 10
+assert y.d.e.f == [2, {"abc": "pqrs"}, 2.234]
+
+y.d.e.g = 11
+
+# del y.abc.c
+# OR
+del y["abc"]["c"]
+
+assert y.to_dict() == {
+    "abc": {"b": 10},
+    "d": {"e": {"f": [2, {"abc": "pqrs"}, 2.234], "g": 11}},
+}
+
+y.pretty(indent=2)
+
+assert "abc" in y
+assert "def" not in y
+print("e" in y.d)

+

+
{
+  "abc": {
+    "b": 10
+  },
+  "d": {
+    "e": {
+      "f": [
+        2,
+        {
+          "abc": "pqrs"
+        },
+        2.234
+      ],
+      "g": 11
+    }
+  }
+}
+True

+

+

+

+
x = {"abc": {"b": 10, "c": 11}, "d": {"e": {"f": [2, {"abc": "pqrs"}, 2.234]}}}
+y = AttrDict(x)
+y.abc[["b", "c"]]

+

+

+```↯ AttrDict ↯
+b - 10 (🏷️ int)
+c - 11 (🏷️ int)
+
+```

+

+

+

+

+

+
write_json

+

+
 write_json (obj, fpath, silent=False)

+

+

+

+

+
read_json

+

+
 read_json (fpath)

+

+

+
try:
+    import torch
+
+    d = AD(a=torch.Tensor([1, 2, 3]), b="hello")
+    write_json(d, "/tmp/tmp.json")
+    print("\n".join(readlines("/tmp/tmp.json")))
+except ModuleNotFoundError:
+    ...

+

+
[08/24/24 12:50:35] INFO     loaded 8 lines                                                                                        d=266370;file://<ipython-input-1-e6d68859b80d>:6\<ipython-input-1-e6d68859b80d>;;\:d=65937;file://<ipython-input-1-e6d68859b80d>:6#<module>:6\<module>:6;;\
+{
+"a": [
+1.0,
+2.0,
+3.0
+],
+"b": "hello"
+}

+

+

+

+
d = [1, {1: 1, 2: 2}, 3]
+
+pretty_json({1: 1, 2: 2})
+pretty_json(d)
+
+f = write_json(d, "/tmp/test.json")
+print(f)
+read_json(f)

+

+
/tmp/test.json

+

+

+
[1, {'1': 1, '2': 2}, 3]

+

+

+

+

+

+
read_jsonl

+

+
 read_jsonl (file)

+

+

+

+

+
write_jsonl

+

+
 write_jsonl (items, dest, mode='a')

+

+

+

+

+
write_yaml

+

+
 write_yaml (content, fpath)

+

+

+

+

+
read_yaml

+

+
 read_yaml (file)

+

+

+

+

+
write_xml

+

+
 write_xml (data:Union[torch_snippets.markup2.AttrDict,dict],
+            file_path:Union[str,pathlib.Path])

+

+

+

+

+
read_xml

+

+
 read_xml (file_path:Union[str,pathlib.Path])

+

+
Read xml data as a dictionary

+

+
y.to_dict()

+

+
{'abc': {'b': 10, 'c': 11}, 'd': {'e': {'f': [2, {'abc': 'pqrs'}, 2.234]}}}

+

+

+

+
y

+

+

+```↯ AttrDict ↯
+abc
+  b - 10 (🏷️ int)
+  c - 11 (🏷️ int)
+d
+  e
+    f
+      0 - 2 (🏷️ int)
+      1
+        abc - pqrs (🏷️ str)
+      2 - 2.234 (🏷️ float)
+
+```

+

+

+
+
+

+
+
 
+
+
 
+
+
+
+
+
\ No newline at end of file
diff --git a/_proc/docs/misc.html b/_proc/docs/misc.html
new file mode 100644
index 0000000..ee16094
--- /dev/null
+++ b/_proc/docs/misc.html
@@ -0,0 +1,1347 @@
+
+
+
+
+
+
+
+
+
+Miscellaneous – torch_snippets
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

+  

+    
+  
+

+
+

+
+  
+

+
+    
+
+

+
+

+

+
Miscellaneous

+

+
+
+
+

+
+    
+  
+    
+  

+  
+
+
+

+
+
+
+

+
time.sleep(3)

+

+

+
Timer

+
Use timer as a standalone class so you have full control on when to call a lap (most useful in while loops)…

+

+
N = 50
+t = Timer(N)
+info = None
+
+for i in range(N):
+    time.sleep(0.1)
+    t(info=info)  # Lap and present the time
+    if i == N//2:
+        print()
+        info = f"My Info: {i*3.122}"

+

+
26/50 (2.70s - 2.49s remaining - 9.62 iters/s)          
+My Info: 78.05  50/50 (5.20s - 0.00s remaining - 9.61 iters/s)          

+

+

+

+

+
Track2

+
… or use track2 to directly track a for loop

+

+
N = 50
+info = None
+
+for i in (tracker := track2(range(N), total=N)):
+    time.sleep(0.1)
+    info = f"My Info: {i*3.122:.2f}"
+    if i == N // 2:
+        print()
+    if i >= N // 2:
+        tracker.send(info)

+

+
25/50 (2.59s - 2.59s remaining - 9.66 iters/s)          
+My Info: 152.98 50/50 (5.18s - 0.00s remaining - 9.65 iters/s)          

+

+

+
Warning! NEVER RUN tracker.send(None) as this will skip variables silently

+

+
# https://youtu.be/XcU-a-eksMA
+from IPython.display import YouTubeVideo
+
+# Replace 'video_id' with the actual YouTube video ID
+YouTubeVideo('XcU-a-eksMA', width=560, height=315)

+

+
+        
+        
+

+

+

+
+
+

+
Try Catch with a single line

+

+
@tryy
+def do(a, b, c):
+    return 1 / 0
+
+
+x = do(1, 2, c=10)
+assert x is None  # tryy returns None by default

+

+
[08/24/24 12:46:28] ERROR    Error for `do` with                                                                                                      1057038067.py:inner:30
+                             args()                                                                                                                                         
+                               0 - 1 (🏷️ int)                                                                                                                                
+                               1 - 2 (🏷️ int)                                                                                                                                
+                             kwargs                                                                                                                                         
+                               c - 10 (🏷️ int)                                                                                                                               
+                                                                                                                                                                            
+                             ZeroDivisionError: division by zero                                                                                                            
+                             Traceback (most recent call last):                                                                                                             
+                                                                                                                                                                            
+                               File "/var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_45033/1057038067.py", line 21, in inner                                     
+                                 return f(*args, **kwargs)                                                                                                                  
+                                        |  |       -> {'c': 10}                                                                                                             
+                                        |  -> (1, 2)                                                                                                                        
+                                        -> <function do at 0x11ecdd800>                                                                                                     
+                                                                                                                                                                            
+                               File "/var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_45033/884313129.py", line 3, in do                                          
+                                 return 1 / 0                                                                                                                               
+                                                                                                                                                                            
+                             ZeroDivisionError: division by zero                                                                                                            
+                                                                                                                                                                            
+                             ╭───────────────────────────────────────── Traceback (most recent call last) ──────────────────────────────────────────╮                       
+                             │ in inner:21                                                                                                          │                       
+                             │                                                                                                                      │                       
+                             │   18 │   │                                                                                                           │                       
+                             │   19 │   │   def inner(*args, **kwargs):                                                                             │                       
+                             │   20 │   │   │   try:                                                                                                │                       
+                             │ ❱ 21 │   │   │   │   return f(*args, **kwargs)                                                                       │                       
+                             │   22 │   │   │   except Exception as e:                                                                              │                       
+                             │   23 │   │   │   │   if not print_traceback:                                                                         │                       
+                             │   24 │   │   │   │   │   tb = f"{type(e).__name__}: {str(e)}"                                                        │                       
+                             │                                                                                                                      │                       
+                             │ in do:3                                                                                                              │                       
+                             │                                                                                                                      │                       
+                             │   1 @tryy                                                                                                            │                       
+                             │   2 def do(a, b, c):                                                                                                 │                       
+                             │ ❱ 3 │   return 1 / 0                                                                                                 │                       
+                             │   4                                                                                                                  │                       
+                             │   5                                                                                                                  │                       
+                             │   6 x = do(1, 2, c=10)                                                                                               │                       
+                             ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯                       
+                             ZeroDivisionError: division by zero                                                                                                            
+

+

+

+
Use your own default on failure

+

+
@tryy(output_to_return_on_fail="😔")
+def do(a, b, c):
+    return 1 / 0
+
+
+do(1, 2, c=10)

+

+
                    ERROR    Error for `do` with                                                                                                      1057038067.py:inner:30
+                             args()                                                                                                                                         
+                               0 - 1 (🏷️ int)                                                                                                                                
+                               1 - 2 (🏷️ int)                                                                                                                                
+                             kwargs                                                                                                                                         
+                               c - 10 (🏷️ int)                                                                                                                               
+                                                                                                                                                                            
+                             ZeroDivisionError: division by zero                                                                                                            
+                             Traceback (most recent call last):                                                                                                             
+                                                                                                                                                                            
+                               File "/var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_45033/1057038067.py", line 21, in inner                                     
+                                 return f(*args, **kwargs)                                                                                                                  
+                                        |  |       -> {'c': 10}                                                                                                             
+                                        |  -> (1, 2)                                                                                                                        
+                                        -> <function do at 0x11ecdeac0>                                                                                                     
+                                                                                                                                                                            
+                               File "/var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_45033/3352784188.py", line 3, in do                                         
+                                 return 1 / 0                                                                                                                               
+                                                                                                                                                                            
+                             ZeroDivisionError: division by zero                                                                                                            
+                                                                                                                                                                            
+                             ╭───────────────────────────────────────── Traceback (most recent call last) ──────────────────────────────────────────╮                       
+                             │ in inner:21                                                                                                          │                       
+                             │                                                                                                                      │                       
+                             │   18 │   │                                                                                                           │                       
+                             │   19 │   │   def inner(*args, **kwargs):                                                                             │                       
+                             │   20 │   │   │   try:                                                                                                │                       
+                             │ ❱ 21 │   │   │   │   return f(*args, **kwargs)                                                                       │                       
+                             │   22 │   │   │   except Exception as e:                                                                              │                       
+                             │   23 │   │   │   │   if not print_traceback:                                                                         │                       
+                             │   24 │   │   │   │   │   tb = f"{type(e).__name__}: {str(e)}"                                                        │                       
+                             │                                                                                                                      │                       
+                             │ in do:3                                                                                                              │                       
+                             │                                                                                                                      │                       
+                             │   1 @tryy(output_to_return_on_fail="😔")                                                                             │                       
+                             │   2 def do(a, b, c):                                                                                                 │                       
+                             │ ❱ 3 │   return 1 / 0                                                                                                 │                       
+                             │   4                                                                                                                  │                       
+                             │   5                                                                                                                  │                       
+                             │   6 do(1, 2, c=10)                                                                                                   │                       
+                             ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯                       
+                             ZeroDivisionError: division by zero                                                                                                            
+

+

+

+
'😔'

+

+

+
Optionally print the full stacktrace if needed

+

+
@tryy(print_traceback=True, output_to_return_on_fail="😔")
+def do(a, b, c):
+    return 1 / 0
+
+
+do(1, 2, c=10)

+

+
                    ERROR    Error for `do` with                                                                                                      1057038067.py:inner:30
+                             args()                                                                                                                                         
+                               0 - 1 (🏷️ int)                                                                                                                                
+                               1 - 2 (🏷️ int)                                                                                                                                
+                             kwargs                                                                                                                                         
+                               c - 10 (🏷️ int)                                                                                                                               
+                                                                                                                                                                            
+                             Traceback (most recent call last):                                                                                                             
+                               File "/var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_45033/1057038067.py", line 21, in inner                                     
+                                 return f(*args, **kwargs)                                                                                                                  
+                                        ^^^^^^^^^^^^^^^^^^                                                                                                                  
+                               File "/var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_45033/580638143.py", line 3, in do                                          
+                                 return 1 / 0                                                                                                                               
+                                        ~~^~~                                                                                                                               
+                             ZeroDivisionError: division by zero                                                                                                            
+                                                                                                                                                                            
+                             Traceback (most recent call last):                                                                                                             
+                                                                                                                                                                            
+                               File "/var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_45033/1057038067.py", line 21, in inner                                     
+                                 return f(*args, **kwargs)                                                                                                                  
+                                        |  |       -> {'c': 10}                                                                                                             
+                                        |  -> (1, 2)                                                                                                                        
+                                        -> <function do at 0x11ecdeb60>                                                                                                     
+                                                                                                                                                                            
+                               File "/var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_45033/580638143.py", line 3, in do                                          
+                                 return 1 / 0                                                                                                                               
+                                                                                                                                                                            
+                             ZeroDivisionError: division by zero                                                                                                            
+                                                                                                                                                                            
+                             ╭───────────────────────────────────────── Traceback (most recent call last) ──────────────────────────────────────────╮                       
+                             │ in inner:21                                                                                                          │                       
+                             │                                                                                                                      │                       
+                             │   18 │   │                                                                                                           │                       
+                             │   19 │   │   def inner(*args, **kwargs):                                                                             │                       
+                             │   20 │   │   │   try:                                                                                                │                       
+                             │ ❱ 21 │   │   │   │   return f(*args, **kwargs)                                                                       │                       
+                             │   22 │   │   │   except Exception as e:                                                                              │                       
+                             │   23 │   │   │   │   if not print_traceback:                                                                         │                       
+                             │   24 │   │   │   │   │   tb = f"{type(e).__name__}: {str(e)}"                                                        │                       
+                             │                                                                                                                      │                       
+                             │ in do:3                                                                                                              │                       
+                             │                                                                                                                      │                       
+                             │   1 @tryy(print_traceback=True, output_to_return_on_fail="😔")                                                       │                       
+                             │   2 def do(a, b, c):                                                                                                 │                       
+                             │ ❱ 3 │   return 1 / 0                                                                                                 │                       
+                             │   4                                                                                                                  │                       
+                             │   5                                                                                                                  │                       
+                             │   6 do(1, 2, c=10)                                                                                                   │                       
+                             ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯                       
+                             ZeroDivisionError: division by zero                                                                                                            
+

+

+

+
'😔'

+

+

+
You can also silence the errors completely

+

+
@tryy(silence_errors=True, output_to_return_on_fail="😔")
+def do(a, b, c):
+    return 1 / 0
+
+
+do(1, 2, c=10)

+

+
'😔'

+

+

+
You can collect all your errors in a list

+

+
import random
+
+errors = []
+
+
+@tryy(silence_errors=True, store_errors=errors)
+def do(a, b, c):
+    if random.randint(0, 100) < 50:
+        return 1 / 0
+    else:
+        raise NotImplementedError("🤔")
+
+
+for _ in range(4):
+    do(1, random.randint(0, 10), c=random.randint(0, 100))
+
+print(errors)

+

+
[
+```↯ AttrDict ↯
+func - do (🏷️ str)
+args()
+  0 - 1 (🏷️ int)
+  1 - 5 (🏷️ int)
+kwargs
+  c - 21 (🏷️ int)
+tb - NotImplementedError: 🤔 (🏷️ str)
+err_type - NotImplementedError (🏷️ str)
+
+```
+, 
+```↯ AttrDict ↯
+func - do (🏷️ str)
+args()
+  0 - 1 (🏷️ int)
+  1 - 7 (🏷️ int)
+kwargs
+  c - 93 (🏷️ int)
+tb - NotImplementedError: 🤔 (🏷️ str)
+err_type - NotImplementedError (🏷️ str)
+
+```
+, 
+```↯ AttrDict ↯
+func - do (🏷️ str)
+args()
+  0 - 1 (🏷️ int)
+  1 - 4 (🏷️ int)
+kwargs
+  c - 4 (🏷️ int)
+tb - ZeroDivisionError: division by zero (🏷️ str)
+err_type - ZeroDivisionError (🏷️ str)
+
+```
+, 
+```↯ AttrDict ↯
+func - do (🏷️ str)
+args()
+  0 - 1 (🏷️ int)
+  1 - 0 (🏷️ int)
+kwargs
+  c - 36 (🏷️ int)
+tb - NotImplementedError: 🤔 (🏷️ str)
+err_type - NotImplementedError (🏷️ str)
+
+```
+]

+

+

+
There’s onlly one usecase where you would want to send in a list by yourself - when you want to append your errors to an existing list. The sensible default is to always store the errors, especially because this is a debugging tool.

+
Just access all the errors in a dataframe like so

+

+
import random
+
+random.seed(10)
+
+
+@tryy(silence_errors=True)
+def do(a, b, c):
+    if c < 50:
+        return 1 / 0
+    else:
+        raise NotImplementedError("🤔")
+
+
+for _ in range(4):
+    do(1, random.randint(0, 10), c=random.randint(0, 100))
+
+do.error_summary()

+

+

+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
funcargskwargstberr_type
0do[1, 9]{'c': 4}ZeroDivisionError: division by zeroZeroDivisionError
1do[1, 6]{'c': 61}NotImplementedError: 🤔NotImplementedError
2do[1, 9]{'c': 1}ZeroDivisionError: division by zeroZeroDivisionError
3do[1, 3]{'c': 59}NotImplementedError: 🤔NotImplementedError

+
+

+

+

+
and the actual list of errors like so

+

+
do.error_store

+

+
[
+ ```↯ AttrDict ↯
+ func - do (🏷️ str)
+ args()
+   0 - 1 (🏷️ int)
+   1 - 9 (🏷️ int)
+ kwargs
+   c - 4 (🏷️ int)
+ tb - ZeroDivisionError: division by zero (🏷️ str)
+ err_type - ZeroDivisionError (🏷️ str)
+ 
+ ```,
+ 
+ ```↯ AttrDict ↯
+ func - do (🏷️ str)
+ args()
+   0 - 1 (🏷️ int)
+   1 - 6 (🏷️ int)
+ kwargs
+   c - 61 (🏷️ int)
+ tb - NotImplementedError: 🤔 (🏷️ str)
+ err_type - NotImplementedError (🏷️ str)
+ 
+ ```,
+ 
+ ```↯ AttrDict ↯
+ func - do (🏷️ str)
+ args()
+   0 - 1 (🏷️ int)
+   1 - 9 (🏷️ int)
+ kwargs
+   c - 1 (🏷️ int)
+ tb - ZeroDivisionError: division by zero (🏷️ str)
+ err_type - ZeroDivisionError (🏷️ str)
+ 
+ ```,
+ 
+ ```↯ AttrDict ↯
+ func - do (🏷️ str)
+ args()
+   0 - 1 (🏷️ int)
+   1 - 3 (🏷️ int)
+ kwargs
+   c - 59 (🏷️ int)
+ tb - NotImplementedError: 🤔 (🏷️ str)
+ err_type - NotImplementedError (🏷️ str)
+ 
+ ```]

+

+

+
Finally, you want to run the function (without try) to reproduce the error and actually start debugging. Just use the .F attribute to access the original function that you created

+

+
ix = 2
+data = do.error_store[ix]
+do.F(*data.args, **data.kwargs)

+

+

+
---------------------------------------------------------------------------
+ZeroDivisionError                         Traceback (most recent call last)
+Cell In[19], line 3
+      1 ix = 2
+      2 data = do.error_store[ix]
+----> 3 do.F(*data.args, **data.kwargs)
+
+Cell In[17], line 9, in do(a, b, c)
+      6 @tryy(silence_errors=True)
+      7 def do(a, b, c):
+      8     if c < 50:
+----> 9         return 1 / 0
+     10     else:
+     11         raise NotImplementedError("🤔")
+
+ZeroDivisionError: division by zero

+

+

+

+

+
def deco(decorator):
+    @wraps(decorator)
+    def wrapper(*args, **kwargs):
+        def real_decorator(func):
+            @wraps(func)
+            def inner_wrapper(*fargs, **fkwargs):
+                return decorator(func, *fargs, **fkwargs)
+
+            return inner_wrapper
+
+        if len(args) == 1 and callable(args[0]) and not kwargs:
+            # Case when B is used without arguments
+            return real_decorator(args[0])
+        else:
+            # Case when B is used with arguments
+            def custom_decorator(func):
+                return decorator(func, **kwargs)
+
+            return custom_decorator
+
+    return wrapper

+

+

+
@deco
+def B(func, *args, deco_param="default", **kwargs):
+    print("B", deco_param, print("args", *args, "kwargs", **kwargs))
+    return func(*args, **kwargs)

+

+

+
@B
+def C(a, b, c):
+    print("C")
+    return a + (b * c)
+
+
+C(1, 2, 3)

+

+

+
@B(deco_param="new_param")
+def C(a, b, c):
+    return a + (b * c)
+
+
+# Testing
+print(C(1, 2, 3))  # Outputs 'new_param' then the result 7

+

+
+
+

+
+
 
+
+
 
+
+
+
+
+
\ No newline at end of file
diff --git a/_proc/docs/paths.html b/_proc/docs/paths.html
new file mode 100644
index 0000000..e226cc4
--- /dev/null
+++ b/_proc/docs/paths.html
@@ -0,0 +1,1573 @@
+
+
+
+
+
+
+
+
+
+paths – torch_snippets
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

+  

+    
+  
+

+
+

+
+  
+

+
+    
+
+

+
+
+
+
+

+
Paths

+
Utilities to manipulate Paths

+
+

+
print(P().ls())
+print(P().resolve())

+

+
[» _quarto.yml, » decorators.ipynb, » markups.ipynb, » sidebar.yml, » AttrDict.ipynb, » interactive_show.ipynb, » load_defautls.ipynb, » sklegos.ipynb, » bounding_boxes.ipynb, » show.ipynb, » pdf.ipynb, » charts.ipynb, » paths.ipynb, » nbdev.yml, » tmp.csv, » jupyter_notebook.ipynb, » config.ipynb, » misc.ipynb, » registry.ipynb, » adapters.ipynb, » report.ipynb, » .ipynb_checkpoints, » capsule.ipynb, » logging.ipynb, » inspector.ipynb, » bokeh_plotting.ipynb, » index.ipynb, » imgaug_loader.ipynb]
+/Users/yeshwanth/Code/Personal/torch_snippets/nbs

+

+

+

+
# !touch tmp.txt tmp.csv
+# # x = P()
+# x.tmp__csv, x.tmp__txt, x.misc

+

+

+
!rm tmp.txt tmp.misc

+

+
rm: tmp.txt: No such file or directory
+rm: tmp.misc: No such file or directory

+

+

+

+

+
Path.rm

+

+
 Path.rm (confirm_prompt='Are you sure you want to delete `{self}`?
+          [y/N]', silent=True, missing_ok=True, force=False,
+          dry_run=False)

+

+

+

+

+
remove_file

+

+
 remove_file (dry_run)

+

+

+

+

+
Path.cp

+

+
 Path.cp (to)

+

+

+

+

+
Path.mv

+

+
 Path.mv (to)

+

+

+

+

+
Path.sample

+

+
 Path.sample (pattern='*')

+

+

+

+

+
extn

+

+
 extn (fpath)

+

+

+

+

+
Path.extn

+

+
 Path.extn ()

+

+

+

+

+
Path.sz

+

+
 Path.sz ()

+

+

+

+

+
Path.size

+

+
 Path.size ()

+

+

+

+

+
Path.rmtree

+

+
 Path.rmtree (prompt='Really remove `{self}` and its contents? [y/n] ',
+              force=False)

+

+

+
p = P("test.txt")
+p.touch()
+logger.info(p.size())

+

+
[08/24/24 11:12:12] INFO     0 KB                                                                                                                   1417366103.py:<module>:3
+

+

+

+
Path objects can be moved and copied

+

+
p = p.mv("test1.txt")
+q = p.cp("test2.txt")

+

+
Path objects have a size, extn (extension) and parent attributes

+

+
assert isinstance(q, P)
+assert q.size() == "0 KB"
+assert str(p) == "test1.txt"
+assert p.extn == "txt"
+assert p.parent == P()

+

+
They can be deleted with/without a prompt

+

+
p.rm(force=True)
+q.rm(confirm_prompt=False)

+

+
Folders can be globbed with a default of everything

+

+
p = P("../torch_snippets")
+assert P().ls() == P().Glob()
+p.Glob("*.py")

+

+
(#34) [» ../torch_snippets/misc.py,» ../torch_snippets/load_defaults.py,» ../torch_snippets/text_utils.py,» ../torch_snippets/_nbdev.py,» ../torch_snippets/paths.py,» ../torch_snippets/charts.py,» ../torch_snippets/pdf_loader.py,» ../torch_snippets/interactive_show.py,» ../torch_snippets/registry.py,» ../torch_snippets/markup2.py,» ../torch_snippets/_modidx.py,» ../torch_snippets/inspector.py,» ../torch_snippets/__init__.py,» ../torch_snippets/tmp.py,» ../torch_snippets/torch_loader.py,» ../torch_snippets/logger.py,» ../torch_snippets/markup.py,» ../torch_snippets/fastcores.py,» ../torch_snippets/sklegos.py,» ../torch_snippets/cli.py...]

+

+

+
You can sample a random file from the directory

+

+
q = p.sample("*.py")
+Info(f"Sample file: `{q}`")
+Info(f"Sample file size: `{q.size()}`")

+

+
                    INFO     Sample file: `../torch_snippets/logger.py`                                                                             1725593160.py:<module>:2
+

+

+

+
                    INFO     Sample file size: `9 KB`                                                                                               1725593160.py:<module>:3
+

+

+

+

+
try:
+    p.size()
+except Exception as e:
+    logger.warning(e)

+

+
                    WARNING  `../torch_snippets` is a directory                                                                                     1980994904.py:<module>:4
+

+

+

+

+
p = P("test.txt")
+p.touch()
+assert isdir(p) == False
+assert fname(p) == "test.txt"
+assert parent(p) == P()
+assert stem(p) == "test"
+assert extn(p) == "txt"
+
+print(find("capsule", Glob("./")))
+
+p.rm(confirm_prompt=False)

+

+
capsule.ipynb

+

+

+

+

+

+
list_zip

+

+
 list_zip (file)

+

+

+

+

+
unzip_file

+

+
 unzip_file (file, dest)

+

+

+

+

+
zip_files

+

+
 zip_files (list_of_files, dest)

+

+

+
!touch test1.txt test2.txt
+f = zip_files(P().Glob("*.txt"), "test.tar.gz")
+unzip_file(f, "./")
+[f.rm(force=True) for f in P().Glob("*.txt")]
+P("test.tar.gz").rm(force=True)
+
+!touch test1.txt test2.txt
+f = zip_files(P().Glob("*.txt"), "test.zip")
+unzip_file(f, "./")
+[f.rm(force=True) for f in P().Glob("*.txt")]
+P("test.zip").rm(force=True)

+

+
                    INFO     Zipping 2 files to test.tar.gz...                                                                                     3826617683.py:zip_files:8
+

+

+

+
100%|██████████| 2/2 [00:00<00:00, 649.78it/s]
+/var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_40147/3826617683.py:27: DeprecationWarning: Python 3.14 will, by default, filter extracted tar archives and reject files or modify their metadata. Use the filter argument to control this behavior.
+  f.extractall(dest)

+

+

+
                    INFO     Zipping 2 files to test.zip...                                                                                        3826617683.py:zip_files:8
+

+

+

+
100%|██████████| 2/2 [00:00<00:00, 2958.94it/s]

+

+

+

+

+

+
folder_summary

+

+
 folder_summary (thing)

+

+

+

+

+
common_items

+

+
 common_items (*fldrs, verbose=True)

+

+

+

+

+
remove_duplicates

+

+
 remove_duplicates (files)

+

+
Check a list of files and remove duplicates based on their checksum

+

+

+

+
md5

+

+
 md5 (fname)

+

+

+
md5("paths.ipynb")

+

+
'74eb392fa44e9293fb30ffed68c14112'

+

+

+

+

+

+
Path.write_lines

+

+
 Path.write_lines (lines, mode)

+

+

+

+

+
writelines

+

+
 writelines (lines, file, mode)

+

+

+

+

+
Path.read_file

+

+
 Path.read_file (**kwargs)

+

+

+

+

+
readfile

+

+
 readfile (*args, **kwargs)

+

+

+

+

+
Path.read_lines

+

+
 Path.read_lines (silent=False, encoding=None)

+

+

+

+

+
readlines

+

+
 readlines (fpath, silent=False, encoding=None, _strip=True)

+

+

+
P("paths.ipynb").read_lines()[:10]

+

+
                    INFO     loaded 1075 lines                                                                                                   2901855681.py:read_lines:13
+

+

+

+
['{',
+ '"cells": [',
+ '{',
+ '"cell_type": "raw",',
+ '"metadata": {},',
+ '"source": [',
+ '"# Paths\\n",',
+ '"Utilities to manipulate Paths\\n",',
+ '"---\\n",',
+ '"{}\\n",']

+

+

+

+

+

+
tree

+

+
 tree (directory='./', filelimit=50, to=None)

+

+

+
P("../").tree

+

+
/Users/yeshwanth/Code/Personal/torch_snippets
+ ── LICENSE
+ ── LICENSE.txt
+ ── MANIFEST
+ ── MANIFEST.in
+ ── Makefile
+ ── README.md
+ ── Screenshot 2022-09-08 at 4.40.18 PM.png
+ ── __module_timing__.py.lprof
+ ── _proc
+     ── AttrDict.ipynb
+     ── _quarto.yml
+     ── adapters.ipynb
+     ── bokeh_plotting.ipynb
+     ── bounding_boxes.ipynb
+     ── capsule.ipynb
+     ── charts.ipynb
+     ── config.ipynb
+     ── decorators.ipynb
+     ── docs
+         ── AttrDict.html
+         ── adapters.html
+         ── bokeh_plotting.html
+         ── bounding_boxes.html
+         ── bounding_boxes_files
+             ── figure-html
+                 ── cell-6-output-1.png
+                 ── cell-7-output-1.png
+                 ── cell-8-output-1.png
+                 ── cell-9-output-1.png
+         ── capsule.html
+         ── capsule_files
+             ── figure-html
+                 ── cell-6-output-2.png
+         ── charts.html
+         ── charts_files
+             ── figure-html
+                 ── cell-7-output-1.png
+         ── config.html
+         ── decorators.html
+         ── imgaug_loader.html
+         ── imgaug_loader_files
+             ── figure-html
+                 ── cell-3-output-1.png
+                 ── cell-5-output-10.png
+                 ── cell-5-output-12.png
+                 ── cell-5-output-14.png
+                 ── cell-5-output-16.png
+                 ── cell-5-output-18.png
+                 ── cell-5-output-2.png
+                 ── cell-5-output-20.png
+                 ── cell-5-output-22.png
+                 ── cell-5-output-4.png
+                 ── cell-5-output-6.png
+                 ── cell-5-output-8.png
+                 ── cell-6-output-1.png
+                 ── cell-7-output-1.png
+                 ── cell-8-output-2.png
+         ── index.html
+         ── inspector.html
+         ── interactive_show.html
+         ── jupyter_notebook.html
+         ── load_defautls.html
+         ── logging.html
+         ── markups.html
+         ── misc.html
+         ── paths.html
+         ── pdf.html
+         ── registry.html
+         ── report.html
+         ── report_files
+             ── figure-html
+                 ── cell-3-output-2.png
+                 ── cell-5-output-1.png
+         ── robots.txt
+         ── search.json
+         ── show.html
+         ── show_files
+             ── figure-html
+                 ── cell-2-output-1.png
+                 ── cell-3-output-1.png
+                 ── cell-4-output-2.png
+                 ── cell-4-output-4.png
+                 ── cell-4-output-6.png
+                 ── cell-5-output-1.png
+                 ── cell-8-output-1.png
+                 ── cell-9-output-1.png
+         ── site_libs
+             ── bootstrap
+                 ── bootstrap-icons.css
+                 ── bootstrap-icons.woff
+                 ── bootstrap.min.css
+                 ── bootstrap.min.js
+             ── clipboard
+                 ── clipboard.min.js
+             ── quarto-html
+                 ── anchor.min.js
+                 ── popper.min.js
+                 ── quarto-syntax-highlighting.css
+                 ── quarto.js
+                 ── tippy.css
+                 ── tippy.umd.min.js
+             ── quarto-nav
+                 ── headroom.min.js
+                 ── quarto-nav.js
+             ── quarto-search
+                 ── autocomplete.umd.js
+                 ── fuse.min.js
+                 ── quarto-search.js
+         ── sitemap.xml
+         ── sklegos.html
+     ── imgaug_loader.ipynb
+     ── index.ipynb
+     ── inspector.ipynb
+     ── interactive_show.ipynb
+     ── jupyter_notebook.ipynb
+     ── load_defautls.ipynb
+     ── logging.ipynb
+     ── markups.ipynb
+     ── misc.ipynb
+     ── nbdev.yml
+     ── paths.ipynb
+     ── pdf.ipynb
+     ── registry.ipynb
+     ── report.ipynb
+     ── show.ipynb
+     ── sidebar.yml
+     ── sklegos.ipynb
+     ── test.txt
+     ── test1.txt
+     ── test2.txt
+     ── tmp.csv
+     ── tmp.txt
+ ── assets
+     ── Preamble.csv
+     ── Preamble.png
+     ── avgs0.png
+     ── avgs1.png
+     ── demo.gif
+ ── backups
+     ── testing
+         ── 0000.html
+ ── build
+     ── bdist.macosx-11.1-arm64
+     ── lib
+         ── torch_snippets
+             ── __init__.py
+             ── __module_timing__.py
+             ── _modidx.py
+             ── _nbdev.py
+             ── adapters.py
+             ── bb_utils.py
+             ── bokeh_loader.py
+             ── charts.py
+             ── cli.py
+             ── dates.py
+             ── decorators.py
+             ── fastcores.py
+             ── icecream.py
+             ── imgaug_loader.py
+             ── inspector.py
+             ── interactive_show.py
+             ── ipython.py
+             ── load_defaults.py
+             ── loader.py
+             ── logger.py
+             ── markup.py
+             ── markup2.py
+             ── misc.py
+             ── paths.py
+             ── pdf_loader.py
+             ── registry.py
+             ── s3_loader.py
+             ── scp.py
+             ── sklegos.py
+             ── text_utils.py
+             ── thinc_parser
+                 ── __init__.py
+                 ── parser.py
+             ── tmp.py
+             ── torch_loader.py
+             ── trainer
+                 ── __init__.py
+                 ── capsule.py
+                 ── config.py
+                 ── hooks.py
+                 ── neural_graph.py
+             ── video.py
+             ── zen.py
+ ── changelog.md
+ ── conda
+     ── torch_snippets
+         ── meta.yaml
+ ── debug
+     ── profile_time.txt
+     ── profile_time_202408241027.txt
+ ── dist
+     ── torch_snippets-0.6.0-py3-none-any.whl
+     ── torch_snippets-0.6.0.tar.gz
+ ── docker-compose.yml
+ ── docs
+     ── AttrDict.html
+     ── adapters.html
+     ── bokeh_plotting.html
+     ── bounding_boxes.html
+     ── bounding_boxes_files
+         ── figure-html
+             ── cell-6-output-1.png
+             ── cell-7-output-1.png
+             ── cell-8-output-1.png
+             ── cell-9-output-1.png
+     ── capsule.html
+     ── capsule_files
+         ── figure-html
+             ── cell-5-output-2.png
+     ── charts.html
+     ── charts_files
+         ── figure-html
+             ── cell-7-output-1.png
+     ── config.html
+     ── decorators.html
+     ── imgaug_loader.html
+     ── imgaug_loader_files
+         ── figure-html
+             ── cell-3-output-1.png
+             ── cell-5-output-10.png
+             ── cell-5-output-12.png
+             ── cell-5-output-14.png
+             ── cell-5-output-16.png
+             ── cell-5-output-18.png
+             ── cell-5-output-2.png
+             ── cell-5-output-20.png
+             ── cell-5-output-22.png
+             ── cell-5-output-4.png
+             ── cell-5-output-6.png
+             ── cell-5-output-8.png
+             ── cell-6-output-1.png
+             ── cell-7-output-1.png
+             ── cell-8-output-2.png
+     ── index.html
+     ── inspector.html
+     ── interactive_show.html
+     ── jupyter_notebook.html
+     ── load_defautls.html
+     ── logging.html
+     ── markups.html
+     ── misc.html
+     ── paths.html
+     ── pdf.html
+     ── registry.html
+     ── report.html
+     ── report_files
+         ── figure-html
+             ── cell-3-output-2.png
+             ── cell-5-output-1.png
+     ── robots.txt
+     ── search.json
+     ── show.html
+     ── show_files
+         ── figure-html
+             ── cell-2-output-1.png
+             ── cell-3-output-1.png
+             ── cell-4-output-2.png
+             ── cell-4-output-4.png
+             ── cell-4-output-6.png
+             ── cell-5-output-1.png
+             ── cell-8-output-1.png
+             ── cell-9-output-1.png
+     ── site_libs
+         ── bootstrap
+             ── bootstrap-icons.css
+             ── bootstrap-icons.woff
+             ── bootstrap.min.css
+             ── bootstrap.min.js
+         ── clipboard
+             ── clipboard.min.js
+         ── quarto-html
+             ── anchor.min.js
+             ── popper.min.js
+             ── quarto-syntax-highlighting.css
+             ── quarto.js
+             ── tippy.css
+             ── tippy.umd.min.js
+         ── quarto-nav
+             ── headroom.min.js
+             ── quarto-nav.js
+         ── quarto-search
+             ── autocomplete.umd.js
+             ── fuse.min.js
+             ── quarto-search.js
+     ── sitemap.xml
+     ── sklegos.html
+ ── nbs
+     ── AttrDict.ipynb
+     ── _quarto.yml
+     ── adapters.ipynb
+     ── bokeh_plotting.ipynb
+     ── bounding_boxes.ipynb
+     ── capsule.ipynb
+     ── charts.ipynb
+     ── config.ipynb
+     ── decorators.ipynb
+     ── imgaug_loader.ipynb
+     ── index.ipynb
+     ── inspector.ipynb
+     ── interactive_show.ipynb
+     ── jupyter_notebook.ipynb
+     ── load_defautls.ipynb
+     ── logging.ipynb
+     ── markups.ipynb
+     ── misc.ipynb
+     ── nbdev.yml
+     ── paths.ipynb
+     ── pdf.ipynb
+     ── registry.ipynb
+     ── report.ipynb
+     ── show.ipynb
+     ── sidebar.yml
+     ── sklegos.ipynb
+     ── tmp.csv
+ ── requirements.txt
+ ── scripts.ipynb
+ ── settings.ini
+ ── setup.cfg
+ ── setup.py
+ ── testing.ipynb
+ ── tmp.ini
+ ── torch_snippets
+     ── __init__.py
+     ── __module_timing__.py
+     ── __pycache__
+         ── __init__.cpython-312.pyc
+         ── __module_timing__.cpython-312.pyc
+         ── bb_utils.cpython-312.pyc
+         ── bokeh_loader.cpython-312.pyc
+         ── charts.cpython-312.pyc
+         ── cli.cpython-312.pyc
+         ── dates.cpython-312.pyc
+         ── decorators.cpython-312.pyc
+         ── icecream.cpython-312.pyc
+         ── inspector.cpython-312.pyc
+         ── ipython.cpython-312.pyc
+         ── load_defaults.cpython-312.pyc
+         ── loader.cpython-312.pyc
+         ── logger.cpython-312.pyc
+         ── markup.cpython-312.pyc
+         ── markup2.cpython-312.pyc
+         ── misc.cpython-312.pyc
+         ── paths.cpython-312.pyc
+         ── pdf_loader.cpython-312.pyc
+         ── registry.cpython-312.pyc
+         ── s3_loader.cpython-312.pyc
+         ── torch_loader.cpython-312.pyc
+         ── zen.cpython-312.pyc
+     ── _modidx.py
+     ── _nbdev.py
+     ── adapters.py
+     ── bb_utils.py
+     ── bokeh_loader.py
+     ── charts.py
+     ── cli.py
+     ── dates.py
+     ── decorators.py
+     ── fastcores.py
+     ── icecream.py
+     ── imgaug_loader.py
+     ── inspector.py
+     ── interactive_show.py
+     ── ipython.py
+     ── load_defaults.py
+     ── loader.py
+     ── logger.py
+     ── markup.py
+     ── markup2.py
+     ── misc.py
+     ── paths.py
+     ── pdf_loader.py
+     ── registry.py
+     ── s3_loader.py
+     ── scp.py
+     ── sklegos.py
+     ── text_utils.py
+     ── thinc_parser
+         ── __init__.py
+         ── __pycache__
+             ── __init__.cpython-312.pyc
+             ── parser.cpython-312.pyc
+         ── parser.py
+     ── tmp.py
+     ── torch_loader.py
+     ── trainer
+         ── __init__.py
+         ── __pycache__
+             ── __init__.cpython-312.pyc
+             ── capsule.cpython-312.pyc
+         ── capsule.py
+         ── config.py
+         ── hooks.py
+         ── neural_graph.py
+     ── video.py
+     ── zen.py
+ ── torch_snippets.egg-info
+     ── PKG-INFO
+     ── SOURCES.txt
+     ── dependency_links.txt
+     ── entry_points.txt
+     ── not-zip-safe
+     ── requires.txt
+     ── top_level.txt
+
+61 directories, 348 files
+

+

+

+

+

+

+
folder_structure_to_json

+

+
 folder_structure_to_json (path, output_file=None)

+

+
Creates a JSON file representing the folder structure of the given directory.

+

+

+

+
folder_structure_to_dict

+

+
 folder_structure_to_dict (path)

+

+
Recursively constructs a nested dictionary that represents the folder structure.

+

+
x = P("tmp.txt")
+x.touch()
+x.write_lines([i for i in range(1000)], mode="w")
+lines = x.read_lines()
+assert lines == [f"{i}" for i in range(1000)]
+logger.info(x.size())
+
+x.rm(confirm_prompt=False)

+

+
[08/24/24 11:12:13] INFO     loaded 1000 lines                                                                                                   2901855681.py:read_lines:13
+

+

+

+
                    INFO     3 KB                                                                                                                   1033976698.py:<module>:6
+

+

+

+

+

+

+
loaddill

+

+
 loaddill (fpath)

+

+
Load a python object from a dill file

+

+

+

+
dumpdill

+

+
 dumpdill (obj, fpath, silent=False, message='Dumped object of size
+           ≈{fsize} @ "{fpath}" in {dumptime:.2e} seconds')

+

+
Dump a python object as a dill file (better replacement to pickle)

+

+
p = P("test.tmp")
+dumpdill([1, 2, 3], p)
+y = loaddill(p)
+p.rm(confirm_prompt=False)
+assert y == [1, 2, 3]

+

+
                    INFO     Dumped object of size ≈0 KB @ "test.tmp" in 4.11e-04 seconds                                                            554288780.py:<module>:2
+

+

+

+
+
+

+

+
+
 
+
+
 
+
+
+
+
+
\ No newline at end of file
diff --git a/_proc/docs/pdf.html b/_proc/docs/pdf.html
new file mode 100644
index 0000000..464c77f
--- /dev/null
+++ b/_proc/docs/pdf.html
@@ -0,0 +1,725 @@
+
+
+
+
+
+
+
+
+
+PDF – torch_snippets
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

+  

+    
+  
+

+
+

+
+  
+

+
+    
+
+

+
+

+

+
PDF

+

+
+
+
+

+
+    
+  
+    
+  

+  
+
+
+

+
+
+
+

+

+
PDF

+

+
 PDF (path, dfs=None, dpi=150)

+

+
Load a PDF file from path as a list of images Use show function to see the images WIP

+
+
+

+
+
 
+
+
 
+
+
+
+
+
\ No newline at end of file
diff --git a/_proc/docs/registry.html b/_proc/docs/registry.html
new file mode 100644
index 0000000..99a5ef8
--- /dev/null
+++ b/_proc/docs/registry.html
@@ -0,0 +1,909 @@
+
+
+
+
+
+
+
+
+
+Registry – torch_snippets
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

+  

+    
+  
+

+
+

+
+  
+

+
+    
+
+

+
+

+

+
Registry

+

+
+
+
+

+
+    
+  
+    
+  

+  
+
+
+

+
+
+
+
Suppose you have a file called config.ini like so…

+

+
!cat /tmp/config.ini

+

+

+[META]
+version = 0.0.1
+name = mnist
+root = /home/me/projects/${META.name}
+description = This is a sample
+    config file with a multiline
+    description. These are useful for
+    project descriptions/changelog/devnotes etc...
+
+[Data]
+source = https://files.fast.ai/data/examples/mnist_tiny.tgz
+root = ${META.root}/data/
+
+[misc]
+x = 1
+y = 20
+z = float(${x}*${y}**2)
+a = ['hello','hi','how','are','you', ${x}*${z}*${y}]
+b = {"hi": 1, "hello": 2}
+
+[load]
+    [load.test]
+    @load = print_root_location
+    root = ${Data.root}
+    
+    [load.csv]
+    @load = load_csv_function
+    root = ${Data.root}
+    
+    [load.json]
+    @load = load_json_class
+    root = ${Data.root}
+    
+    
+

+

+

+
You can load it up as an AttrDict

+

+
config = parse("/tmp/config.ini")
+assert config.META.version == "0.0.1"
+assert config.META.root == "/home/me/projects/mnist"
+assert isinstance(config.misc.b, AttrDict), type(config.project.data.b)
+assert isinstance(config.misc.a, L)

+

+
Notice, how the ${} variables got resolved.

+Not just that, the varaible z got computed on the fly.

+Not just that, some of the variables like list and dict got resolved into their respective python data structures.

+

+
config.pretty()

+

+
{
+    "Data": {
+        "root": "/home/me/projects/mnist/data/",
+        "source": "https://files.fast.ai/data/examples/mnist_tiny.tgz"
+    },
+    "META": {
+        "description": "This is a sample\nconfig file with a multiline\ndescription. These are useful for\nproject 
+descriptions/changelog/devnotes etc...",
+        "name": "mnist",
+        "root": "/home/me/projects/mnist",
+        "version": "0.0.1"
+    },
+    "load": {
+        "csv": {
+            "@load": null,
+            "root": "/home/me/projects/mnist/data/"
+        },
+        "json": {
+            "@load": "load_json_class",
+            "root": "/home/me/projects/mnist/data/"
+        },
+        "test": {
+            "@load": "print_root_location",
+            "root": "/home/me/projects/mnist/data/"
+        }
+    },
+    "misc": {
+        "a": [
+            "hello",
+            "hi",
+            "how",
+            "are",
+            "you",
+            8000.0
+        ],
+        "b": {
+            "hello": 2,
+            "hi": 1
+        },
+        "x": 1,
+        "y": 20,
+        "z": 400.0
+    }
+}
+

+

+

+

+
print(config.META.description)

+

+
This is a sample
+config file with a multiline
+description. These are useful for
+project descriptions/changelog/devnotes etc...

+

+

+
You can also register/call python functions/callables/classes/objects to strings by running

+

+
registry.create("load")
+
+
+@registry.load.register("print_root_location")
+def printer(root):
+    return root
+
+
+@registry.load.register("load_csv_function")
+def _load_csv_function(root):
+    def load_csv_function(file):
+        return f"Loading file from {root}/{file}"
+
+    return load_csv_function
+
+
+@registry.load.register("load_json_class")
+class JsonLoader:
+    def __init__(self, root):
+        self.root = root
+
+    def __call__(self, file):
+        assert file.endswith("json")
+        return f"Loading file from {self.root}/{file}"

+

+
… and resolve them on parse

+

+
config = parse_and_resolve("/tmp/config.ini")

+

+

+
config.load.test

+

+
'/home/me/projects/mnist/data/'

+

+

+

+
config.load.csv(file="file.csv")

+

+
'Loading file from /home/me/projects/mnist/data//file.csv'

+

+

+

+
config.load.json(file="file.json")

+

+
'Loading file from /home/me/projects/mnist/data//file.json'

+

+

+
+
+
+
 
+
+
 
+
+
+
+
+
\ No newline at end of file
diff --git a/_proc/docs/report.html b/_proc/docs/report.html
new file mode 100644
index 0000000..f6c2351
--- /dev/null
+++ b/_proc/docs/report.html
@@ -0,0 +1,812 @@
+
+
+
+
+
+
+
+
+
+report – torch_snippets
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

+  

+    
+  
+

+
+

+
+  
+

+
+    
+
+

+
+
+
+
+

+
Report

+
+

+
from torch_snippets.torch_loader import Report
+import numpy as np
+import time

+

+

+
n_epochs = 3
+report = Report(n_epochs)
+random_walker1 = 0
+random_walker2 = 0
+
+for epoch in range(n_epochs):
+    for ix in range(1000):
+        report.record(
+            pos=epoch + (ix + 1) / 1000,
+            loss=random_walker1,
+            val_loss=random_walker2,
+            end="\r",
+        )
+        random_walker1 += np.random.normal()
+        random_walker2 += np.random.normal()
+        time.sleep(0.001)
+    report.report_avgs(epoch + 1)
+
+report.plot()

+

+
EPOCH: 1.000    loss: -6.503    val_loss: -3.093    (1.19s - 2.38s remaining)))
+EPOCH: 2.000    loss: 48.754    val_loss: -6.265    (2.37s - 1.18s remaining))
+EPOCH: 3.000    loss: 38.115    val_loss: -29.732   (3.54s - 0.00s remaining)

+

+

+

+

+

+

+

+

+

+

+
n_epochs = 5
+report = Report(n_epochs, old_report=report)
+
+for epoch in range(n_epochs):
+    for ix in range(1000):
+        report.record(
+            pos=epoch + (ix + 1) / 1000,
+            loss=random_walker1,
+            val_loss=random_walker2,
+            end="\r",
+        )
+        random_walker1 += np.random.normal()
+        random_walker2 += np.random.normal()
+        time.sleep(0.001)
+    report.report_avgs(epoch + 1)

+

+
EPOCH: 1.000    loss: 29.338    val_loss: -74.955   (1.17s - 4.70s remaining))
+EPOCH: 2.000    loss: 0.340 val_loss: -110.763  (2.35s - 3.52s remaining)))
+EPOCH: 3.000    loss: 30.617    val_loss: -84.599   (3.51s - 2.34s remaining))
+EPOCH: 4.000    loss: 34.309    val_loss: -27.520   (4.68s - 1.17s remaining)
+EPOCH: 5.000    loss: 15.252    val_loss: -46.033   (5.85s - 0.00s remaining)

+

+

+

+
import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots()
+ax.vlines(0, -100, 100, colors=["red"])
+report.plot(ax=ax)

+

+

+

+

+

+

+

+

+
+
+

+
+
 
+
+
 
+
+
+
+
+
\ No newline at end of file
diff --git a/_proc/docs/report_files/figure-html/cell-3-output-2.png b/_proc/docs/report_files/figure-html/cell-3-output-2.png
new file mode 100644
index 0000000..497c483
Binary files /dev/null and b/_proc/docs/report_files/figure-html/cell-3-output-2.png differ
diff --git a/_proc/docs/report_files/figure-html/cell-5-output-1.png b/_proc/docs/report_files/figure-html/cell-5-output-1.png
new file mode 100644
index 0000000..fee45f8
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diff --git a/_proc/docs/robots.txt b/_proc/docs/robots.txt
new file mode 100644
index 0000000..9d2e34d
--- /dev/null
+++ b/_proc/docs/robots.txt
@@ -0,0 +1 @@
+Sitemap: https://sizhky.github.io/torch_snippets/sitemap.xml
diff --git a/_proc/docs/search.json b/_proc/docs/search.json
new file mode 100644
index 0000000..15f7266
--- /dev/null
+++ b/_proc/docs/search.json
@@ -0,0 +1,582 @@
+[
+  {
+    "objectID": "imgaug_loader.html",
+    "href": "imgaug_loader.html",
+    "title": "Imgaug",
+    "section": "",
+    "text": "wrappers for common imgaug functions, that accept image and ocr data frame of bounding boxes\n\n\nfrom torch_snippets.loader import read, pd, show\n\nIM = read(\"/Users/yeshwanth.y/code/torch_snippets/assets/Preamble.png\")\nDF = pd.read_csv(\"/Users/yeshwanth.y/code/torch_snippets/assets/Preamble.csv\")\nDF = to_relative(DF, *IM.shape[:2])\nDF.head()\n\n\n\n\n\n\n\n\nx\ny\nX\nY\ntext\nblock_id\n\n\n\n\n0\n0.249538\n0.253501\n0.569316\n0.305322\nConstITUtIO\n0\n\n\n1\n0.288355\n0.369748\n0.401109\n0.397759\nNLTHE\n1\n\n\n2\n0.402957\n0.369748\n0.510166\n0.397759\nPEOPLE\n1\n\n\n3\n0.493530\n0.371148\n0.545287\n0.394958\nOF\n1\n\n\n4\n0.548983\n0.369748\n0.630314\n0.397759\nINDIA,\n1\n\n\n\n\n\n\n\n\n\n\nim = IM.copy()\nim = rescale(im, sz=(400, 600))\nshow(im, sz=5)\n\n\n\n\n\n\n\n\n\ndf = DF.copy()\nim = IM.copy()\nim, df = rescale(im, df, (400, 600))\nshow(im, df=df, sz=5)\ndf.head()\n\n\n---------------------------------------------------------------------------\nValueError                                Traceback (most recent call last)\n/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb Cell 8 in <cell line: 3>()\n      <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=0'>1</a> df = DF.copy()\n      <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=1'>2</a> im = IM.copy()\n----> <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=2'>3</a> im, df = rescale(im, df, (400, 600))\n      <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=3'>4</a> show(im, df=df, sz=5)\n      <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=4'>5</a> df.head()\n\n/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb Cell 8 in rescale(im, bbs, sz)\n    <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=110'>111</a> H, W = get_size(sz, h, w)\n    <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=111'>112</a> aug = iaa.Resize({\"height\": H, \"width\": W})\n--> <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=112'>113</a> im, bbs = do(im, bbs, aug)\n    <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=113'>114</a> if to_pil:\n    <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=114'>115</a>     im = PIL.Image.fromarray(im)\n\n/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb Cell 8 in do(img, bbs, aug, cval)\n     <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=41'>42</a>         __df = combine_xyXY_to_bb(__df)\n     <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=42'>43</a>     bbs = __df\n---> <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=43'>44</a> if bbs == []:\n     <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=44'>45</a>     return img, []\n     <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=45'>46</a> return img, bbs\n\nFile ~/miniconda3/lib/python3.9/site-packages/pandas/core/ops/__init__.py:705, in _comp_method_FRAME.<locals>.f(self, other)\n    701 @Appender(f\"Wrapper for comparison method {op_name}\")\n    702 def f(self, other):\n    703     axis = 1  # only relevant for Series other case\n--> 705     self, other = _align_method_FRAME(self, other, axis, level=None, flex=False)\n    707     # See GH#4537 for discussion of scalar op behavior\n    708     new_data = dispatch_to_series(self, other, op, axis=axis)\n\nFile ~/miniconda3/lib/python3.9/site-packages/pandas/core/ops/__init__.py:503, in _align_method_FRAME(left, right, axis, flex, level)\n    496         raise ValueError(\n    497             \"Unable to coerce to Series/DataFrame, \"\n    498             f\"dimension must be <= 2: {right.shape}\"\n    499         )\n    501 elif is_list_like(right) and not isinstance(right, (ABCSeries, ABCDataFrame)):\n    502     # GH17901\n--> 503     right = to_series(right)\n    505 if flex is not None and isinstance(right, ABCDataFrame):\n    506     if not left._indexed_same(right):\n\nFile ~/miniconda3/lib/python3.9/site-packages/pandas/core/ops/__init__.py:465, in _align_method_FRAME.<locals>.to_series(right)\n    463 else:\n    464     if len(left.columns) != len(right):\n--> 465         raise ValueError(\n    466             msg.format(req_len=len(left.columns), given_len=len(right))\n    467         )\n    468     right = left._constructor_sliced(right, index=left.columns)\n    469 return right\n\nValueError: Unable to coerce to Series, length must be 6: given 0\n\n\n\n\n\n\n\nfor i in range(11):\n    angle = (i - 5) * 5\n    print(angle)\n    im = rotate(IM, angle=angle)\n    show(im, sz=1)\n\n-25\n\n\n\n\n\n\n\n\n\n-20\n\n\n\n\n\n\n\n\n\n-15\n\n\n\n\n\n\n\n\n\n-10\n\n\n\n\n\n\n\n\n\n-5\n\n\n\n\n\n\n\n\n\n0\n\n\n\n\n\n\n\n\n\n5\n\n\n\n\n\n\n\n\n\n10\n\n\n\n\n\n\n\n\n\n15\n\n\n\n\n\n\n\n\n\n20\n\n\n\n\n\n\n\n\n\n25\n\n\n\n\n\n\n\n\n\n\ndf = DF.copy()\nim = IM.copy()\nim, df = rotate(im, df, 90)\nshow(im, df=df, sz=20)\nprint(type(im))\ndf.head()\n\n\n\n\n\n\n\n\n<class 'numpy.ndarray'>\n\n\n\n\n\n\n\n\n\ntext\nblock_id\nx\ny\nX\nY\n\n\n\n\n0\nConstITUtIO\n0\n0.693277\n0.249538\n0.745098\n0.569316\n\n\n1\nNLTHE\n1\n0.600840\n0.288355\n0.628852\n0.401109\n\n\n2\nPEOPLE\n1\n0.600840\n0.402957\n0.628852\n0.510166\n\n\n3\nOF\n1\n0.603641\n0.493530\n0.627451\n0.545287\n\n\n4\nINDIA,\n1\n0.600840\n0.548983\n0.628852\n0.630314\n\n\n\n\n\n\n\n\n\n\n\ndf = DF.copy()\nim = IM.copy()\nim, df = pad(im, df, deltas=(90, 90), cval=0)\nshow(im, df=df, sz=20)\nprint(type(im))\ndf.head()\n\n\n\n\n\n\n\n\n<class 'numpy.ndarray'>\n\n\n\n\n\n\n\n\n\ntext\nblock_id\nx\ny\nX\nY\n\n\n\n\n0\nConstITUtIO\n0\n0.310536\n0.302521\n0.550832\n0.343137\n\n\n1\nNLTHE\n1\n0.340111\n0.394958\n0.425139\n0.417367\n\n\n2\nPEOPLE\n1\n0.426987\n0.394958\n0.506470\n0.417367\n\n\n3\nOF\n1\n0.493530\n0.396359\n0.532348\n0.415966\n\n\n4\nINDIA,\n1\n0.536044\n0.394958\n0.597043\n0.417367\n\n\n\n\n\n\n\n\n\n\nAll functions will work with data frames that contain either of absolute/relative coordinates, and will preserve the image type (np.ndarray or PIL.Image.Image) too\n\ndf = DF.copy()\nim = IM.copy()\nheight, width = im.shape\nshow(df.head())\nim = PIL.Image.fromarray(im)\nim, df = rotate(im, df, 45, cval=127)\nim, df = pad(im, df, deltas=(200, 200), cval=0)\nim, df = rescale(im, df, sz=(300, 300))\nshow(im, df=df, sz=20)\nprint(type(im))\ndf.head()\n\n\n\n\n\n\n\n\nx\ny\nX\nY\ntext\nblock_id\n\n\n\n\n0\n0.249538\n0.253501\n0.569316\n0.305322\nConstITUtIO\n0\n\n\n1\n0.288355\n0.369748\n0.401109\n0.397759\nNLTHE\n1\n\n\n2\n0.402957\n0.369748\n0.510166\n0.397759\nPEOPLE\n1\n\n\n3\n0.493530\n0.371148\n0.545287\n0.394958\nOF\n1\n\n\n4\n0.548983\n0.369748\n0.630314\n0.397759\nINDIA,\n1\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<class 'PIL.Image.Image'>\n\n\n\n\n\n\n\n\n\ntext\nblock_id\nx\ny\nX\nY\n\n\n\n\n0\nConstITUtIO\n0\n0.500000\n0.326667\n0.613333\n0.440000\n\n\n1\nNLTHE\n1\n0.473333\n0.383333\n0.516667\n0.426667\n\n\n2\nPEOPLE\n1\n0.506667\n0.416667\n0.550000\n0.460000\n\n\n3\nOF\n1\n0.536667\n0.446667\n0.560000\n0.470000\n\n\n4\nINDIA,\n1\n0.550000\n0.460000\n0.586667\n0.493333",
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+    "text": "im = IM.copy()\nim = rescale(im, sz=(400, 600))\nshow(im, sz=5)\n\n\n\n\n\n\n\n\n\ndf = DF.copy()\nim = IM.copy()\nim, df = rescale(im, df, (400, 600))\nshow(im, df=df, sz=5)\ndf.head()\n\n\n---------------------------------------------------------------------------\nValueError                                Traceback (most recent call last)\n/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb Cell 8 in <cell line: 3>()\n      <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=0'>1</a> df = DF.copy()\n      <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=1'>2</a> im = IM.copy()\n----> <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=2'>3</a> im, df = rescale(im, df, (400, 600))\n      <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=3'>4</a> show(im, df=df, sz=5)\n      <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=4'>5</a> df.head()\n\n/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb Cell 8 in rescale(im, bbs, sz)\n    <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=110'>111</a> H, W = get_size(sz, h, w)\n    <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=111'>112</a> aug = iaa.Resize({\"height\": H, \"width\": W})\n--> <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=112'>113</a> im, bbs = do(im, bbs, aug)\n    <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=113'>114</a> if to_pil:\n    <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=114'>115</a>     im = PIL.Image.fromarray(im)\n\n/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb Cell 8 in do(img, bbs, aug, cval)\n     <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=41'>42</a>         __df = combine_xyXY_to_bb(__df)\n     <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=42'>43</a>     bbs = __df\n---> <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=43'>44</a> if bbs == []:\n     <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=44'>45</a>     return img, []\n     <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=45'>46</a> return img, bbs\n\nFile ~/miniconda3/lib/python3.9/site-packages/pandas/core/ops/__init__.py:705, in _comp_method_FRAME.<locals>.f(self, other)\n    701 @Appender(f\"Wrapper for comparison method {op_name}\")\n    702 def f(self, other):\n    703     axis = 1  # only relevant for Series other case\n--> 705     self, other = _align_method_FRAME(self, other, axis, level=None, flex=False)\n    707     # See GH#4537 for discussion of scalar op behavior\n    708     new_data = dispatch_to_series(self, other, op, axis=axis)\n\nFile ~/miniconda3/lib/python3.9/site-packages/pandas/core/ops/__init__.py:503, in _align_method_FRAME(left, right, axis, flex, level)\n    496         raise ValueError(\n    497             \"Unable to coerce to Series/DataFrame, \"\n    498             f\"dimension must be <= 2: {right.shape}\"\n    499         )\n    501 elif is_list_like(right) and not isinstance(right, (ABCSeries, ABCDataFrame)):\n    502     # GH17901\n--> 503     right = to_series(right)\n    505 if flex is not None and isinstance(right, ABCDataFrame):\n    506     if not left._indexed_same(right):\n\nFile ~/miniconda3/lib/python3.9/site-packages/pandas/core/ops/__init__.py:465, in _align_method_FRAME.<locals>.to_series(right)\n    463 else:\n    464     if len(left.columns) != len(right):\n--> 465         raise ValueError(\n    466             msg.format(req_len=len(left.columns), given_len=len(right))\n    467         )\n    468     right = left._constructor_sliced(right, index=left.columns)\n    469 return right\n\nValueError: Unable to coerce to Series, length must be 6: given 0",
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+    "text": "for i in range(11):\n    angle = (i - 5) * 5\n    print(angle)\n    im = rotate(IM, angle=angle)\n    show(im, sz=1)\n\n-25\n\n\n\n\n\n\n\n\n\n-20\n\n\n\n\n\n\n\n\n\n-15\n\n\n\n\n\n\n\n\n\n-10\n\n\n\n\n\n\n\n\n\n-5\n\n\n\n\n\n\n\n\n\n0\n\n\n\n\n\n\n\n\n\n5\n\n\n\n\n\n\n\n\n\n10\n\n\n\n\n\n\n\n\n\n15\n\n\n\n\n\n\n\n\n\n20\n\n\n\n\n\n\n\n\n\n25\n\n\n\n\n\n\n\n\n\n\ndf = DF.copy()\nim = IM.copy()\nim, df = rotate(im, df, 90)\nshow(im, df=df, sz=20)\nprint(type(im))\ndf.head()\n\n\n\n\n\n\n\n\n<class 'numpy.ndarray'>\n\n\n\n\n\n\n\n\n\ntext\nblock_id\nx\ny\nX\nY\n\n\n\n\n0\nConstITUtIO\n0\n0.693277\n0.249538\n0.745098\n0.569316\n\n\n1\nNLTHE\n1\n0.600840\n0.288355\n0.628852\n0.401109\n\n\n2\nPEOPLE\n1\n0.600840\n0.402957\n0.628852\n0.510166\n\n\n3\nOF\n1\n0.603641\n0.493530\n0.627451\n0.545287\n\n\n4\nINDIA,\n1\n0.600840\n0.548983\n0.628852\n0.630314",
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+    "text": "df = DF.copy()\nim = IM.copy()\nim, df = pad(im, df, deltas=(90, 90), cval=0)\nshow(im, df=df, sz=20)\nprint(type(im))\ndf.head()\n\n\n\n\n\n\n\n\n<class 'numpy.ndarray'>\n\n\n\n\n\n\n\n\n\ntext\nblock_id\nx\ny\nX\nY\n\n\n\n\n0\nConstITUtIO\n0\n0.310536\n0.302521\n0.550832\n0.343137\n\n\n1\nNLTHE\n1\n0.340111\n0.394958\n0.425139\n0.417367\n\n\n2\nPEOPLE\n1\n0.426987\n0.394958\n0.506470\n0.417367\n\n\n3\nOF\n1\n0.493530\n0.396359\n0.532348\n0.415966\n\n\n4\nINDIA,\n1\n0.536044\n0.394958\n0.597043\n0.417367",
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+    "text": "All functions will work with data frames that contain either of absolute/relative coordinates, and will preserve the image type (np.ndarray or PIL.Image.Image) too\n\ndf = DF.copy()\nim = IM.copy()\nheight, width = im.shape\nshow(df.head())\nim = PIL.Image.fromarray(im)\nim, df = rotate(im, df, 45, cval=127)\nim, df = pad(im, df, deltas=(200, 200), cval=0)\nim, df = rescale(im, df, sz=(300, 300))\nshow(im, df=df, sz=20)\nprint(type(im))\ndf.head()\n\n\n\n\n\n\n\n\nx\ny\nX\nY\ntext\nblock_id\n\n\n\n\n0\n0.249538\n0.253501\n0.569316\n0.305322\nConstITUtIO\n0\n\n\n1\n0.288355\n0.369748\n0.401109\n0.397759\nNLTHE\n1\n\n\n2\n0.402957\n0.369748\n0.510166\n0.397759\nPEOPLE\n1\n\n\n3\n0.493530\n0.371148\n0.545287\n0.394958\nOF\n1\n\n\n4\n0.548983\n0.369748\n0.630314\n0.397759\nINDIA,\n1\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<class 'PIL.Image.Image'>\n\n\n\n\n\n\n\n\n\ntext\nblock_id\nx\ny\nX\nY\n\n\n\n\n0\nConstITUtIO\n0\n0.500000\n0.326667\n0.613333\n0.440000\n\n\n1\nNLTHE\n1\n0.473333\n0.383333\n0.516667\n0.426667\n\n\n2\nPEOPLE\n1\n0.506667\n0.416667\n0.550000\n0.460000\n\n\n3\nOF\n1\n0.536667\n0.446667\n0.560000\n0.470000\n\n\n4\nINDIA,\n1\n0.550000\n0.460000\n0.586667\n0.493333",
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+    "title": "Rich Logging and printing",
+    "section": "",
+    "text": "Logging is packed in these 5 functions\n\nreset_logger(\"trace\", console_width=80)\nTrace(\"Testing {1,2,3}\")\nDebug(\"TESTING {1,2,3}\")\nInfo(\"TESTING {1,2,3}\")\nWarn(\"TESTING {1,2,3}\")\nExcep(\"TESTING {1,2,3}\")\n\n[08/24/24 12:16:05] TRACE    Testing {1,2,3}            1838145784.py:<module>:2\n\n\n\n                    DEBUG    TESTING {1,2,3}            1838145784.py:<module>:3\n\n\n\n                    INFO     TESTING {1,2,3}            1838145784.py:<module>:4\n\n\n\n                    WARNING  TESTING {1,2,3}            1838145784.py:<module>:5\n\n\n\n                    ERROR    TESTING {1,2,3}            1838145784.py:<module>:6\n                             NoneType: None                                     \n                                                                                \n                                                                                \n\n\n\nExcep will also print a traceback\n\ndef do():\n    try:\n        1 / 0\n    except Exception as e:\n        Excep(e)\n\ndef do2():\n    do()\n\ndo2()\n\n[08/24/24 12:16:07] ERROR    division by zero                 1923207781.py:do:5\n                             ZeroDivisionError: division by                     \n                             zero                                               \n                                                                                \n                             ╭─ Traceback (most recent call─╮                   \n                             │ in do:3                      │                   \n                             │                              │                   \n                             │    1 def do():               │                   \n                             │    2 │   try:                │                   \n                             │ ❱  3 │   │   1 / 0           │                   \n                             │    4 │   except Exception as │                   \n                             │    5 │   │   Excep(e)        │                   \n                             │    6                         │                   \n                             ╰──────────────────────────────╯                   \n                             ZeroDivisionError: division by                     \n                             zero                                               \n\n\n\nAll logging functions have an optional depth that will be helpful to raise the context level to outer functions\n\ndef do():\n    Info('In do')\n\ndo()\n\n[08/24/24 12:16:15] INFO     In do                             809811040.py:do:2\n\n\n\n\ndef do2():\n    Info('Log will still say it is from `do2` now, to the right of log print')\n\ndef do():\n    do2()\n\ndo()\n\n[08/24/24 12:17:13] INFO     Log will still say it is from   2099333876.py:do2:2\n                             `do2` now, to the right of log                     \n                             print                                              \n\n\n\n\ndef do2():\n    Info('But now, log will still say it is from do', depth=1)\n\ndef do():\n    do2()\n\ndo()\n\n[08/24/24 12:17:25] INFO     But now, log will still say it   4054019042.py:do:5\n                             is from do",
+    "crumbs": [
+      "Rich Logging and printing"
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+  },
+  {
+    "objectID": "logging.html#ez-logging",
+    "href": "logging.html#ez-logging",
+    "title": "Rich Logging and printing",
+    "section": "",
+    "text": "Logging is packed in these 5 functions\n\nreset_logger(\"trace\", console_width=80)\nTrace(\"Testing {1,2,3}\")\nDebug(\"TESTING {1,2,3}\")\nInfo(\"TESTING {1,2,3}\")\nWarn(\"TESTING {1,2,3}\")\nExcep(\"TESTING {1,2,3}\")\n\n[08/24/24 12:16:05] TRACE    Testing {1,2,3}            1838145784.py:<module>:2\n\n\n\n                    DEBUG    TESTING {1,2,3}            1838145784.py:<module>:3\n\n\n\n                    INFO     TESTING {1,2,3}            1838145784.py:<module>:4\n\n\n\n                    WARNING  TESTING {1,2,3}            1838145784.py:<module>:5\n\n\n\n                    ERROR    TESTING {1,2,3}            1838145784.py:<module>:6\n                             NoneType: None                                     \n                                                                                \n                                                                                \n\n\n\nExcep will also print a traceback\n\ndef do():\n    try:\n        1 / 0\n    except Exception as e:\n        Excep(e)\n\ndef do2():\n    do()\n\ndo2()\n\n[08/24/24 12:16:07] ERROR    division by zero                 1923207781.py:do:5\n                             ZeroDivisionError: division by                     \n                             zero                                               \n                                                                                \n                             ╭─ Traceback (most recent call─╮                   \n                             │ in do:3                      │                   \n                             │                              │                   \n                             │    1 def do():               │                   \n                             │    2 │   try:                │                   \n                             │ ❱  3 │   │   1 / 0           │                   \n                             │    4 │   except Exception as │                   \n                             │    5 │   │   Excep(e)        │                   \n                             │    6                         │                   \n                             ╰──────────────────────────────╯                   \n                             ZeroDivisionError: division by                     \n                             zero                                               \n\n\n\nAll logging functions have an optional depth that will be helpful to raise the context level to outer functions\n\ndef do():\n    Info('In do')\n\ndo()\n\n[08/24/24 12:16:15] INFO     In do                             809811040.py:do:2\n\n\n\n\ndef do2():\n    Info('Log will still say it is from `do2` now, to the right of log print')\n\ndef do():\n    do2()\n\ndo()\n\n[08/24/24 12:17:13] INFO     Log will still say it is from   2099333876.py:do2:2\n                             `do2` now, to the right of log                     \n                             print                                              \n\n\n\n\ndef do2():\n    Info('But now, log will still say it is from do', depth=1)\n\ndef do():\n    do2()\n\ndo()\n\n[08/24/24 12:17:25] INFO     But now, log will still say it   4054019042.py:do:5\n                             is from do",
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+      "Rich Logging and printing"
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+  },
+  {
+    "objectID": "logging.html#logging-level-and-context",
+    "href": "logging.html#logging-level-and-context",
+    "title": "Rich Logging and printing",
+    "section": "Logging Level and context",
+    "text": "Logging Level and context\n\n\nin_logger_mode\n\n in_logger_mode (level:str)\n\nreturn’s T/F, checking if logger is in a specific mode or not\n\n\n\nlogger_mode\n\n logger_mode (level)\n\ntemporarily, using with context, set the level to something else\n\n\n\nget_logger_level\n\n get_logger_level ()\n\nget the current logger’s level\nLet’s log every level in the do function below. We can control what we need to log from outside the function’s context by\nusing with <level>_model():\n\ndef do():\n    Trace(0)\n    Debug(1)\n    Info(2)\n    Warn(3)\n    Excep(4)\n\n\ndef line(x):\n    sep = \"=\" * 20\n    print(f\"{sep}{x}{sep}\")\n    print(f\"{in_excep_mode()=}\")\n    print(f\"{in_warn_mode()=}\")\n    print(f\"{in_info_mode()=}\")\n    print(f\"{in_debug_mode()=}\")\n    print(f\"{in_trace_mode()=}\")\n\n\nreset_logger()\n\nwith excep_mode():\n    line(\"Excep mode\")\n    do()\n\nwith warn_mode():\n    line(\"Warn mode\")\n    do()\n\nwith info_mode():\n    line(\"Info mode\")\n    do()\n\nwith debug_mode():\n    line(\"Debug mode\")\n    do()\n\nwith trace_mode():\n    line(\"Trace mode\")\n    do()\n\n====================Excep mode====================\nin_excep_mode()=True\nin_warn_mode()=False\nin_info_mode()=False\nin_debug_mode()=False\nin_trace_mode()=False\n\n\n[08/24/24 12:13:08] ERROR    4                                                                                                                            3432048509.py:do:6\n                             NoneType: None                                                                                                                                 \n                                                                                                                                                                            \n                                                                                                                                                                            \n\n\n\n====================Warn mode====================\nin_excep_mode()=False\nin_warn_mode()=True\nin_info_mode()=False\nin_debug_mode()=False\nin_trace_mode()=False\n\n\n[08/24/24 12:13:08] WARNING  3                                                                                                                            3432048509.py:do:5\n\n\n\n                    ERROR    4                                                                                                                            3432048509.py:do:6\n                             NoneType: None                                                                                                                                 \n                                                                                                                                                                            \n                                                                                                                                                                            \n\n\n\n====================Info mode====================\nin_excep_mode()=False\nin_warn_mode()=False\nin_info_mode()=True\nin_debug_mode()=False\nin_trace_mode()=False\n\n\n[08/24/24 12:13:08] INFO     2                                                                                                                            3432048509.py:do:4\n\n\n\n                    WARNING  3                                                                                                                            3432048509.py:do:5\n\n\n\n                    ERROR    4                                                                                                                            3432048509.py:do:6\n                             NoneType: None                                                                                                                                 \n                                                                                                                                                                            \n                                                                                                                                                                            \n\n\n\n====================Debug mode====================\nin_excep_mode()=False\nin_warn_mode()=False\nin_info_mode()=False\nin_debug_mode()=True\nin_trace_mode()=False\n\n\n[08/24/24 12:13:08] DEBUG    1                                                                                                                            3432048509.py:do:3\n\n\n\n                    INFO     2                                                                                                                            3432048509.py:do:4\n\n\n\n                    WARNING  3                                                                                                                            3432048509.py:do:5\n\n\n\n                    ERROR    4                                                                                                                            3432048509.py:do:6\n                             NoneType: None                                                                                                                                 \n                                                                                                                                                                            \n                                                                                                                                                                            \n\n\n\n====================Trace mode====================\nin_excep_mode()=False\nin_warn_mode()=False\nin_info_mode()=False\nin_debug_mode()=False\nin_trace_mode()=True\n\n\n[08/24/24 12:13:08] TRACE    0                                                                                                                            3432048509.py:do:2\n\n\n\n                    DEBUG    1                                                                                                                            3432048509.py:do:3\n\n\n\n                    INFO     2                                                                                                                            3432048509.py:do:4\n\n\n\n                    WARNING  3                                                                                                                            3432048509.py:do:5\n\n\n\n                    ERROR    4                                                                                                                            3432048509.py:do:6\n                             NoneType: None                                                                                                                                 \n                                                                                                                                                                            \n                                                                                                                                                                            \n\n\n\nThe in_<level>_mode gives an additional layer of control, to be used for debugging dynamically. Let’s say, you want to show an image (for the sake of debugging)\n\ndef do(im_path):\n    from torch_snippets import show, read\n    im = read(im_path)\n    show(im, sz=3)\n    print(im.mean())\n\ndo('assets/Preamble.png')\n\n\n\n\n\n\n\n\n145.5542982442515\n\n\nBut now you are happy with your code and don’t want the show, say the code is going to production. A common way out is to just comment that line\n\ndef do(im_path):\n    from torch_snippets import show, read\n    im = read(im_path)\n    # show(im, sz=3) # line is commented, but will need to be re-uncommented any time it needs debugging\n    print(im.mean())\n\ndo('assets/Preamble.png')\n\n145.5542982442515\n\n\nBut if you want to re-check, it’s a pain to again uncomment. Not to mention this method is not scalable to 100s of lines of code. The simple way to deal with such transient code that needs to activate only when you want it to, is to enclose in an if in_<level>_mode conditional like so\n\ndef do(im_path):\n    from torch_snippets import show, read\n    im = read(im_path)\n    if in_debug_mode():\n        show(im, sz=3)\n    print(im.mean())\n\ndo('assets/Preamble.png')\n\n145.5542982442515\n\n\nThis way, you can always activate the show by calling do with a temporary with_debug_mode context\n\nwith debug_mode():\n    do('assets/Preamble.png')\n\n\n\n\n\n\n\n\n145.5542982442515",
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+  },
+  {
+    "objectID": "logging.html#notify-waiting",
+    "href": "logging.html#notify-waiting",
+    "title": "Rich Logging and printing",
+    "section": "Notify Waiting",
+    "text": "Notify Waiting\n\nreset_logger(console_width=100)\n\nwith trace_mode():\n    with notify_waiting(\"Downloading\"):\n        time.sleep(10)\n\nwith notify_waiting(\"Downloading\", spinner=\"guess_the_movie\"):\n    time.sleep(10)\n\nwith notify_waiting(\"One more message\", spinner=\"earth\"):\n    time.sleep(3)\n\n[08/24/24 12:36:51] TRACE    Available Spinners: dict_keys(['dots', 'dots2', 'dots3', 'dots4', 'dots5', 'dots6', 'dots7', 'dots8', 'dots9',  3340474699.py:notify_waiting:41\n                             'dots10', 'dots11', 'dots12', 'dots8Bit', 'line', 'line2', 'pipe', 'simpleDots', 'simpleDotsScrolling', 'star',                                \n                             'star2', 'flip', 'hamburger', 'growVertical', 'growHorizontal', 'balloon', 'balloon2', 'noise', 'bounce',                                      \n                             'boxBounce', 'boxBounce2', 'triangle', 'arc', 'circle', 'squareCorners', 'circleQuarters', 'circleHalves',                                     \n                             'squish', 'toggle', 'toggle2', 'toggle3', 'toggle4', 'toggle5', 'toggle6', 'toggle7', 'toggle8', 'toggle9',                                    \n                             'toggle10', 'toggle11', 'toggle12', 'toggle13', 'arrow', 'arrow2', 'arrow3', 'bouncingBar', 'bouncingBall',                                    \n                             'smiley', 'monkey', 'hearts', 'clock', 'earth', 'material', 'moon', 'runner', 'pong', 'shark', 'dqpb',                                         \n                             'weather', 'christmas', 'grenade', 'point', 'layer', 'betaWave', 'aesthetic', 'guess', 'guess_the_movie'])                                     \n                                                                                                                                                                            \n                             Using: clock                                                                                                                                   \n\n\n\n\n\n\n\n\n\n[08/24/24 12:37:01] INFO     Downloading - Completed in 10.01 s                                                                                   contextlib.py:__exit__:144\n\n\n\n\n\n\n\n\n\n[08/24/24 12:37:11] INFO     Downloading - Completed in 10.01 s                                                                                   contextlib.py:__exit__:144\n\n\n\n\n\n\n\n\n\n[08/24/24 12:37:14] INFO     One more message - Completed in 3.00 s                                                                               contextlib.py:__exit__:144",
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+  {
+    "objectID": "report.html",
+    "href": "report.html",
+    "title": "Report",
+    "section": "",
+    "text": "Report\n\n\nfrom torch_snippets.torch_loader import Report\nimport numpy as np\nimport time\n\n\nn_epochs = 3\nreport = Report(n_epochs)\nrandom_walker1 = 0\nrandom_walker2 = 0\n\nfor epoch in range(n_epochs):\n    for ix in range(1000):\n        report.record(\n            pos=epoch + (ix + 1) / 1000,\n            loss=random_walker1,\n            val_loss=random_walker2,\n            end=\"\\r\",\n        )\n        random_walker1 += np.random.normal()\n        random_walker2 += np.random.normal()\n        time.sleep(0.001)\n    report.report_avgs(epoch + 1)\n\nreport.plot()\n\nEPOCH: 1.000    loss: -6.503    val_loss: -3.093    (1.19s - 2.38s remaining)))\nEPOCH: 2.000    loss: 48.754    val_loss: -6.265    (2.37s - 1.18s remaining))\nEPOCH: 3.000    loss: 38.115    val_loss: -29.732   (3.54s - 0.00s remaining)\n\n\n\n\n\n\n\n\n\n\nn_epochs = 5\nreport = Report(n_epochs, old_report=report)\n\nfor epoch in range(n_epochs):\n    for ix in range(1000):\n        report.record(\n            pos=epoch + (ix + 1) / 1000,\n            loss=random_walker1,\n            val_loss=random_walker2,\n            end=\"\\r\",\n        )\n        random_walker1 += np.random.normal()\n        random_walker2 += np.random.normal()\n        time.sleep(0.001)\n    report.report_avgs(epoch + 1)\n\nEPOCH: 1.000    loss: 29.338    val_loss: -74.955   (1.17s - 4.70s remaining))\nEPOCH: 2.000    loss: 0.340 val_loss: -110.763  (2.35s - 3.52s remaining)))\nEPOCH: 3.000    loss: 30.617    val_loss: -84.599   (3.51s - 2.34s remaining))\nEPOCH: 4.000    loss: 34.309    val_loss: -27.520   (4.68s - 1.17s remaining)\nEPOCH: 5.000    loss: 15.252    val_loss: -46.033   (5.85s - 0.00s remaining)\n\n\n\nimport matplotlib.pyplot as plt\n\nfig, ax = plt.subplots()\nax.vlines(0, -100, 100, colors=[\"red\"])\nreport.plot(ax=ax)",
+    "crumbs": [
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+    "objectID": "registry.html",
+    "href": "registry.html",
+    "title": "Registry",
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+    "text": "Suppose you have a file called config.ini like so…\n\n!cat /tmp/config.ini\n\n\n[META]\nversion = 0.0.1\nname = mnist\nroot = /home/me/projects/${META.name}\ndescription = This is a sample\n    config file with a multiline\n    description. These are useful for\n    project descriptions/changelog/devnotes etc...\n\n[Data]\nsource = https://files.fast.ai/data/examples/mnist_tiny.tgz\nroot = ${META.root}/data/\n\n[misc]\nx = 1\ny = 20\nz = float(${x}*${y}**2)\na = ['hello','hi','how','are','you', ${x}*${z}*${y}]\nb = {\"hi\": 1, \"hello\": 2}\n\n[load]\n    [load.test]\n    @load = print_root_location\n    root = ${Data.root}\n    \n    [load.csv]\n    @load = load_csv_function\n    root = ${Data.root}\n    \n    [load.json]\n    @load = load_json_class\n    root = ${Data.root}\n    \n    \n\n\n\nYou can load it up as an AttrDict\n\nconfig = parse(\"/tmp/config.ini\")\nassert config.META.version == \"0.0.1\"\nassert config.META.root == \"/home/me/projects/mnist\"\nassert isinstance(config.misc.b, AttrDict), type(config.project.data.b)\nassert isinstance(config.misc.a, L)\n\nNotice, how the ${} variables got resolved.\nNot just that, the varaible z got computed on the fly.\nNot just that, some of the variables like list and dict got resolved into their respective python data structures.\n\nconfig.pretty()\n\n{\n    \"Data\": {\n        \"root\": \"/home/me/projects/mnist/data/\",\n        \"source\": \"https://files.fast.ai/data/examples/mnist_tiny.tgz\"\n    },\n    \"META\": {\n        \"description\": \"This is a sample\\nconfig file with a multiline\\ndescription. These are useful for\\nproject \ndescriptions/changelog/devnotes etc...\",\n        \"name\": \"mnist\",\n        \"root\": \"/home/me/projects/mnist\",\n        \"version\": \"0.0.1\"\n    },\n    \"load\": {\n        \"csv\": {\n            \"@load\": null,\n            \"root\": \"/home/me/projects/mnist/data/\"\n        },\n        \"json\": {\n            \"@load\": \"load_json_class\",\n            \"root\": \"/home/me/projects/mnist/data/\"\n        },\n        \"test\": {\n            \"@load\": \"print_root_location\",\n            \"root\": \"/home/me/projects/mnist/data/\"\n        }\n    },\n    \"misc\": {\n        \"a\": [\n            \"hello\",\n            \"hi\",\n            \"how\",\n            \"are\",\n            \"you\",\n            8000.0\n        ],\n        \"b\": {\n            \"hello\": 2,\n            \"hi\": 1\n        },\n        \"x\": 1,\n        \"y\": 20,\n        \"z\": 400.0\n    }\n}\n\n\n\n\nprint(config.META.description)\n\nThis is a sample\nconfig file with a multiline\ndescription. These are useful for\nproject descriptions/changelog/devnotes etc...\n\n\nYou can also register/call python functions/callables/classes/objects to strings by running\n\nregistry.create(\"load\")\n\n\n@registry.load.register(\"print_root_location\")\ndef printer(root):\n    return root\n\n\n@registry.load.register(\"load_csv_function\")\ndef _load_csv_function(root):\n    def load_csv_function(file):\n        return f\"Loading file from {root}/{file}\"\n\n    return load_csv_function\n\n\n@registry.load.register(\"load_json_class\")\nclass JsonLoader:\n    def __init__(self, root):\n        self.root = root\n\n    def __call__(self, file):\n        assert file.endswith(\"json\")\n        return f\"Loading file from {self.root}/{file}\"\n\n… and resolve them on parse\n\nconfig = parse_and_resolve(\"/tmp/config.ini\")\n\n\nconfig.load.test\n\n'/home/me/projects/mnist/data/'\n\n\n\nconfig.load.csv(file=\"file.csv\")\n\n'Loading file from /home/me/projects/mnist/data//file.csv'\n\n\n\nconfig.load.json(file=\"file.json\")\n\n'Loading file from /home/me/projects/mnist/data//file.json'",
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+    "objectID": "config.html",
+    "href": "config.html",
+    "title": "torch_snippets",
+    "section": "",
+    "text": "DeepLearningConfig\n\n DeepLearningConfig ()\n\n*A configuration class for deep learning models.\nThis class provides methods to access and manipulate configuration settings.\nAttributes: input_variables (list): List of input variables defined in the class constructor.\nMethods: keys(): Returns the list of input variables. getitem(key): Returns the value of the specified key. contains(key): Checks if the specified key is present in the input variables. from_ini_file(filepath, config_root=None): Creates an instance of the class from an INI file. repr(): Returns a string representation of the class.\nExample usage: config = DeepLearningConfig() config.from_ini_file(‘config.ini’) print(config.keys()) print(config[‘learning_rate’])*\n\nfrom torch_snippets.registry import parse_string\nfrom torch_snippets.torch_loader import *\nfrom torch_snippets import writelines\n\n\nconfig_str = \"\"\"\n[META]\nexperiment = mnist.v1\ndescription = Training MLP with \n    mnist data on 10k images only\n    using huggingface trainer and \n    cosine annealing\n\n[ModelConfig]\nn_layers = 3\nn_hidden = 256\nn_classes = 10\n\n[DatasetConfig]\nroot = /home/datasets/mnist\ntrain = ${root}/train\nval = ${root}/val\ntrain_subset = 10000\nval_subest = ${train_subset}//10\n\n[TrainingConfig]\nmax_steps = ${DatasetConfig.train_subset} * 5\nlearning_rate = 3e-4\noutput_dir = ./results/${META.experiment}\nper_device_train_batch_size = 256\nper_device_eval_batch_size = ${per_device_train_batch_size}\nevaluation_strategy = \"steps\"\neval_steps = 500\nlogging_strategy = ${evaluation_strategy}\nlogging_steps = ${eval_steps}//100\nsave_strategy = ${evaluation_strategy}\nsave_steps = ${eval_steps}\nsave_total_limit = 1\nseed = 1234\nlabel_names = ['targets']\nlr_scheduler_type = cosine\n\"\"\".strip()\n\nconfig = parse_string(config_str)\n\n\nclass MNIST(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.model = nn.Sequential(\n            nn.Linear(768, config.n_hidden),\n            *[\n                nn.Sequential(nn.Linear(config.n_hidden, config.n_hidden), nn.ReLU())\n                for _ in range(config.n_layers - 1)\n            ],\n            nn.Linear(config.n_hidden, config.n_classes)\n        )\n\n    def forward(self, images): ...\n\n\nmodel = MNIST(config.ModelConfig)\nprint(model)\n\nMNIST(\n  (model): Sequential(\n    (0): Linear(in_features=768, out_features=256, bias=True)\n    (1): Sequential(\n      (0): Linear(in_features=256, out_features=256, bias=True)\n      (1): ReLU()\n    )\n    (2): Sequential(\n      (0): Linear(in_features=256, out_features=256, bias=True)\n      (1): ReLU()\n    )\n    (3): Linear(in_features=256, out_features=10, bias=True)\n  )\n)\n\n\nIf needed, configs can be unpacked like a dictionary too\n\nclass MNIST(nn.Module):\n    \"\"\"\n    A PyTorch module for a multi-layer perceptron (MLP) model for MNIST classification.\n\n    Args:\n        n_hidden (int): The number of hidden units in each hidden layer.\n        n_classes (int): The number of output classes.\n        n_layers (int): The number of hidden layers in the model.\n\n    Attributes:\n        model (nn.Sequential): The sequential model that represents the MLP.\n\n    \"\"\"\n\n    def __init__(self, *, n_hidden, n_classes, n_layers):\n        super().__init__()\n        self.model = nn.Sequential(\n            nn.Linear(768, n_hidden),\n            *[\n                nn.Sequential(nn.Linear(n_hidden, n_hidden), nn.ReLU())\n                for _ in range(n_layers - 1)\n            ],\n            nn.Linear(n_hidden, n_classes)\n        )\n\n    def forward(self, images): ...\n\n\nmodel = MNIST(**config.ModelConfig)\nprint(model)\n\nMNIST(\n  (model): Sequential(\n    (0): Linear(in_features=768, out_features=256, bias=True)\n    (1): Sequential(\n      (0): Linear(in_features=256, out_features=256, bias=True)\n      (1): ReLU()\n    )\n    (2): Sequential(\n      (0): Linear(in_features=256, out_features=256, bias=True)\n      (1): ReLU()\n    )\n    (3): Linear(in_features=256, out_features=10, bias=True)\n  )\n)\n\n\n\n\n\nGenericConfig\n\n GenericConfig (**kwargs)\n\n*A configuration class for deep learning models.\nThis class provides methods to access and manipulate configuration settings.\nAttributes: input_variables (list): List of input variables defined in the class constructor.\nMethods: keys(): Returns the list of input variables. getitem(key): Returns the value of the specified key. contains(key): Checks if the specified key is present in the input variables. from_ini_file(filepath, config_root=None): Creates an instance of the class from an INI file. repr(): Returns a string representation of the class.\nExample usage: config = DeepLearningConfig() config.from_ini_file(‘config.ini’) print(config.keys()) print(config[‘learning_rate’])*\nGenericConfig is a special class that can have attributes solely based on the config file, i.e., when we are unsure what are the arguments in the config going to be\n\nwritelines(config_str.split(\"\\n\"), \"/tmp/tmp.ini\", \"w\")\ntraining_config = GenericConfig.from_ini_file(\n    \"/tmp/tmp.ini\", config_root=\"TrainingConfig\"\n)\n\n\ndef train(**kwargs):\n    for k, v in kwargs.items():\n        print(k, v)\n\n\ntrain(**training_config)\n\nmax_steps 50000\nlearning_rate 0.00030000000000000003\noutput_dir ./results/mnist.v1\nper_device_train_batch_size 256\nper_device_eval_batch_size 256\nevaluation_strategy steps\neval_steps 500\nlogging_strategy steps\nlogging_steps 5\nsave_strategy steps\nsave_steps 500\nsave_total_limit 1\nseed 1234\nlabel_names ['targets']\nlr_scheduler_type cosine",
+    "crumbs": [
+      "config.html"
+    ]
+  },
+  {
+    "objectID": "paths.html",
+    "href": "paths.html",
+    "title": "Paths",
+    "section": "",
+    "text": "Paths\nUtilities to manipulate Paths\n\n\nprint(P().ls())\nprint(P().resolve())\n\n[» _quarto.yml, » decorators.ipynb, » markups.ipynb, » sidebar.yml, » AttrDict.ipynb, » interactive_show.ipynb, » load_defautls.ipynb, » sklegos.ipynb, » bounding_boxes.ipynb, » show.ipynb, » pdf.ipynb, » charts.ipynb, » paths.ipynb, » nbdev.yml, » tmp.csv, » jupyter_notebook.ipynb, » config.ipynb, » misc.ipynb, » registry.ipynb, » adapters.ipynb, » report.ipynb, » .ipynb_checkpoints, » capsule.ipynb, » logging.ipynb, » inspector.ipynb, » bokeh_plotting.ipynb, » index.ipynb, » imgaug_loader.ipynb]\n/Users/yeshwanth/Code/Personal/torch_snippets/nbs\n\n\n\n# !touch tmp.txt tmp.csv\n# # x = P()\n# x.tmp__csv, x.tmp__txt, x.misc\n\n\n!rm tmp.txt tmp.misc\n\nrm: tmp.txt: No such file or directory\nrm: tmp.misc: No such file or directory\n\n\n\n\nPath.rm\n\n Path.rm (confirm_prompt='Are you sure you want to delete `{self}`?\n          [y/N]', silent=True, missing_ok=True, force=False,\n          dry_run=False)\n\n\n\n\nremove_file\n\n remove_file (dry_run)\n\n\n\n\nPath.cp\n\n Path.cp (to)\n\n\n\n\nPath.mv\n\n Path.mv (to)\n\n\n\n\nPath.sample\n\n Path.sample (pattern='*')\n\n\n\n\nextn\n\n extn (fpath)\n\n\n\n\nPath.extn\n\n Path.extn ()\n\n\n\n\nPath.sz\n\n Path.sz ()\n\n\n\n\nPath.size\n\n Path.size ()\n\n\n\n\nPath.rmtree\n\n Path.rmtree (prompt='Really remove `{self}` and its contents? [y/n] ',\n              force=False)\n\n\np = P(\"test.txt\")\np.touch()\nlogger.info(p.size())\n\n[08/24/24 11:12:12] INFO     0 KB                                                                                                                   1417366103.py:<module>:3\n\n\n\nPath objects can be moved and copied\n\np = p.mv(\"test1.txt\")\nq = p.cp(\"test2.txt\")\n\nPath objects have a size, extn (extension) and parent attributes\n\nassert isinstance(q, P)\nassert q.size() == \"0 KB\"\nassert str(p) == \"test1.txt\"\nassert p.extn == \"txt\"\nassert p.parent == P()\n\nThey can be deleted with/without a prompt\n\np.rm(force=True)\nq.rm(confirm_prompt=False)\n\nFolders can be globbed with a default of everything\n\np = P(\"../torch_snippets\")\nassert P().ls() == P().Glob()\np.Glob(\"*.py\")\n\n(#34) [» ../torch_snippets/misc.py,» ../torch_snippets/load_defaults.py,» ../torch_snippets/text_utils.py,» ../torch_snippets/_nbdev.py,» ../torch_snippets/paths.py,» ../torch_snippets/charts.py,» ../torch_snippets/pdf_loader.py,» ../torch_snippets/interactive_show.py,» ../torch_snippets/registry.py,» ../torch_snippets/markup2.py,» ../torch_snippets/_modidx.py,» ../torch_snippets/inspector.py,» ../torch_snippets/__init__.py,» ../torch_snippets/tmp.py,» ../torch_snippets/torch_loader.py,» ../torch_snippets/logger.py,» ../torch_snippets/markup.py,» ../torch_snippets/fastcores.py,» ../torch_snippets/sklegos.py,» ../torch_snippets/cli.py...]\n\n\nYou can sample a random file from the directory\n\nq = p.sample(\"*.py\")\nInfo(f\"Sample file: `{q}`\")\nInfo(f\"Sample file size: `{q.size()}`\")\n\n                    INFO     Sample file: `../torch_snippets/logger.py`                                                                             1725593160.py:<module>:2\n\n\n\n                    INFO     Sample file size: `9 KB`                                                                                               1725593160.py:<module>:3\n\n\n\n\ntry:\n    p.size()\nexcept Exception as e:\n    logger.warning(e)\n\n                    WARNING  `../torch_snippets` is a directory                                                                                     1980994904.py:<module>:4\n\n\n\n\np = P(\"test.txt\")\np.touch()\nassert isdir(p) == False\nassert fname(p) == \"test.txt\"\nassert parent(p) == P()\nassert stem(p) == \"test\"\nassert extn(p) == \"txt\"\n\nprint(find(\"capsule\", Glob(\"./\")))\n\np.rm(confirm_prompt=False)\n\ncapsule.ipynb\n\n\n\n\n\nlist_zip\n\n list_zip (file)\n\n\n\n\nunzip_file\n\n unzip_file (file, dest)\n\n\n\n\nzip_files\n\n zip_files (list_of_files, dest)\n\n\n!touch test1.txt test2.txt\nf = zip_files(P().Glob(\"*.txt\"), \"test.tar.gz\")\nunzip_file(f, \"./\")\n[f.rm(force=True) for f in P().Glob(\"*.txt\")]\nP(\"test.tar.gz\").rm(force=True)\n\n!touch test1.txt test2.txt\nf = zip_files(P().Glob(\"*.txt\"), \"test.zip\")\nunzip_file(f, \"./\")\n[f.rm(force=True) for f in P().Glob(\"*.txt\")]\nP(\"test.zip\").rm(force=True)\n\n                    INFO     Zipping 2 files to test.tar.gz...                                                                                     3826617683.py:zip_files:8\n\n\n\n100%|██████████| 2/2 [00:00<00:00, 649.78it/s]\n/var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_40147/3826617683.py:27: DeprecationWarning: Python 3.14 will, by default, filter extracted tar archives and reject files or modify their metadata. Use the filter argument to control this behavior.\n  f.extractall(dest)\n\n\n                    INFO     Zipping 2 files to test.zip...                                                                                        3826617683.py:zip_files:8\n\n\n\n100%|██████████| 2/2 [00:00<00:00, 2958.94it/s]\n\n\n\n\n\nfolder_summary\n\n folder_summary (thing)\n\n\n\n\ncommon_items\n\n common_items (*fldrs, verbose=True)\n\n\n\n\nremove_duplicates\n\n remove_duplicates (files)\n\nCheck a list of files and remove duplicates based on their checksum\n\n\n\nmd5\n\n md5 (fname)\n\n\nmd5(\"paths.ipynb\")\n\n'74eb392fa44e9293fb30ffed68c14112'\n\n\n\n\n\nPath.write_lines\n\n Path.write_lines (lines, mode)\n\n\n\n\nwritelines\n\n writelines (lines, file, mode)\n\n\n\n\nPath.read_file\n\n Path.read_file (**kwargs)\n\n\n\n\nreadfile\n\n readfile (*args, **kwargs)\n\n\n\n\nPath.read_lines\n\n Path.read_lines (silent=False, encoding=None)\n\n\n\n\nreadlines\n\n readlines (fpath, silent=False, encoding=None, _strip=True)\n\n\nP(\"paths.ipynb\").read_lines()[:10]\n\n                    INFO     loaded 1075 lines                                                                                                   2901855681.py:read_lines:13\n\n\n\n['{',\n '\"cells\": [',\n '{',\n '\"cell_type\": \"raw\",',\n '\"metadata\": {},',\n '\"source\": [',\n '\"# Paths\\\\n\",',\n '\"Utilities to manipulate Paths\\\\n\",',\n '\"---\\\\n\",',\n '\"{}\\\\n\",']\n\n\n\n\n\ntree\n\n tree (directory='./', filelimit=50, to=None)\n\n\nP(\"../\").tree\n\n/Users/yeshwanth/Code/Personal/torch_snippets\n ── LICENSE\n ── LICENSE.txt\n ── MANIFEST\n ── MANIFEST.in\n ── Makefile\n ── README.md\n ── Screenshot 2022-09-08 at 4.40.18 PM.png\n ── __module_timing__.py.lprof\n ── _proc\n     ── AttrDict.ipynb\n     ── _quarto.yml\n     ── adapters.ipynb\n     ── bokeh_plotting.ipynb\n     ── bounding_boxes.ipynb\n     ── capsule.ipynb\n     ── charts.ipynb\n     ── config.ipynb\n     ── decorators.ipynb\n     ── docs\n         ── AttrDict.html\n         ── adapters.html\n         ── bokeh_plotting.html\n         ── bounding_boxes.html\n         ── bounding_boxes_files\n             ── figure-html\n                 ── cell-6-output-1.png\n                 ── cell-7-output-1.png\n                 ── cell-8-output-1.png\n                 ── cell-9-output-1.png\n         ── capsule.html\n         ── capsule_files\n             ── figure-html\n                 ── cell-6-output-2.png\n         ── charts.html\n         ── charts_files\n             ── figure-html\n                 ── cell-7-output-1.png\n         ── config.html\n         ── decorators.html\n         ── imgaug_loader.html\n         ── imgaug_loader_files\n             ── figure-html\n                 ── cell-3-output-1.png\n                 ── cell-5-output-10.png\n                 ── cell-5-output-12.png\n                 ── cell-5-output-14.png\n                 ── cell-5-output-16.png\n                 ── cell-5-output-18.png\n                 ── cell-5-output-2.png\n                 ── cell-5-output-20.png\n                 ── cell-5-output-22.png\n                 ── cell-5-output-4.png\n                 ── cell-5-output-6.png\n                 ── cell-5-output-8.png\n                 ── cell-6-output-1.png\n                 ── cell-7-output-1.png\n                 ── cell-8-output-2.png\n         ── index.html\n         ── inspector.html\n         ── interactive_show.html\n         ── jupyter_notebook.html\n         ── load_defautls.html\n         ── logging.html\n         ── markups.html\n         ── misc.html\n         ── paths.html\n         ── pdf.html\n         ── registry.html\n         ── report.html\n         ── report_files\n             ── figure-html\n                 ── cell-3-output-2.png\n                 ── cell-5-output-1.png\n         ── robots.txt\n         ── search.json\n         ── show.html\n         ── show_files\n             ── figure-html\n                 ── cell-2-output-1.png\n                 ── cell-3-output-1.png\n                 ── cell-4-output-2.png\n                 ── cell-4-output-4.png\n                 ── cell-4-output-6.png\n                 ── cell-5-output-1.png\n                 ── cell-8-output-1.png\n                 ── cell-9-output-1.png\n         ── site_libs\n             ── bootstrap\n                 ── bootstrap-icons.css\n                 ── bootstrap-icons.woff\n                 ── bootstrap.min.css\n                 ── bootstrap.min.js\n             ── clipboard\n                 ── clipboard.min.js\n             ── quarto-html\n                 ── anchor.min.js\n                 ── popper.min.js\n                 ── quarto-syntax-highlighting.css\n                 ── quarto.js\n                 ── tippy.css\n                 ── tippy.umd.min.js\n             ── quarto-nav\n                 ── headroom.min.js\n                 ── quarto-nav.js\n             ── quarto-search\n                 ── autocomplete.umd.js\n                 ── fuse.min.js\n                 ── quarto-search.js\n         ── sitemap.xml\n         ── sklegos.html\n     ── imgaug_loader.ipynb\n     ── index.ipynb\n     ── inspector.ipynb\n     ── interactive_show.ipynb\n     ── jupyter_notebook.ipynb\n     ── load_defautls.ipynb\n     ── logging.ipynb\n     ── markups.ipynb\n     ── misc.ipynb\n     ── nbdev.yml\n     ── paths.ipynb\n     ── pdf.ipynb\n     ── registry.ipynb\n     ── report.ipynb\n     ── show.ipynb\n     ── sidebar.yml\n     ── sklegos.ipynb\n     ── test.txt\n     ── test1.txt\n     ── test2.txt\n     ── tmp.csv\n     ── tmp.txt\n ── assets\n     ── Preamble.csv\n     ── Preamble.png\n     ── avgs0.png\n     ── avgs1.png\n     ── demo.gif\n ── backups\n     ── testing\n         ── 0000.html\n ── build\n     ── bdist.macosx-11.1-arm64\n     ── lib\n         ── torch_snippets\n             ── __init__.py\n             ── __module_timing__.py\n             ── _modidx.py\n             ── _nbdev.py\n             ── adapters.py\n             ── bb_utils.py\n             ── bokeh_loader.py\n             ── charts.py\n             ── cli.py\n             ── dates.py\n             ── decorators.py\n             ── fastcores.py\n             ── icecream.py\n             ── imgaug_loader.py\n             ── inspector.py\n             ── interactive_show.py\n             ── ipython.py\n             ── load_defaults.py\n             ── loader.py\n             ── logger.py\n             ── markup.py\n             ── markup2.py\n             ── misc.py\n             ── paths.py\n             ── pdf_loader.py\n             ── registry.py\n             ── s3_loader.py\n             ── scp.py\n             ── sklegos.py\n             ── text_utils.py\n             ── thinc_parser\n                 ── __init__.py\n                 ── parser.py\n             ── tmp.py\n             ── torch_loader.py\n             ── trainer\n                 ── __init__.py\n                 ── capsule.py\n                 ── config.py\n                 ── hooks.py\n                 ── neural_graph.py\n             ── video.py\n             ── zen.py\n ── changelog.md\n ── conda\n     ── torch_snippets\n         ── meta.yaml\n ── debug\n     ── profile_time.txt\n     ── profile_time_202408241027.txt\n ── dist\n     ── torch_snippets-0.6.0-py3-none-any.whl\n     ── torch_snippets-0.6.0.tar.gz\n ── docker-compose.yml\n ── docs\n     ── AttrDict.html\n     ── adapters.html\n     ── bokeh_plotting.html\n     ── bounding_boxes.html\n     ── bounding_boxes_files\n         ── figure-html\n             ── cell-6-output-1.png\n             ── cell-7-output-1.png\n             ── cell-8-output-1.png\n             ── cell-9-output-1.png\n     ── capsule.html\n     ── capsule_files\n         ── figure-html\n             ── cell-5-output-2.png\n     ── charts.html\n     ── charts_files\n         ── figure-html\n             ── cell-7-output-1.png\n     ── config.html\n     ── decorators.html\n     ── imgaug_loader.html\n     ── imgaug_loader_files\n         ── figure-html\n             ── cell-3-output-1.png\n             ── cell-5-output-10.png\n             ── cell-5-output-12.png\n             ── cell-5-output-14.png\n             ── cell-5-output-16.png\n             ── cell-5-output-18.png\n             ── cell-5-output-2.png\n             ── cell-5-output-20.png\n             ── cell-5-output-22.png\n             ── cell-5-output-4.png\n             ── cell-5-output-6.png\n             ── cell-5-output-8.png\n             ── cell-6-output-1.png\n             ── cell-7-output-1.png\n             ── cell-8-output-2.png\n     ── index.html\n     ── inspector.html\n     ── interactive_show.html\n     ── jupyter_notebook.html\n     ── load_defautls.html\n     ── logging.html\n     ── markups.html\n     ── misc.html\n     ── paths.html\n     ── pdf.html\n     ── registry.html\n     ── report.html\n     ── report_files\n         ── figure-html\n             ── cell-3-output-2.png\n             ── cell-5-output-1.png\n     ── robots.txt\n     ── search.json\n     ── show.html\n     ── show_files\n         ── figure-html\n             ── cell-2-output-1.png\n             ── cell-3-output-1.png\n             ── cell-4-output-2.png\n             ── cell-4-output-4.png\n             ── cell-4-output-6.png\n             ── cell-5-output-1.png\n             ── cell-8-output-1.png\n             ── cell-9-output-1.png\n     ── site_libs\n         ── bootstrap\n             ── bootstrap-icons.css\n             ── bootstrap-icons.woff\n             ── bootstrap.min.css\n             ── bootstrap.min.js\n         ── clipboard\n             ── clipboard.min.js\n         ── quarto-html\n             ── anchor.min.js\n             ── popper.min.js\n             ── quarto-syntax-highlighting.css\n             ── quarto.js\n             ── tippy.css\n             ── tippy.umd.min.js\n         ── quarto-nav\n             ── headroom.min.js\n             ── quarto-nav.js\n         ── quarto-search\n             ── autocomplete.umd.js\n             ── fuse.min.js\n             ── quarto-search.js\n     ── sitemap.xml\n     ── sklegos.html\n ── nbs\n     ── AttrDict.ipynb\n     ── _quarto.yml\n     ── adapters.ipynb\n     ── bokeh_plotting.ipynb\n     ── bounding_boxes.ipynb\n     ── capsule.ipynb\n     ── charts.ipynb\n     ── config.ipynb\n     ── decorators.ipynb\n     ── imgaug_loader.ipynb\n     ── index.ipynb\n     ── inspector.ipynb\n     ── interactive_show.ipynb\n     ── jupyter_notebook.ipynb\n     ── load_defautls.ipynb\n     ── logging.ipynb\n     ── markups.ipynb\n     ── misc.ipynb\n     ── nbdev.yml\n     ── paths.ipynb\n     ── pdf.ipynb\n     ── registry.ipynb\n     ── report.ipynb\n     ── show.ipynb\n     ── sidebar.yml\n     ── sklegos.ipynb\n     ── tmp.csv\n ── requirements.txt\n ── scripts.ipynb\n ── settings.ini\n ── setup.cfg\n ── setup.py\n ── testing.ipynb\n ── tmp.ini\n ── torch_snippets\n     ── __init__.py\n     ── __module_timing__.py\n     ── __pycache__\n         ── __init__.cpython-312.pyc\n         ── __module_timing__.cpython-312.pyc\n         ── bb_utils.cpython-312.pyc\n         ── bokeh_loader.cpython-312.pyc\n         ── charts.cpython-312.pyc\n         ── cli.cpython-312.pyc\n         ── dates.cpython-312.pyc\n         ── decorators.cpython-312.pyc\n         ── icecream.cpython-312.pyc\n         ── inspector.cpython-312.pyc\n         ── ipython.cpython-312.pyc\n         ── load_defaults.cpython-312.pyc\n         ── loader.cpython-312.pyc\n         ── logger.cpython-312.pyc\n         ── markup.cpython-312.pyc\n         ── markup2.cpython-312.pyc\n         ── misc.cpython-312.pyc\n         ── paths.cpython-312.pyc\n         ── pdf_loader.cpython-312.pyc\n         ── registry.cpython-312.pyc\n         ── s3_loader.cpython-312.pyc\n         ── torch_loader.cpython-312.pyc\n         ── zen.cpython-312.pyc\n     ── _modidx.py\n     ── _nbdev.py\n     ── adapters.py\n     ── bb_utils.py\n     ── bokeh_loader.py\n     ── charts.py\n     ── cli.py\n     ── dates.py\n     ── decorators.py\n     ── fastcores.py\n     ── icecream.py\n     ── imgaug_loader.py\n     ── inspector.py\n     ── interactive_show.py\n     ── ipython.py\n     ── load_defaults.py\n     ── loader.py\n     ── logger.py\n     ── markup.py\n     ── markup2.py\n     ── misc.py\n     ── paths.py\n     ── pdf_loader.py\n     ── registry.py\n     ── s3_loader.py\n     ── scp.py\n     ── sklegos.py\n     ── text_utils.py\n     ── thinc_parser\n         ── __init__.py\n         ── __pycache__\n             ── __init__.cpython-312.pyc\n             ── parser.cpython-312.pyc\n         ── parser.py\n     ── tmp.py\n     ── torch_loader.py\n     ── trainer\n         ── __init__.py\n         ── __pycache__\n             ── __init__.cpython-312.pyc\n             ── capsule.cpython-312.pyc\n         ── capsule.py\n         ── config.py\n         ── hooks.py\n         ── neural_graph.py\n     ── video.py\n     ── zen.py\n ── torch_snippets.egg-info\n     ── PKG-INFO\n     ── SOURCES.txt\n     ── dependency_links.txt\n     ── entry_points.txt\n     ── not-zip-safe\n     ── requires.txt\n     ── top_level.txt\n\n61 directories, 348 files\n\n\n\n\n\n\nfolder_structure_to_json\n\n folder_structure_to_json (path, output_file=None)\n\nCreates a JSON file representing the folder structure of the given directory.\n\n\n\nfolder_structure_to_dict\n\n folder_structure_to_dict (path)\n\nRecursively constructs a nested dictionary that represents the folder structure.\n\nx = P(\"tmp.txt\")\nx.touch()\nx.write_lines([i for i in range(1000)], mode=\"w\")\nlines = x.read_lines()\nassert lines == [f\"{i}\" for i in range(1000)]\nlogger.info(x.size())\n\nx.rm(confirm_prompt=False)\n\n[08/24/24 11:12:13] INFO     loaded 1000 lines                                                                                                   2901855681.py:read_lines:13\n\n\n\n                    INFO     3 KB                                                                                                                   1033976698.py:<module>:6\n\n\n\n\n\n\nloaddill\n\n loaddill (fpath)\n\nLoad a python object from a dill file\n\n\n\ndumpdill\n\n dumpdill (obj, fpath, silent=False, message='Dumped object of size\n           ≈{fsize} @ \"{fpath}\" in {dumptime:.2e} seconds')\n\nDump a python object as a dill file (better replacement to pickle)\n\np = P(\"test.tmp\")\ndumpdill([1, 2, 3], p)\ny = loaddill(p)\np.rm(confirm_prompt=False)\nassert y == [1, 2, 3]\n\n                    INFO     Dumped object of size ≈0 KB @ \"test.tmp\" in 4.11e-04 seconds                                                            554288780.py:<module>:2",
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+    "objectID": "pdf.html",
+    "href": "pdf.html",
+    "title": "PDF",
+    "section": "",
+    "text": "PDF\n\n PDF (path, dfs=None, dpi=150)\n\nLoad a PDF file from path as a list of images Use show function to see the images WIP",
+    "crumbs": [
+      "PDF"
+    ]
+  },
+  {
+    "objectID": "bounding_boxes.html",
+    "href": "bounding_boxes.html",
+    "title": "Bounding Box",
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+    "text": "Bounding Box\n\nCreate a box by mentioning the top-left (x, y) and bottom-right (X, Y) coordinates\nSay x, y, X, Y are 10, 20, 40, 50 respectively\n\nbb = BB([10, 20, 40, 50])\n\nYou get the following attributes for free\n\n\n\nbb.x=10              (top left - x)\nbb.y=20              (top left - y)\nbb.X=40              (bottom right - x)\nbb.Y=50              (bottom right - y)\nbb.w=30              (width)\nbb.h=30              (height)\nbb.xc=25.0           (center x)\nbb.yc=35.0           (center y)\nbb.c=(25.0, 35.0)    (center)\nbb.area=900          (area)\nbb.shape=(30, 30)    (height, width)\n\n\n\n\nfrom torch_snippets import show, read, P, pd\n\n\nassets = P(\"/Users/yeshwanth.y/code/torch_snippets/assets/\")\nim = read(assets / \"Preamble.png\")\ndf = pd.read_csv(assets / \"Preamble.csv\")\nshow(df.head())\ndf = to_absolute(df, *im.shape[:2])\nshow(df.head())\ndf = to_relative(df, *im.shape[:2])\nshow(df.head())\n\n\n  \n    \n      \n      x\n      y\n      X\n      Y\n      text\n      block_id\n    \n  \n  \n    \n      0\n      135\n      181\n      308\n      218\n      ConstITUtIO\n      0\n    \n    \n      1\n      156\n      264\n      217\n      284\n      NLTHE\n      1\n    \n    \n      2\n      218\n      264\n      276\n      284\n      PEOPLE\n      1\n    \n    \n      3\n      267\n      265\n      295\n      282\n      OF\n      1\n    \n    \n      4\n      297\n      264\n      341\n      284\n      INDIA,\n      1\n    \n  \n\n\n\n\n  \n    \n      \n      x\n      y\n      X\n      Y\n      text\n      block_id\n    \n  \n  \n    \n      0\n      135\n      181\n      308\n      218\n      ConstITUtIO\n      0\n    \n    \n      1\n      156\n      264\n      217\n      284\n      NLTHE\n      1\n    \n    \n      2\n      218\n      264\n      276\n      284\n      PEOPLE\n      1\n    \n    \n      3\n      267\n      265\n      295\n      282\n      OF\n      1\n    \n    \n      4\n      297\n      264\n      341\n      284\n      INDIA,\n      1\n    \n  \n\n\n\n\n  \n    \n      \n      x\n      y\n      X\n      Y\n      text\n      block_id\n    \n  \n  \n    \n      0\n      0.249538\n      0.253501\n      0.569316\n      0.305322\n      ConstITUtIO\n      0\n    \n    \n      1\n      0.288355\n      0.369748\n      0.401109\n      0.397759\n      NLTHE\n      1\n    \n    \n      2\n      0.402957\n      0.369748\n      0.510166\n      0.397759\n      PEOPLE\n      1\n    \n    \n      3\n      0.493530\n      0.371148\n      0.545287\n      0.394958\n      OF\n      1\n    \n    \n      4\n      0.548983\n      0.369748\n      0.630314\n      0.397759\n      INDIA,\n      1\n    \n  \n\n\n\n\nshow(im, df=df, sz=10)\n\n\n\n\n\n\n\n\n\nshow(im, df=to_absolute(df, *im.shape[:2]), sz=10)\n\n\n\n\n\n\n\n\n\n_df = to_absolute(df, *im.shape[:2])\nshow(im, df=to_relative(_df, *im.shape[:2]), sz=10)\n\n\n\n\n\n\n\n\n\n_df = combine_xyXY_to_bb(_df)\nshow(im, df=to_relative(_df, *im.shape[:2]), sz=10)\n\n\n\n\n\n\n\n\n\nto_relative(_df, *im.shape[:2])\n\n\n\n\n\n\n\n\ntext\nblock_id\nbb\n\n\n\n\n0\nConstITUtIO\n0\n[0.24953789279112754, 0.2535014005602241, 0.56...\n\n\n1\nNLTHE\n1\n[0.28835489833641403, 0.3697478991596639, 0.40...\n\n\n2\nPEOPLE\n1\n[0.4029574861367837, 0.3697478991596639, 0.510...\n\n\n3\nOF\n1\n[0.49353049907578556, 0.3711484593837535, 0.54...\n\n\n4\nINDIA,\n1\n[0.5489833641404805, 0.3697478991596639, 0.630...\n\n\n...\n...\n...\n...\n\n\n68\nGIVE\n13\n[0.4011090573012939, 0.7366946778711485, 0.478...\n\n\n69\nTO\n13\n[0.4879852125693161, 0.7366946778711485, 0.536...\n\n\n70\noUrSELVES\n13\n[0.5508317929759704, 0.7394957983193278, 0.706...\n\n\n71\nTHIS\n13\n[0.7190388170055453, 0.7366946778711485, 0.783...\n\n\n72\nCONSTITUTION.\n14\n[0.23290203327171904, 0.7689075630252101, 0.44...\n\n\n\n\n73 rows × 3 columns\n\n\n\n\n\nisin\n\n isin (bboxes1, bboxes2, return_matrix=True)\n\nreturn indexes of those boxes from bboxes1 that are completely inside bboxes2\n\n\n\nmerge_by_bb\n\n merge_by_bb (df1, df2, suffixes=('_x', '_y'), iou_threshold=0.1)\n\nMerge df1 columns to df2 by using iou Make sure both df1 & df2 are relative or both absolute",
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+      "Bounding Box"
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+  {
+    "objectID": "load_defautls.html",
+    "href": "load_defautls.html",
+    "title": "File Exists",
+    "section": "",
+    "text": "File Exists\nand what to do if it doesn’t\n\n\n\nloadifexists\n\n loadifexists (fpath, default)\n\nLoad data from a dill file if it exists, else return default value\n\n\n\nexists\n\n exists (fpath)\n\nAlias for os.path.exists",
+    "crumbs": [
+      "File Exists"
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+  },
+  {
+    "objectID": "AttrDict.html",
+    "href": "AttrDict.html",
+    "title": "torch_snippets",
+    "section": "",
+    "text": "from torch_snippets.loader import L\n\n[07/01/24 11:12:47] WARNING  Unable to load torch and dependent libraries from torch-snippets.                                                        loader.py:<module>:108\n                             Functionalities might be limited. pip install lovely-tensors in case there are torch related errors                                            \n\n\n\n\nclass AD(object):\n    forbidden = set(\":,'\\\"}{.\")\n\n    def __init__(self, *args, given_input_to_ad=None, **kwargs):\n        given_input_to_ad = {} if given_input_to_ad is None else given_input_to_ad\n        if len(args) == 1 and isinstance(args[0], (Mapping, AttrDict)):\n            given_input_to_ad = args[0]\n            args = {}\n        else:\n            _args = dict(ic.io(*args)) if len(args) > 0 else {}\n            args = {}\n            for k, v in _args.items():\n                if any(c in self.forbidden for c in k):\n                    assert isinstance(\n                        v, (dict, AttrDict)\n                    ), f\"Input `{v}` can't be a list\"\n                    given_input_to_ad = {**v, **given_input_to_ad}\n                else:\n                    args = {**{k: v}, **args}\n\n        given_input_to_ad = {**kwargs, **given_input_to_ad, **args}\n        for name, value in given_input_to_ad.items():\n            setattr(self, str(name), self._wrap(value))\n\n    def items(self):\n        return self.__dict__.items()\n\n    def keys(self):\n        return self.__dict__.keys()\n\n    def values(self):\n        return self.__dict__.values()\n\n    def __json__(self):\n        return self.to_dict()\n\n    def _wrap(self, value):\n        if isinstance(value, (L, tuple, list, set, frozenset)):\n            value = type(value)([self._wrap(v) for v in value])\n            if isinstance(value, (list, L)):\n                value = L(value)\n            return value\n        else:\n            return (\n                AttrDict(given_input_to_ad=value) if isinstance(value, dict) else value\n            )\n\n    __getitem__ = lambda self, x: (\n        AttrDict({_x: self[_x] for _x in x})\n        if isinstance(x, (list, L))\n        else getattr(self, str(x))\n    )\n    __setitem__ = lambda self, k, v: setattr(self, str(k), self._wrap(v))\n\n    def update(self, dict):\n        for k, v in dict.items():\n            self[k] = v\n\n    def get(self, key, default=None):\n        key = str(key)\n        return self[key] if key in self else default\n\n    def __iter__(self):\n        return iter(self.keys())\n\n    def __len__(self):\n        return len(self.keys())\n\n    def __repr__(self):\n        return f\"\\n```↯ AttrDict ↯\\n{self.summary()}\\n```\\n\"\n\n    def __dir__(self):\n        return self.__dict__.keys()\n\n    def __contains__(self, key):\n        key = str(key)\n        if \".\" not in key:\n            return key in self.__dict__.keys()\n        else:\n            d = self\n            for _k in key.split(\".\"):\n                try:\n                    d = d[_k]\n                except AttributeError:\n                    return False\n            return True\n\n    def __delitem__(self, key):\n        key = str(key)\n        del self.__dict__[key]\n\n    def map(self, func):\n        for k in dir(self):\n            v = self[k]\n            if isinstance(v, AttrDict):\n                v.map(func)\n            elif isinstance(v, (L, tuple, list, set, frozenset)):\n                v = [_v.map(func) if isinstance(_v, AttrDict) else func(_v) for _v in v]\n            else:\n                v = func(v)\n            self[k] = v\n\n    def drop(self, key):\n        if key in self:\n            del self[key]\n        for k in dir(self):\n            v = self[k]\n            if isinstance(v, AttrDict):\n                v.drop(key)\n            if isinstance(v, (L, tuple, list, set, frozenset)):\n                v = [_v.drop(key) for _v in v if isinstance(_v, AttrDict)]\n\n    def to_dict(self):\n        d = {}\n        for k in self.__dict__.keys():  # can't use dir here\n            v = self[k]\n            if isinstance(v, AttrDict):\n                v = v.to_dict()\n            if isinstance(v, (L, tuple, list, set, frozenset)):\n                v = [_v.to_dict() if isinstance(_v, AttrDict) else _v for _v in v]\n            d[k] = v\n        return d\n\n    dict = to_dict\n\n    def pretty(self, print_with_logger=False, *args, **kwargs):\n        pretty_json(\n            self.to_dict(), print_with_logger=print_with_logger, *args, **kwargs\n        )\n\n    def __eq__(self, other):\n        return AttrDict(given_input_to_ad=other).to_dict() == self.to_dict()\n\n    def find_address(self, key, current_path=\"\"):\n        addresses = []\n        for k in self.keys():\n            if current_path:\n                new_path = f\"{current_path}.{k}\"\n            else:\n                new_path = k\n\n            if k == key:\n                addresses.append(new_path)\n\n            if isinstance(self[k], AttrDict):\n                addresses.extend(self[k].find_address(key, new_path))\n\n            elif isinstance(self[k], (L, tuple, list, set, frozenset)):\n                for i, item in enumerate(self[k]):\n                    if isinstance(item, AttrDict):\n                        addresses.extend(item.find_address(key, f\"{new_path}.{i}\"))\n        return addresses\n\n    def summary(self, current_path=\"\", depth=0, sep=\"  \", max_items=10):\n        max_items = int(os.environ.get(\"AD_MAX_ITEMS\", max_items))\n        sep = os.environ.get(\"AD_SEP\", sep)\n\n        def format_path(path, key):\n            return f\"{path}.{key}\" if path else key\n\n        def format_item(key, item, path, depth, sep):\n            import numpy as np\n            import pandas as pd\n\n            try:\n                import torch\n            except ModuleNotFoundError:\n\n                class Torch:\n                    Tensor = type(None)\n\n                torch = Torch()\n\n            if isinstance(item, (pd.DataFrame,)):\n                return f\"{sep * depth}{key} - {type(item).__name__} - shape {item.shape} - columns {item.columns} - {hash_pandas_dataframe(item)}\\n\"\n            if isinstance(item, AttrDict) or hasattr(item, \"keys\"):\n                item = AttrDict(**item)\n                return f\"{sep*depth}{key}\\n\" + item.summary(path, depth + 1, sep)\n            elif isinstance(item, (list, tuple, set, frozenset, L)):\n                return summarize_collection(key, item, path, depth + 1, sep)\n            elif isinstance(item, (torch.Tensor, np.ndarray)):\n                is_np = False\n                if isinstance(item, np.ndarray):\n                    is_np = True\n                    item = torch.tensor(item)\n                is_np = \"🔦\" if not is_np else \"np.\"\n                return f\"{sep * depth}{key} - {is_np}{item} - {hash_tensor(item)}\\n\"\n\n            else:\n                is_multiline = False\n                ogitem = item\n                if isinstance(item, (str, P)):\n                    item = str(item)\n                    is_multiline = \"\\n\" in item\n                    _sep = \" ...\\n...\\n...\\n...\\n... \" if is_multiline else \".........\"\n                    if len(item) > 100:\n                        item = item[:35] + _sep + item[-35:]\n                    if is_multiline:\n                        _item = item.split(\"\\n\")\n                        _item = \"\\n\".join([f\"{sep*(depth+1)}{l}\" for l in _item])\n                        item = f\"↓\\n{sep*(depth+1)}```\\n{_item}\\n{sep*(depth+1)}```\"\n                multiline = \"\" if not is_multiline else \"Multiline \"\n                return f\"{sep * depth}{key} - {item} (🏷️ {multiline}{type(ogitem).__name__})\\n\"\n\n        def summarize_collection(key, collection, path, d, s):\n            summary_str = f\"{s * (d - 1)}{key}\\n\"\n            for i, item in enumerate(collection):\n                item_path = format_path(path, i)\n                if i < max_items:\n                    summary_str += format_item(i, item, item_path, d, s)\n                else:\n                    summary_str += (\n                        f\"{s*d}... {len(collection) - max_items} more items ...\\n\"\n                    )\n                    break\n            return summary_str\n\n        summary_str = \"\"\n        for ix, key in enumerate(self.keys()):\n            if ix >= max_items:\n                summary_str += (\n                    f\"{sep*depth} ... {len(self.keys()) - max_items} more keys ...\\n\"\n                )\n                break\n            new_path = format_path(current_path, key)\n            summary_str += format_item(key, self[key], new_path, depth, sep)\n        return summary_str\n\n    def print_summary(self, **kwargs):\n        from builtins import print\n\n        print(self.summary(**kwargs))\n\n    def write_summary(self, to, **kwargs):\n        writelines(self.summary(**kwargs).split(\"\\n\"), to)\n\n    def fetch(self, addr):\n        if isinstance(addr, (list, L)):\n            return L([self.fetch(_addr) for _addr in addr])\n\n        o = self\n        for p in addr.split(\".\"):\n            try:\n                o = o[int(p)]\n            except:\n                o = o[p]\n        return o\n\n\nx = AD(x=1)\nx\n\n\n---------------------------------------------------------------------------\nNameError                                 Traceback (most recent call last)\nFile ~/miniconda3/miniconda3/envs/ts/lib/python3.10/site-packages/IPython/core/formatters.py:708, in PlainTextFormatter.__call__(self, obj)\n    701 stream = StringIO()\n    702 printer = pretty.RepresentationPrinter(stream, self.verbose,\n    703     self.max_width, self.newline,\n    704     max_seq_length=self.max_seq_length,\n    705     singleton_pprinters=self.singleton_printers,\n    706     type_pprinters=self.type_printers,\n    707     deferred_pprinters=self.deferred_printers)\n--> 708 printer.pretty(obj)\n    709 printer.flush()\n    710 return stream.getvalue()\n\nFile ~/miniconda3/miniconda3/envs/ts/lib/python3.10/site-packages/IPython/lib/pretty.py:410, in RepresentationPrinter.pretty(self, obj)\n    407                         return meth(obj, self, cycle)\n    408                 if cls is not object \\\n    409                         and callable(cls.__dict__.get('__repr__')):\n--> 410                     return _repr_pprint(obj, self, cycle)\n    412     return _default_pprint(obj, self, cycle)\n    413 finally:\n\nFile ~/miniconda3/miniconda3/envs/ts/lib/python3.10/site-packages/IPython/lib/pretty.py:778, in _repr_pprint(obj, p, cycle)\n    776 \"\"\"A pprint that just redirects to the normal repr function.\"\"\"\n    777 # Find newlines and replace them with p.break_()\n--> 778 output = repr(obj)\n    779 lines = output.splitlines()\n    780 with p.group():\n\nCell In[8], line 69, in AD.__repr__(self)\n     68 def __repr__(self):\n---> 69     return f\"\\n```↯ AttrDict ↯\\n{self.summary()}\\n```\\n\"\n\nCell In[8], line 154, in AD.summary(self, current_path, depth, sep, max_items)\n    153 def summary(self, current_path=\"\", depth=0, sep=\"  \", max_items=10):\n--> 154     max_items = int(os.environ.get(\"AD_MAX_ITEMS\", max_items))\n    155     sep = os.environ.get(\"AD_SEP\", sep)\n    157     def format_path(path, key):\n\nNameError: name 'os' is not defined",
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+    "title": "Decorator Utilites",
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+    "text": "check_kwargs_not_none\n\n check_kwargs_not_none (func)\n\n*A decorator that checks if any keyword argument is None. Raises a ValueError if any argument is None.\nArgs: func: The function to be decorated.\nReturns: The decorated function.\nRaises: ValueError: If any keyword argument is None.*\n\n\n\nio\n\n io (func)\n\n*A decorator that inspects the inputs and outputs of a function.\nArgs: func: The function to be decorated.\nReturns: The decorated function.*\n\n\n\ntimeit\n\n timeit (func)\n\n*A decorator that measures the execution time of a function.\nArgs: func (callable): The function to be timed.\nReturns: callable: The wrapped function.\nExample: @timeit def my_function(): # code to be timed pass\nmy_function()  # prints the execution time of my_function*\n\n\n\nwarn_on_fail\n\n warn_on_fail (func)\n\n\n\n\nformat\n\n format (input)\n\n\n@timeit\n@io\ndef foo(a, b):\n    \"\"\"\n    This function takes two arguments, `a` and `b`, and returns their sum.\n\n    Parameters:\n    a (int): The first number.\n    b (int): The second number.\n\n    Returns:\n    int: The sum of `a` and `b`.\n    \"\"\"\n    import time\n\n    time.sleep(1)\n    return a + b\n\n\nfoo(10, 11)\n\n══════════════════════════════════════════════════════════════════\nINPUTS:ARGS:\ntuple of 2 items\n    int: 10\n    int: 11\n══════════════════════════════════════════════════════════════════\n══════════════════════════════════════════════════════════════════\nOUTPUTS:\nint: 21\n══════════════════════════════════════════════════════════════════\n[08/24/24 12:50:36] INFO     foo took 1.00 seconds to execute                                                                      d=152002;file://<ipython-input-1-6ac2073623b5>:47\u001b\\<ipython-input-1-6ac2073623b5>;;\u001b\\:d=283174;file://<ipython-input-1-6ac2073623b5>:47#wrapper:47\u001b\\wrapper:47;;\u001b\\\n\n\n21\n\n\n\n@check_kwargs_not_none\n@io\ndef foo(*, a=None, b=None):\n    return a + b\n\n\nfoo(a=None, b=10)\n\n\n---------------------------------------------------------------------------\nValueError                                Traceback (most recent call last)\nCell In[34], line 6\n      1 @check_kwargs_not_none\n      2 @io\n      3 def foo(*, a=None, b=None):\n      4     return a + b\n----> 6 foo(a=None, b=10)\n\nCell In[32], line 32, in check_kwargs_not_none.<locals>.wrapper(*args, **kwargs)\n     30 for key, value in kwargs.items():\n     31     if value is None:\n---> 32         raise ValueError(f\"Input argument '{key}' cannot be None\")\n     33 return func(*args, **kwargs)\n\nValueError: Input argument 'a' cannot be None\n\n\n\n\nimport nbdev\n\nnbdev.nbdev_export()",
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+    "text": "AttrDict\n\n AttrDict (*args, given_input_to_ad=None, **kwargs)\n\n*Utility class to interact with a dictionary as if it were an object. AD is an alias to this class\nFEATURES: 0. Access and modify keys (including nested keys) as if they were object attributes, supporting tab-completion. Example: self.key1.key2[0].key3 1. Keys and values are recursively converted to AttrDict instances. 2. Pretty-print the dictionary using print. 3. Convert the entire structure to a regular dictionary at any time using self.to_dict() / self.dict(). 3. Recursively remove keys using self.drop(key) from a JSON object. 4. Apply a function to all values at all levels using map.\nGOTCHAS: 1. All integer keys are implicitly converted to strings due to the enforced self.key format. 2. You can still use self[int], but this internally converts the integer to a string.\nMETHODS: - items(): Return the items of the AttrDict as key-value pairs. - keys(): Return the keys of the AttrDict. - values(): Return the values of the AttrDict. - update(dict): Update the AttrDict with key-value pairs from another dictionary. - get(key, default=None): Get the value associated with a key, with an optional default value. - __iter__(): Allow iteration over the keys of the AttrDict. - __len__(): Return the number of keys in the AttrDict. - __repr__(): Return a string representation of the AttrDict. - __dir__(): List the keys of the AttrDict as attributes. - __contains__(key): Check if a key exists in the AttrDict, use ‘a.b.c’ notation to directly check for a nested attribute. - __delitem__(key): Delete a key from the AttrDict. - map(func): Apply a function to all values in the AttrDict. - drop(key): Recursively remove a key and its values from the AttrDict. - to_dict(): Convert the AttrDict and its nested structure to a regular dictionary. - pretty(print_with_logger=False, *args, **kwargs): Pretty-print the AttrDict as JSON. - __eq__(other): Compare the AttrDict with another dictionary for equality. - find_address(key, current_path=\"\"): Find and return all addresses (paths) of a given key in the AttrDict. - summary(current_path='', summary_str='', depth=0, sep='  '): Generate a summary of the structure and values in the AttrDict. - write_summary(to, **kwargs): Write the summary to a file or stream. - fetch(addr): Retrieve a value at a specified address (path).\nPARAMETERS: - data (dict, optional): Initial data to populate the AttrDict.\nUSAGE: - Create an AttrDict instance by providing an optional initial dictionary, and then access and manipulate its contents as if they were object attributes.\nEXAMPLE:\nmy_dict = {'name': 'John', 'age': 30, 'address': {'city': 'New York', 'zip': '10001'}}\nattr_dict = AttrDict(my_dict)\nprint(attr_dict.name)  # Access values like attributes\nattr_dict.address.city = 'Los Angeles'  # Modify nested values\n```*\n\n\n::: {#bfb4146f-90bf-42bc-bf40-69fb6d612ea2 .cell tags='[]' execution_count=5}\n``` {.python .cell-code}\nxx = AttrDict({\"a\": 1, \"b\": [{\"c\": 2, \"d\": 4}, {\"e\": 3}], \"f\": {\"g\": {\"h\": 20}}})\nprint(type(xx.b[0]))\nprint(type(xx.to_dict()[\"b\"][0]))\nprint(\"f.g.h\" in xx)\nxx.pretty()\n\n<class 'torch_snippets.markup2.AttrDict'>\n<class 'dict'>\nTrue\n{\n    \"a\": 1,\n    \"b\": [\n        {\n            \"c\": 2,\n            \"d\": 4\n        },\n        {\n            \"e\": 3\n        }\n    ],\n    \"f\": {\n        \"g\": {\n            \"h\": 20\n        }\n    }\n}\n\n:::\n\nx = {\"abc\": {\"b\": 10, \"c\": 11}, \"d\": {\"e\": {\"f\": [2, {\"abc\": \"pqrs\"}, 2.234]}}}\n\ny = AttrDict(x)\n\nassert y.abc.b == 10\nassert y.d.e.f == [2, {\"abc\": \"pqrs\"}, 2.234]\n\ny.d.e.g = 11\n\n# del y.abc.c\n# OR\ndel y[\"abc\"][\"c\"]\n\nassert y.to_dict() == {\n    \"abc\": {\"b\": 10},\n    \"d\": {\"e\": {\"f\": [2, {\"abc\": \"pqrs\"}, 2.234], \"g\": 11}},\n}\n\ny.pretty(indent=2)\n\nassert \"abc\" in y\nassert \"def\" not in y\nprint(\"e\" in y.d)\n\n{\n  \"abc\": {\n    \"b\": 10\n  },\n  \"d\": {\n    \"e\": {\n      \"f\": [\n        2,\n        {\n          \"abc\": \"pqrs\"\n        },\n        2.234\n      ],\n      \"g\": 11\n    }\n  }\n}\nTrue\n\n\n\nx = {\"abc\": {\"b\": 10, \"c\": 11}, \"d\": {\"e\": {\"f\": [2, {\"abc\": \"pqrs\"}, 2.234]}}}\ny = AttrDict(x)\ny.abc[[\"b\", \"c\"]]\n\n\n```↯ AttrDict ↯\nb - 10 (🏷️ int)\nc - 11 (🏷️ int)\n\n```\n\n\n\n\n\nwrite_json\n\n write_json (obj, fpath, silent=False)\n\n\n\n\nread_json\n\n read_json (fpath)\n\n\ntry:\n    import torch\n\n    d = AD(a=torch.Tensor([1, 2, 3]), b=\"hello\")\n    write_json(d, \"/tmp/tmp.json\")\n    print(\"\\n\".join(readlines(\"/tmp/tmp.json\")))\nexcept ModuleNotFoundError:\n    ...\n\n[08/24/24 12:50:35] INFO     loaded 8 lines                                                                                        d=266370;file://<ipython-input-1-e6d68859b80d>:6\u001b\\<ipython-input-1-e6d68859b80d>;;\u001b\\:d=65937;file://<ipython-input-1-e6d68859b80d>:6#<module>:6\u001b\\<module>:6;;\u001b\\\n{\n\"a\": [\n1.0,\n2.0,\n3.0\n],\n\"b\": \"hello\"\n}\n\n\n\nd = [1, {1: 1, 2: 2}, 3]\n\npretty_json({1: 1, 2: 2})\npretty_json(d)\n\nf = write_json(d, \"/tmp/test.json\")\nprint(f)\nread_json(f)\n\n/tmp/test.json\n\n\n[1, {'1': 1, '2': 2}, 3]\n\n\n\n\n\nread_jsonl\n\n read_jsonl (file)\n\n\n\n\nwrite_jsonl\n\n write_jsonl (items, dest, mode='a')\n\n\n\n\nwrite_yaml\n\n write_yaml (content, fpath)\n\n\n\n\nread_yaml\n\n read_yaml (file)\n\n\n\n\nwrite_xml\n\n write_xml (data:Union[torch_snippets.markup2.AttrDict,dict],\n            file_path:Union[str,pathlib.Path])\n\n\n\n\nread_xml\n\n read_xml (file_path:Union[str,pathlib.Path])\n\nRead xml data as a dictionary\n\ny.to_dict()\n\n{'abc': {'b': 10, 'c': 11}, 'd': {'e': {'f': [2, {'abc': 'pqrs'}, 2.234]}}}\n\n\n\ny\n\n\n```↯ AttrDict ↯\nabc\n  b - 10 (🏷️ int)\n  c - 11 (🏷️ int)\nd\n  e\n    f\n      0 - 2 (🏷️ int)\n      1\n        abc - pqrs (🏷️ str)\n      2 - 2.234 (🏷️ float)\n\n```",
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+    "text": "ishow\n\n ishow (im, df, additional_attrs=None, **kwargs)\n\n\n\n\ndf2graph_nodes\n\n df2graph_nodes (df, text_attr='text', additional_attrs=None)\n\n\n\n\nviz2\n\n viz2 (graph, node_attrs=None, undirected=True, **kwargs)\n\n\n\n\nconvert_to_nx\n\n convert_to_nx (g, node_attrs=None, undirected=True)\n\n\n\n\ntolist\n\n tolist (i)\n\n\n\n\ntonp\n\n tonp (i)\n\n\n\n\nplot_graph\n\n plot_graph (g, output, im=None, **kwargs)\n\n\n\n\nplot_image\n\n plot_image (p, image, sz)\n\n\n\n\nto_networkx\n\n to_networkx (data, node_attrs:Optional[Iterable[str]]=None,\n              edge_attrs:Optional[Iterable[str]]=None,\n              graph_attrs:Optional[Iterable[str]]=None,\n              to_undirected:Union[bool,str,NoneType]=False,\n              remove_self_loops:bool=False)",
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+    "text": "SK-Legos\nUtilities to do common ML tasks\n\nYou can find 1. train_test_split which also resets the dataframes’ indexes 2. MakeFrame 3. ImputeMisingValues 4. Cat2Num 5. Other scikit-lego blocks that I use a lot\n\n\nMakeFrame\n\n MakeFrame (column_names)\n\nConvert sklearn’s output to a pandas dataframe Especially useful when working with an ensemble of models\nUsage\nCall MakeFrame as the last component in your pipeline with the desired column names.\npipeline = Pipeline([\n    ...,\n    ('output', MakeFrame(['outlier', 'class'])),\n])\n\nRefer to this notebook for an example\n\n\n\n\nImputeMissingValues\n\n ImputeMissingValues (num_mode=<function mean at 0x10695b6f0>,\n                      cat_mode='MISSING')\n\n*DataFrame input - DataFrame output During fit - 1. Store imputable value for each column During transform - 2. Impute missing values with imputable value 3. Create a ’{col}_na’ boolean column to tell if cells contained missing value*\n\n\n\nLambdaTransformer\n\n LambdaTransformer (fn)\n\n*Base class for all estimators in scikit-learn.\nInheriting from this class provides default implementations of:\n\nsetting and getting parameters used by GridSearchCV and friends;\ntextual and HTML representation displayed in terminals and IDEs;\nestimator serialization;\nparameters validation;\ndata validation;\nfeature names validation.\n\nRead more in the :ref:User Guide <rolling_your_own_estimator>.*\n\n\n\nMakeFrame\n\n MakeFrame (column_names)\n\n*Base class for all estimators in scikit-learn.\nInheriting from this class provides default implementations of:\n\nsetting and getting parameters used by GridSearchCV and friends;\ntextual and HTML representation displayed in terminals and IDEs;\nestimator serialization;\nparameters validation;\ndata validation;\nfeature names validation.\n\nRead more in the :ref:User Guide <rolling_your_own_estimator>.*\n\n\n\nCat2Num\n\n Cat2Num ()\n\n*Base class for all estimators in scikit-learn.\nInheriting from this class provides default implementations of:\n\nsetting and getting parameters used by GridSearchCV and friends;\ntextual and HTML representation displayed in terminals and IDEs;\nestimator serialization;\nparameters validation;\ndata validation;\nfeature names validation.\n\nRead more in the :ref:User Guide <rolling_your_own_estimator>.*\n\n\n\nSplitDateColumn\n\n SplitDateColumn (column_names, has_date, has_time, date_format=None)\n\n*Base class for all estimators in scikit-learn.\nInheriting from this class provides default implementations of:\n\nsetting and getting parameters used by GridSearchCV and friends;\ntextual and HTML representation displayed in terminals and IDEs;\nestimator serialization;\nparameters validation;\ndata validation;\nfeature names validation.\n\nRead more in the :ref:User Guide <rolling_your_own_estimator>.*",
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+    "text": "Show is intended to show numpy-arrays/PIL-images\n\nfrom torch_snippets import *\n\nim = np.random.rand(100, 100)\nshow(im)\n\n\n\n\n\n\n\n\n\nshow(im, sz=4)\n\n\n\n\n\n\n\n\nShow will even accept pytorch Tensors and show them as images, even if they are on GPU and have channels first\nIt can accept bounding boxes as tuples of (x,y,X,Y) which can be integers (i.e., absolute coordinates) or fractions (between \\([0,1]\\)). There’s provision to give bb_colors and texts as well\n\nshow(im, bbs=[(0, 0, 0.5, 0.35), (0, 0.2, 0.35, 0.95)])\n\nshow(im, bbs=[(0, 0, 0.5, 0.35), (0, 0.2, 0.35, 0.95)], bb_colors=[\"r\", \"g\"])\n\nshow(\n    im,\n    bbs=[(0, 0, 0.5, 0.35), (0, 0.2, 0.35, 0.95)],\n    bb_colors=[\"b\", \"g\"],\n    texts=[\"bb1\", \"bb2\"],\n    sz=10,\n    text_sz=15,\n)\n\nClipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n\n\n\n\n\n\n\n\n\nClipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n\n\n\n\n\n\n\n\n\nClipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n\n\n\n\n\n\n\n\n\nIt can also read a file path directly and display the image\n\nshow(\"../docs/images/company_logo_big.png\", sz=3)\n\n\n\n\n\n\n\n\nif the input is not an image or string, show will simply display the given input as intended by jupyter notebook\n\ndf = pd.DataFrame(np.random.rand(100, 2))\nshow(df)\n\n\n\n\n\n\n\n\n0\n1\n\n\n\n\n0\n0.242478\n0.929517\n\n\n1\n0.971890\n0.506750\n\n\n2\n0.139826\n0.753227\n\n\n3\n0.864799\n0.524166\n\n\n4\n0.563610\n0.135709\n\n\n...\n...\n...\n\n\n95\n0.379874\n0.639771\n\n\n96\n0.993731\n0.811343\n\n\n97\n0.621683\n0.763054\n\n\n98\n0.844509\n0.915156\n\n\n99\n0.314272\n0.392418\n\n\n\n\n100 rows × 2 columns\n\n\n\n\nchart = Chart(df).mark_circle().encode(x=\"0:Q\", y=\"1:Q\")\nshow(chart.interactive())\n\n\n\n\n\n\n\n\ndisplay multiple images Subplots is a wapper around plt.subplots that accepts a list of images, number of columns as nc and additional kwargs\n\nfrom torch_snippets import subplots\n\nims = [np.random.rand(100, 100) for _ in range(16)]\n\nsubplots(ims, nc=4, sz=5)\n\n\n\n\n\n\n\n\n\nsubplots(\n    ims,\n    nc=2,\n    sz=(5, 15),\n    suptitle=\"RANDOM IMAGES\",\n    titles=[f\"random_{i}\" for i in range(16)],\n)",
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+    "href": "show.html#subplots",
+    "title": "Show",
+    "section": "",
+    "text": "display multiple images Subplots is a wapper around plt.subplots that accepts a list of images, number of columns as nc and additional kwargs\n\nfrom torch_snippets import subplots\n\nims = [np.random.rand(100, 100) for _ in range(16)]\n\nsubplots(ims, nc=4, sz=5)\n\n\n\n\n\n\n\n\n\nsubplots(\n    ims,\n    nc=2,\n    sz=(5, 15),\n    suptitle=\"RANDOM IMAGES\",\n    titles=[f\"random_{i}\" for i in range(16)],\n)",
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+    "href": "charts.html",
+    "title": "Altair and Other Charts",
+    "section": "",
+    "text": "Altair and Other Charts\n\n\nfrom torch_snippets.loader import *\nfrom sklearn.datasets import make_moons\n\nnp.random.seed(10)\nx, y = make_moons(1000, noise=0.1)\ndf = pd.DataFrame({\"x1\": x[:, 0], \"x2\": x[:, 1], \"y\": y})\n\nChart(df).mark_circle().encode(x=\"x1:Q\", y=\"x2:Q\", color=\"y:N\").interactive()\n\n\n\n\n\n\n\nRefer to altair-viz.github.io for more awesome charts.\ntorch-snippets exposes a confusion matrix function CM as an example\n\nMethod 1\n\nn = 10\na = \"qwertyuiopasdfghjklzxcvbnm\"\ntruth = np.random.randint(4, size=1000000)\npred = np.random.randint(4, size=1000000)\nshow(CM(truth=truth, pred=pred, mapping={i: a for i, a in enumerate(a)}))\n# mapping is optional\n\n              precision    recall  f1-score   support\n\n           0       0.25      0.25      0.25    250150\n           1       0.25      0.25      0.25    250245\n           2       0.25      0.25      0.25    249836\n           3       0.25      0.25      0.25    249769\n\n    accuracy                           0.25   1000000\n   macro avg       0.25      0.25      0.25   1000000\nweighted avg       0.25      0.25      0.25   1000000\n\n\n\n\n\n\n\n\n\n\n\nMethod 2\n\ndf = pd.DataFrame(\n    {\n        \"truth\": [randint(n) for _ in range(1000)],\n        \"pred\": [randint(n) for _ in range(1000)],\n    }\n)\nshow(CM(df, \"truth\", \"pred\", mapping={i: a for i, a in enumerate(a)}))\n# mapping is optional\n\n              precision    recall  f1-score   support\n\n           0       0.13      0.14      0.13        92\n           1       0.08      0.09      0.08       101\n           2       0.13      0.12      0.13       107\n           3       0.06      0.06      0.06       105\n           4       0.12      0.11      0.11        94\n           5       0.12      0.09      0.10       115\n           6       0.08      0.10      0.09        88\n           7       0.08      0.07      0.08       113\n           8       0.09      0.09      0.09        99\n           9       0.12      0.15      0.13        86\n\n    accuracy                           0.10      1000\n   macro avg       0.10      0.10      0.10      1000\nweighted avg       0.10      0.10      0.10      1000\n\n\n\n\n\n\n\n\n\n\n\nMethod 3\n\ndf = pd.DataFrame(\n    {\n        \"truth\": [choose(\"abcd\") for _ in range(1000)],\n        \"pred\": [choose(\"abcd\") for _ in range(1000)],\n    }\n)\nshow(CM(df, \"truth\", \"pred\"))\n# mapping is optional\n\n              precision    recall  f1-score   support\n\n           a       0.25      0.29      0.27       229\n           b       0.28      0.29      0.28       256\n           c       0.27      0.24      0.26       267\n           d       0.26      0.25      0.25       248\n\n    accuracy                           0.27      1000\n   macro avg       0.26      0.27      0.26      1000\nweighted avg       0.27      0.27      0.26      1000\n\n\n\n\n\n\n\n\n\n\n\n\nspider\n\n spider (df, id_column=None, title=None, max_values=None, padding=1.25,\n         global_scale=False, ax=None, sz=10)\n\n*Plot a spider chart based on the given dataframe.\nParameters: - df: pandas DataFrame The input dataframe containing the data to be plotted. - id_column: str, optional The column name to be used as the identifier for each data point. If not provided, the index of the dataframe will be used. - title: str, optional The title of the spider chart. - max_values: dict, optional A dictionary specifying the maximum values for each category. If not provided, the maximum values will be calculated based on the data. - padding: float, optional The padding factor to be applied when calculating the maximum values. Default is 1.25. - global_scale: bool or float, optional If False, each category will have its own maximum value. If True, a single maximum value will be used for all categories. If a float value is provided, it will be used as the maximum value for all categories. - ax: matplotlib Axes, optional The axes on which to plot the spider chart. If not provided, a new figure and axes will be created. - sz: float, optional The size of the figure (both width and height) in inches. Default is 10.\nReturns: - None\nExample usage: spider(df, id_column=‘model’, title=‘Spider Chart’, max_values={‘category1’: 10, ‘category2’: 20}, padding=1.5)*\n\nimport pandas as pd\n\nspider(\n    pd.DataFrame(\n        {\n            \"x\": [*\"abcde\"],\n            \"c1\": [10, 11, 12, 13, 14],\n            \"c2\": [0.1, 0.3, 0.4, 0.1, 0.9],\n            \"c3\": [1e5, 2e5, 3.5e5, 8e4, 5e4],\n            \"c4\": [9, 12, 5, 2, 0.2],\n            \"test\": [1, 1, 1, 1, 5],\n        },\n        index=[*\"abcde\"],\n    ),\n    title=\"Sample Spider\",\n    padding=1.1,\n)\n\n\n\n\n\n\n\n\n\n\n\nUpSetAltair\n\n UpSetAltair (data=None, title='', subtitle='', sets=None, abbre=None,\n              sort_by='frequency', sort_order='ascending', width=1200,\n              height=700, height_ratio=0.6,\n              horizontal_bar_chart_width=300, color_range=['#55A8DB',\n              '#3070B5', '#30363F', '#F1AD60', '#DF6234', '#BDC6CA'],\n              highlight_color='#EA4667', glyph_size=200,\n              set_label_bg_size=1000, line_connection_size=2,\n              horizontal_bar_size=20, vertical_bar_label_size=16,\n              vertical_bar_padding=20)\n\n*This function generates Altair-based interactive UpSet plots.\nParameters: - data (pandas.DataFrame): Tabular data containing the membership of each element (row) in exclusive intersecting sets (column). - sets (list): List of set names of interest to show in the UpSet plots. This list reflects the order of sets to be shown in the plots as well. - abbre (list): Abbreviated set names. - sort_by (str): “frequency” or “degree” - sort_order (str): “ascending” or “descending” - width (int): Vertical size of the UpSet plot. - height (int): Horizontal size of the UpSet plot. - height_ratio (float): Ratio of height between upper and under views, ranges from 0 to 1. - horizontal_bar_chart_width (int): Width of horizontal bar chart on the bottom-right. - color_range (list): Color to encode sets. - highlight_color (str): Color to encode intersecting sets upon mouse hover. - glyph_size (int): Size of UpSet glyph (⬤). - set_label_bg_size (int): Size of label background in the horizontal bar chart. - line_connection_size (int): width of lines in matrix view. - horizontal_bar_size (int): Height of bars in the horizontal bar chart. - vertical_bar_label_size (int): Font size of texts in the vertical bar chart on the top. - vertical_bar_padding (int): Gap between a pair of bars in the vertical bar charts.*\n\n\n\nupsetaltair_top_level_configuration\n\n upsetaltair_top_level_configuration (base, legend_orient='top-left',\n                                      legend_symbol_size=30)\n\n*Configure the top-level settings for an UpSet plot in Altair.\nParameters: - base: The base chart to configure. - legend_orient: The orientation of the legend. Default is “top-left”. - legend_symbol_size: The size of the legend symbols. Default is 30.\nReturns: - The configured chart.*\n\ndf\n\n\n\n\n\n\n\n\ntruth\npred\n\n\n\n\n0\nc\nd\n\n\n1\nc\nc\n\n\n2\nd\nd\n\n\n3\nc\na\n\n\n4\nd\nc\n\n\n...\n...\n...\n\n\n995\nc\nc\n\n\n996\na\nc\n\n\n997\nb\na\n\n\n998\nb\nc\n\n\n999\na\nd\n\n\n\n\n1000 rows × 2 columns\n\n\n\n\n# import numpy as np\n\n# i = np.random.randn(300, 7) > 0.33\n# df = pd.DataFrame(i.astype(int))\n# df.columns = [rand() for _ in range(len(df.columns))]\n# show(df)\n\n# UpSetAltair(\n#     df,\n#     sets=list(df.columns),\n#     abbre=list(df.columns),\n#     sort_by=\"frequencey\",\n#     sort_order=\"ascending\",\n# )\n\ndf\n\n\n\n\n4wST3l\nv6Vv72\nWKSGX4\nLBlidv\ng0LDKa\nxpK2f5\npW4oKO\n\n\n\n\n0\n0\n1\n0\n1\n0\n0\n1\n\n\n1\n1\n0\n0\n1\n0\n1\n0\n\n\n2\n1\n0\n1\n0\n0\n1\n0\n\n\n3\n1\n0\n0\n0\n0\n0\n1\n\n\n4\n0\n0\n1\n0\n1\n0\n1\n\n\n5\n0\n0\n0\n1\n0\n0\n0\n\n\n6\n1\n0\n0\n1\n1\n0\n1\n\n\n7\n1\n1\n1\n1\n0\n1\n1\n\n\n8\n1\n0\n0\n1\n0\n1\n1\n\n\n9\n1\n0\n0\n1\n0\n0\n0\n\n\n10\n1\n0\n0\n0\n0\n0\n1\n\n\n11\n1\n0\n1\n1\n0\n0\n0\n\n\n12\n0\n1\n0\n1\n0\n0\n1\n\n\n13\n1\n0\n0\n1\n0\n1\n1\n\n\n14\n1\n1\n0\n1\n0\n0\n0\n\n\n...\n...\n...\n...\n...\n...\n...\n...\n\n\n285\n1\n0\n0\n0\n0\n0\n0\n\n\n286\n1\n0\n0\n1\n1\n1\n1\n\n\n287\n0\n0\n1\n1\n0\n1\n0\n\n\n288\n0\n1\n1\n1\n0\n0\n0\n\n\n289\n0\n1\n1\n1\n0\n0\n0\n\n\n290\n0\n1\n1\n1\n1\n0\n0\n\n\n291\n0\n0\n1\n0\n1\n0\n0\n\n\n292\n0\n0\n1\n1\n0\n0\n1\n\n\n293\n1\n0\n0\n0\n0\n1\n0\n\n\n294\n0\n0\n0\n0\n0\n0\n1\n\n\n295\n0\n1\n1\n1\n0\n0\n0\n\n\n296\n0\n0\n0\n1\n0\n1\n0\n\n\n297\n1\n0\n1\n0\n0\n0\n1\n\n\n298\n0\n1\n1\n0\n1\n0\n0\n\n\n299\n0\n0\n1\n0\n0\n0\n0\n\n\n\n\n\n\n/opt/miniconda3/lib/python3.12/site-packages/altair/utils/deprecation.py:65: AltairDeprecationWarning: 'selection_multi' is deprecated.  Use 'selection_point'\n  warnings.warn(message, AltairDeprecationWarning, stacklevel=1)\n/opt/miniconda3/lib/python3.12/site-packages/altair/utils/deprecation.py:65: AltairDeprecationWarning: 'selection_single' is deprecated.  Use 'selection_point'\n  warnings.warn(message, AltairDeprecationWarning, stacklevel=1)\n\n\n\n---------------------------------------------------------------------------\nSchemaValidationError                     Traceback (most recent call last)\nCell In[14], line 8\n      5 df.columns = [rand() for _ in range(len(df.columns))]\n      6 show(df)\n----> 8 UpSetAltair(\n      9     df,\n     10     sets=list(df.columns),\n     11     abbre=list(df.columns),\n     12     sort_by=\"frequencey\",\n     13     sort_order=\"ascending\",\n     14 )\n\nCell In[10], line 239, in UpSetAltair(data, title, subtitle, sets, abbre, sort_by, sort_order, width, height, height_ratio, horizontal_bar_chart_width, color_range, highlight_color, glyph_size, set_label_bg_size, line_connection_size, horizontal_bar_size, vertical_bar_label_size, vertical_bar_padding)\n    177 base = (\n    178     alt.Chart(data)\n    179     .transform_filter(legend_selection)\n   (...)\n    232     )\n    233 )\n    234 # Now, we have data in the following format:\n    235 # count, set, is_intersect, degree, intersection_id, set_abbre\n    236 \n    237 # Cardinality by intersecting sets (vertical bar chart)\n    238 vertical_bar = (\n--> 239     base.mark_bar(color=main_color, size=vertical_bar_size)\n    240     .encode(\n    241         x=alt.X(\n    242             \"intersection_id:N\",\n    243             axis=alt.Axis(grid=False, labels=False, ticks=False, domain=True),\n    244             sort=x_sort,\n    245             title=None,\n    246         ),\n    247         y=alt.Y(\n    248             \"max(count):Q\",\n    249             axis=alt.Axis(grid=False, tickCount=3, orient=\"right\"),\n    250             title=\"Intersection Size\",\n    251         ),\n    252         color=brush_color,\n    253         tooltip=tooltip,\n    254     )\n    255     .properties(width=matrix_width, height=vertical_bar_chart_height)\n    256 )\n    258 vertical_bar_text = vertical_bar.mark_text(\n    259     color=main_color, dy=-10, size=vertical_bar_label_size\n    260 ).encode(text=alt.Text(\"count:Q\", format=\".0f\"))\n    262 vertical_bar_chart = (vertical_bar + vertical_bar_text).add_selection(\n    263     color_selection\n    264 )\n\nFile /opt/miniconda3/lib/python3.12/site-packages/altair/vegalite/v5/schema/mixins.py:2786, in MarkMethodMixin.mark_bar(self, align, angle, aria, ariaRole, ariaRoleDescription, aspect, bandSize, baseline, binSpacing, blend, clip, color, continuousBandSize, cornerRadius, cornerRadiusBottomLeft, cornerRadiusBottomRight, cornerRadiusEnd, cornerRadiusTopLeft, cornerRadiusTopRight, cursor, description, dir, discreteBandSize, dx, dy, ellipsis, fill, fillOpacity, filled, font, fontSize, fontStyle, fontWeight, height, href, innerRadius, interpolate, invalid, limit, line, lineBreak, lineHeight, minBandSize, opacity, order, orient, outerRadius, padAngle, point, radius, radius2, radius2Offset, radiusOffset, shape, size, smooth, stroke, strokeCap, strokeDash, strokeDashOffset, strokeJoin, strokeMiterLimit, strokeOffset, strokeOpacity, strokeWidth, style, tension, text, theta, theta2, theta2Offset, thetaOffset, thickness, timeUnitBandPosition, timeUnitBandSize, tooltip, url, width, x, x2, x2Offset, xOffset, y, y2, y2Offset, yOffset, **kwds)\n   2784 copy = self.copy(deep=False)  # type: ignore[attr-defined]\n   2785 if any(val is not Undefined for val in kwds.values()):\n-> 2786     copy.mark = core.MarkDef(type=\"bar\", **kwds)\n   2787 else:\n   2788     copy.mark = \"bar\"\n\nFile /opt/miniconda3/lib/python3.12/site-packages/altair/vegalite/v5/schema/core.py:23720, in MarkDef.__init__(self, type, align, angle, aria, ariaRole, ariaRoleDescription, aspect, bandSize, baseline, binSpacing, blend, clip, color, continuousBandSize, cornerRadius, cornerRadiusBottomLeft, cornerRadiusBottomRight, cornerRadiusEnd, cornerRadiusTopLeft, cornerRadiusTopRight, cursor, description, dir, discreteBandSize, dx, dy, ellipsis, fill, fillOpacity, filled, font, fontSize, fontStyle, fontWeight, height, href, innerRadius, interpolate, invalid, limit, line, lineBreak, lineHeight, minBandSize, opacity, order, orient, outerRadius, padAngle, point, radius, radius2, radius2Offset, radiusOffset, shape, size, smooth, stroke, strokeCap, strokeDash, strokeDashOffset, strokeJoin, strokeMiterLimit, strokeOffset, strokeOpacity, strokeWidth, style, tension, text, theta, theta2, theta2Offset, thetaOffset, thickness, timeUnitBandPosition, timeUnitBandSize, tooltip, url, width, x, x2, x2Offset, xOffset, y, y2, y2Offset, yOffset, **kwds)\n  22901 def __init__(\n  22902     self,\n  22903     type: Union[\n   (...)\n  23718     **kwds,\n  23719 ):\n> 23720     super(MarkDef, self).__init__(\n  23721         type=type,\n  23722         align=align,\n  23723         angle=angle,\n  23724         aria=aria,\n  23725         ariaRole=ariaRole,\n  23726         ariaRoleDescription=ariaRoleDescription,\n  23727         aspect=aspect,\n  23728         bandSize=bandSize,\n  23729         baseline=baseline,\n  23730         binSpacing=binSpacing,\n  23731         blend=blend,\n  23732         clip=clip,\n  23733         color=color,\n  23734         continuousBandSize=continuousBandSize,\n  23735         cornerRadius=cornerRadius,\n  23736         cornerRadiusBottomLeft=cornerRadiusBottomLeft,\n  23737         cornerRadiusBottomRight=cornerRadiusBottomRight,\n  23738         cornerRadiusEnd=cornerRadiusEnd,\n  23739         cornerRadiusTopLeft=cornerRadiusTopLeft,\n  23740         cornerRadiusTopRight=cornerRadiusTopRight,\n  23741         cursor=cursor,\n  23742         description=description,\n  23743         dir=dir,\n  23744         discreteBandSize=discreteBandSize,\n  23745         dx=dx,\n  23746         dy=dy,\n  23747         ellipsis=ellipsis,\n  23748         fill=fill,\n  23749         fillOpacity=fillOpacity,\n  23750         filled=filled,\n  23751         font=font,\n  23752         fontSize=fontSize,\n  23753         fontStyle=fontStyle,\n  23754         fontWeight=fontWeight,\n  23755         height=height,\n  23756         href=href,\n  23757         innerRadius=innerRadius,\n  23758         interpolate=interpolate,\n  23759         invalid=invalid,\n  23760         limit=limit,\n  23761         line=line,\n  23762         lineBreak=lineBreak,\n  23763         lineHeight=lineHeight,\n  23764         minBandSize=minBandSize,\n  23765         opacity=opacity,\n  23766         order=order,\n  23767         orient=orient,\n  23768         outerRadius=outerRadius,\n  23769         padAngle=padAngle,\n  23770         point=point,\n  23771         radius=radius,\n  23772         radius2=radius2,\n  23773         radius2Offset=radius2Offset,\n  23774         radiusOffset=radiusOffset,\n  23775         shape=shape,\n  23776         size=size,\n  23777         smooth=smooth,\n  23778         stroke=stroke,\n  23779         strokeCap=strokeCap,\n  23780         strokeDash=strokeDash,\n  23781         strokeDashOffset=strokeDashOffset,\n  23782         strokeJoin=strokeJoin,\n  23783         strokeMiterLimit=strokeMiterLimit,\n  23784         strokeOffset=strokeOffset,\n  23785         strokeOpacity=strokeOpacity,\n  23786         strokeWidth=strokeWidth,\n  23787         style=style,\n  23788         tension=tension,\n  23789         text=text,\n  23790         theta=theta,\n  23791         theta2=theta2,\n  23792         theta2Offset=theta2Offset,\n  23793         thetaOffset=thetaOffset,\n  23794         thickness=thickness,\n  23795         timeUnitBandPosition=timeUnitBandPosition,\n  23796         timeUnitBandSize=timeUnitBandSize,\n  23797         tooltip=tooltip,\n  23798         url=url,\n  23799         width=width,\n  23800         x=x,\n  23801         x2=x2,\n  23802         x2Offset=x2Offset,\n  23803         xOffset=xOffset,\n  23804         y=y,\n  23805         y2=y2,\n  23806         y2Offset=y2Offset,\n  23807         yOffset=yOffset,\n  23808         **kwds,\n  23809     )\n\nFile /opt/miniconda3/lib/python3.12/site-packages/altair/vegalite/v5/schema/core.py:149, in AnyMark.__init__(self, *args, **kwds)\n    148 def __init__(self, *args, **kwds):\n--> 149     super(AnyMark, self).__init__(*args, **kwds)\n\nFile /opt/miniconda3/lib/python3.12/site-packages/altair/utils/schemapi.py:771, in SchemaBase.__init__(self, *args, **kwds)\n    768 object.__setattr__(self, \"_kwds\", kwds)\n    770 if DEBUG_MODE and self._class_is_valid_at_instantiation:\n--> 771     self.to_dict(validate=True)\n\nFile /opt/miniconda3/lib/python3.12/site-packages/altair/utils/schemapi.py:978, in SchemaBase.to_dict(self, validate, ignore, context)\n    971         self.validate(result)\n    972     except jsonschema.ValidationError as err:\n    973         # We do not raise `from err` as else the resulting\n    974         # traceback is very long as it contains part\n    975         # of the Vega-Lite schema. It would also first\n    976         # show the less helpful ValidationError instead of\n    977         # the more user friendly SchemaValidationError\n--> 978         raise SchemaValidationError(self, err) from None\n    979 return result\n\nSchemaValidationError: '-7.628865979381443' is an invalid value for `size`.\n\n-7.628865979381443 is less than the minimum of 0\n\n\n\n\n\n\nERROR:root:No traceback has been produced, nothing to debug.",
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+      "Altair and Other Charts"
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+    "objectID": "jupyter_notebook.html",
+    "href": "jupyter_notebook.html",
+    "title": "Jupyter Notebooks",
+    "section": "",
+    "text": "backup_folders_of_nbs\n\n backup_folders_of_nbs (src, dest)\n\n\n\n\nbackup_all_notebooks\n\n backup_all_notebooks (folder)\n\n\n\n\nbackup_this_notebook\n\n backup_this_notebook (this_file_path, save_html_to_dir=None,\n                       override_previous_backup=False, changelog=None,\n                       exclude_input=False, force_save_notebook=True)\n\n\n\n\nsave_notebook\n\n save_notebook (file_path)\n\n\nbackup_this_notebook(\"jupyter_notebook.ipynb\")\n\n\n\n\nshow_big_dataframe\n\n show_big_dataframe (df, max_rows=30)\n\n\n\n\ndisplay_dfs_side_by_side\n\n display_dfs_side_by_side (*args, titles=<itertools.cycle object at\n                           0x11b22ea00>, max_rows=50)\n\n\n\n\nh6\n\n h6 (text)\n\n\n\n\nh5\n\n h5 (text)\n\n\n\n\nh4\n\n h4 (text)\n\n\n\n\nh3\n\n h3 (text)\n\n\n\n\nh2\n\n h2 (text)\n\n\n\n\nh1\n\n h1 (text)\n\n\n\n\nstore_scrap\n\n store_scrap (at)\n\n\n\n\nshutdown_current_notebook\n\n shutdown_current_notebook (delay:int=None)",
+    "crumbs": [
+      "Jupyter Notebooks"
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+  },
+  {
+    "objectID": "misc.html",
+    "href": "misc.html",
+    "title": "Miscellaneous",
+    "section": "",
+    "text": "time.sleep(3)",
+    "crumbs": [
+      "Miscellaneous"
+    ]
+  },
+  {
+    "objectID": "misc.html#timer",
+    "href": "misc.html#timer",
+    "title": "Miscellaneous",
+    "section": "Timer",
+    "text": "Timer\nUse timer as a standalone class so you have full control on when to call a lap (most useful in while loops)…\n\nN = 50\nt = Timer(N)\ninfo = None\n\nfor i in range(N):\n    time.sleep(0.1)\n    t(info=info)  # Lap and present the time\n    if i == N//2:\n        print()\n        info = f\"My Info: {i*3.122}\"\n\n26/50 (2.70s - 2.49s remaining - 9.62 iters/s)          \nMy Info: 78.05  50/50 (5.20s - 0.00s remaining - 9.61 iters/s)",
+    "crumbs": [
+      "Miscellaneous"
+    ]
+  },
+  {
+    "objectID": "misc.html#track2",
+    "href": "misc.html#track2",
+    "title": "Miscellaneous",
+    "section": "Track2",
+    "text": "Track2\n… or use track2 to directly track a for loop\n\nN = 50\ninfo = None\n\nfor i in (tracker := track2(range(N), total=N)):\n    time.sleep(0.1)\n    info = f\"My Info: {i*3.122:.2f}\"\n    if i == N // 2:\n        print()\n    if i >= N // 2:\n        tracker.send(info)\n\n25/50 (2.59s - 2.59s remaining - 9.66 iters/s)          \nMy Info: 152.98 50/50 (5.18s - 0.00s remaining - 9.65 iters/s)          \n\n\nWarning! NEVER RUN tracker.send(None) as this will skip variables silently\n\n# https://youtu.be/XcU-a-eksMA\nfrom IPython.display import YouTubeVideo\n\n# Replace 'video_id' with the actual YouTube video ID\nYouTubeVideo('XcU-a-eksMA', width=560, height=315)",
+    "crumbs": [
+      "Miscellaneous"
+    ]
+  },
+  {
+    "objectID": "misc.html#print-execution-time",
+    "href": "misc.html#print-execution-time",
+    "title": "Miscellaneous",
+    "section": "Print execution time",
+    "text": "Print execution time\n@timeit decorates and prints time taken to execute a function as an info log\n\n@timeit\ndef foo(a, b=None):\n    if b is None:\n        return a + 1\n    else:\n        time.sleep(2)\n        return a + b\n\n\nfoo(10)\nfoo(10, b=20)\n\n[08/24/24 12:46:23] INFO     0.00 seconds to execute `foo`                                                                                            3503404687.py:inner:16\n\n\n\n[08/24/24 12:46:25] INFO     2.01 seconds to execute `foo`                                                                                            3503404687.py:inner:16\n\n\n\n30",
+    "crumbs": [
+      "Miscellaneous"
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+    "objectID": "misc.html#print-io",
+    "href": "misc.html#print-io",
+    "title": "Miscellaneous",
+    "section": "Print IO",
+    "text": "Print IO\n@io will decorate to show inputs and outputs (along with time taken to execute) as a debug log\n\n@io\ndef foo(a, b=None):\n    if b is None:\n        return a + 1\n    else:\n        time.sleep(2)\n        return a + b\n\n\nwith debug_mode():\n    foo(10)\n    foo(10, b=20)\n\n[08/24/24 12:46:26] DEBUG                                                                                                                             3503404687.py:inner:41\n                             0.00 seconds to execute `foo`                                                                                                                  \n                             args()                                                                                                                                         \n                               0 - 10 (🏷️ int)                                                                                                                               \n                             kwargs                                                                                                                                         \n                             outputs - 11 (🏷️ int)                                                                                                                           \n                                                                                                                                                                            \n                                                                                                                                                                            \n\n\n\n[08/24/24 12:46:28] DEBUG                                                                                                                             3503404687.py:inner:41\n                             2.01 seconds to execute `foo`                                                                                                                  \n                             args()                                                                                                                                         \n                               0 - 10 (🏷️ int)                                                                                                                               \n                             kwargs                                                                                                                                         \n                               b - 20 (🏷️ int)                                                                                                                               \n                             outputs - 30 (🏷️ int)                                                                                                                           \n                                                                                                                                                                            \n                                                                                                                                                                            \n\n\n\n@io can be forced to print as log/trace if needed",
+    "crumbs": [
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+    "objectID": "misc.html#try-catch-with-a-single-line",
+    "href": "misc.html#try-catch-with-a-single-line",
+    "title": "Miscellaneous",
+    "section": "Try Catch with a single line",
+    "text": "Try Catch with a single line\n\n@tryy\ndef do(a, b, c):\n    return 1 / 0\n\n\nx = do(1, 2, c=10)\nassert x is None  # tryy returns None by default\n\n[08/24/24 12:46:28] ERROR    Error for `do` with                                                                                                      1057038067.py:inner:30\n                             args()                                                                                                                                         \n                               0 - 1 (🏷️ int)                                                                                                                                \n                               1 - 2 (🏷️ int)                                                                                                                                \n                             kwargs                                                                                                                                         \n                               c - 10 (🏷️ int)                                                                                                                               \n                                                                                                                                                                            \n                             ZeroDivisionError: division by zero                                                                                                            \n                             Traceback (most recent call last):                                                                                                             \n                                                                                                                                                                            \n                               File \"/var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_45033/1057038067.py\", line 21, in inner                                     \n                                 return f(*args, **kwargs)                                                                                                                  \n                                        |  |       -> {'c': 10}                                                                                                             \n                                        |  -> (1, 2)                                                                                                                        \n                                        -> <function do at 0x11ecdd800>                                                                                                     \n                                                                                                                                                                            \n                               File \"/var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_45033/884313129.py\", line 3, in do                                          \n                                 return 1 / 0                                                                                                                               \n                                                                                                                                                                            \n                             ZeroDivisionError: division by zero                                                                                                            \n                                                                                                                                                                            \n                             ╭───────────────────────────────────────── Traceback (most recent call last) ──────────────────────────────────────────╮                       \n                             │ in inner:21                                                                                                          │                       \n                             │                                                                                                                      │                       \n                             │   18 │   │                                                                                                           │                       \n                             │   19 │   │   def inner(*args, **kwargs):                                                                             │                       \n                             │   20 │   │   │   try:                                                                                                │                       \n                             │ ❱ 21 │   │   │   │   return f(*args, **kwargs)                                                                       │                       \n                             │   22 │   │   │   except Exception as e:                                                                              │                       \n                             │   23 │   │   │   │   if not print_traceback:                                                                         │                       \n                             │   24 │   │   │   │   │   tb = f\"{type(e).__name__}: {str(e)}\"                                                        │                       \n                             │                                                                                                                      │                       \n                             │ in do:3                                                                                                              │                       \n                             │                                                                                                                      │                       \n                             │   1 @tryy                                                                                                            │                       \n                             │   2 def do(a, b, c):                                                                                                 │                       \n                             │ ❱ 3 │   return 1 / 0                                                                                                 │                       \n                             │   4                                                                                                                  │                       \n                             │   5                                                                                                                  │                       \n                             │   6 x = do(1, 2, c=10)                                                                                               │                       \n                             ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯                       \n                             ZeroDivisionError: division by zero                                                                                                            \n\n\n\nUse your own default on failure\n\n@tryy(output_to_return_on_fail=\"😔\")\ndef do(a, b, c):\n    return 1 / 0\n\n\ndo(1, 2, c=10)\n\n                    ERROR    Error for `do` with                                                                                                      1057038067.py:inner:30\n                             args()                                                                                                                                         \n                               0 - 1 (🏷️ int)                                                                                                                                \n                               1 - 2 (🏷️ int)                                                                                                                                \n                             kwargs                                                                                                                                         \n                               c - 10 (🏷️ int)                                                                                                                               \n                                                                                                                                                                            \n                             ZeroDivisionError: division by zero                                                                                                            \n                             Traceback (most recent call last):                                                                                                             \n                                                                                                                                                                            \n                               File \"/var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_45033/1057038067.py\", line 21, in inner                                     \n                                 return f(*args, **kwargs)                                                                                                                  \n                                        |  |       -> {'c': 10}                                                                                                             \n                                        |  -> (1, 2)                                                                                                                        \n                                        -> <function do at 0x11ecdeac0>                                                                                                     \n                                                                                                                                                                            \n                               File \"/var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_45033/3352784188.py\", line 3, in do                                         \n                                 return 1 / 0                                                                                                                               \n                                                                                                                                                                            \n                             ZeroDivisionError: division by zero                                                                                                            \n                                                                                                                                                                            \n                             ╭───────────────────────────────────────── Traceback (most recent call last) ──────────────────────────────────────────╮                       \n                             │ in inner:21                                                                                                          │                       \n                             │                                                                                                                      │                       \n                             │   18 │   │                                                                                                           │                       \n                             │   19 │   │   def inner(*args, **kwargs):                                                                             │                       \n                             │   20 │   │   │   try:                                                                                                │                       \n                             │ ❱ 21 │   │   │   │   return f(*args, **kwargs)                                                                       │                       \n                             │   22 │   │   │   except Exception as e:                                                                              │                       \n                             │   23 │   │   │   │   if not print_traceback:                                                                         │                       \n                             │   24 │   │   │   │   │   tb = f\"{type(e).__name__}: {str(e)}\"                                                        │                       \n                             │                                                                                                                      │                       \n                             │ in do:3                                                                                                              │                       \n                             │                                                                                                                      │                       \n                             │   1 @tryy(output_to_return_on_fail=\"😔\")                                                                             │                       \n                             │   2 def do(a, b, c):                                                                                                 │                       \n                             │ ❱ 3 │   return 1 / 0                                                                                                 │                       \n                             │   4                                                                                                                  │                       \n                             │   5                                                                                                                  │                       \n                             │   6 do(1, 2, c=10)                                                                                                   │                       \n                             ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯                       \n                             ZeroDivisionError: division by zero                                                                                                            \n\n\n\n'😔'\n\n\nOptionally print the full stacktrace if needed\n\n@tryy(print_traceback=True, output_to_return_on_fail=\"😔\")\ndef do(a, b, c):\n    return 1 / 0\n\n\ndo(1, 2, c=10)\n\n                    ERROR    Error for `do` with                                                                                                      1057038067.py:inner:30\n                             args()                                                                                                                                         \n                               0 - 1 (🏷️ int)                                                                                                                                \n                               1 - 2 (🏷️ int)                                                                                                                                \n                             kwargs                                                                                                                                         \n                               c - 10 (🏷️ int)                                                                                                                               \n                                                                                                                                                                            \n                             Traceback (most recent call last):                                                                                                             \n                               File \"/var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_45033/1057038067.py\", line 21, in inner                                     \n                                 return f(*args, **kwargs)                                                                                                                  \n                                        ^^^^^^^^^^^^^^^^^^                                                                                                                  \n                               File \"/var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_45033/580638143.py\", line 3, in do                                          \n                                 return 1 / 0                                                                                                                               \n                                        ~~^~~                                                                                                                               \n                             ZeroDivisionError: division by zero                                                                                                            \n                                                                                                                                                                            \n                             Traceback (most recent call last):                                                                                                             \n                                                                                                                                                                            \n                               File \"/var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_45033/1057038067.py\", line 21, in inner                                     \n                                 return f(*args, **kwargs)                                                                                                                  \n                                        |  |       -> {'c': 10}                                                                                                             \n                                        |  -> (1, 2)                                                                                                                        \n                                        -> <function do at 0x11ecdeb60>                                                                                                     \n                                                                                                                                                                            \n                               File \"/var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_45033/580638143.py\", line 3, in do                                          \n                                 return 1 / 0                                                                                                                               \n                                                                                                                                                                            \n                             ZeroDivisionError: division by zero                                                                                                            \n                                                                                                                                                                            \n                             ╭───────────────────────────────────────── Traceback (most recent call last) ──────────────────────────────────────────╮                       \n                             │ in inner:21                                                                                                          │                       \n                             │                                                                                                                      │                       \n                             │   18 │   │                                                                                                           │                       \n                             │   19 │   │   def inner(*args, **kwargs):                                                                             │                       \n                             │   20 │   │   │   try:                                                                                                │                       \n                             │ ❱ 21 │   │   │   │   return f(*args, **kwargs)                                                                       │                       \n                             │   22 │   │   │   except Exception as e:                                                                              │                       \n                             │   23 │   │   │   │   if not print_traceback:                                                                         │                       \n                             │   24 │   │   │   │   │   tb = f\"{type(e).__name__}: {str(e)}\"                                                        │                       \n                             │                                                                                                                      │                       \n                             │ in do:3                                                                                                              │                       \n                             │                                                                                                                      │                       \n                             │   1 @tryy(print_traceback=True, output_to_return_on_fail=\"😔\")                                                       │                       \n                             │   2 def do(a, b, c):                                                                                                 │                       \n                             │ ❱ 3 │   return 1 / 0                                                                                                 │                       \n                             │   4                                                                                                                  │                       \n                             │   5                                                                                                                  │                       \n                             │   6 do(1, 2, c=10)                                                                                                   │                       \n                             ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯                       \n                             ZeroDivisionError: division by zero                                                                                                            \n\n\n\n'😔'\n\n\nYou can also silence the errors completely\n\n@tryy(silence_errors=True, output_to_return_on_fail=\"😔\")\ndef do(a, b, c):\n    return 1 / 0\n\n\ndo(1, 2, c=10)\n\n'😔'\n\n\nYou can collect all your errors in a list\n\nimport random\n\nerrors = []\n\n\n@tryy(silence_errors=True, store_errors=errors)\ndef do(a, b, c):\n    if random.randint(0, 100) < 50:\n        return 1 / 0\n    else:\n        raise NotImplementedError(\"🤔\")\n\n\nfor _ in range(4):\n    do(1, random.randint(0, 10), c=random.randint(0, 100))\n\nprint(errors)\n\n[\n```↯ AttrDict ↯\nfunc - do (🏷️ str)\nargs()\n  0 - 1 (🏷️ int)\n  1 - 5 (🏷️ int)\nkwargs\n  c - 21 (🏷️ int)\ntb - NotImplementedError: 🤔 (🏷️ str)\nerr_type - NotImplementedError (🏷️ str)\n\n```\n, \n```↯ AttrDict ↯\nfunc - do (🏷️ str)\nargs()\n  0 - 1 (🏷️ int)\n  1 - 7 (🏷️ int)\nkwargs\n  c - 93 (🏷️ int)\ntb - NotImplementedError: 🤔 (🏷️ str)\nerr_type - NotImplementedError (🏷️ str)\n\n```\n, \n```↯ AttrDict ↯\nfunc - do (🏷️ str)\nargs()\n  0 - 1 (🏷️ int)\n  1 - 4 (🏷️ int)\nkwargs\n  c - 4 (🏷️ int)\ntb - ZeroDivisionError: division by zero (🏷️ str)\nerr_type - ZeroDivisionError (🏷️ str)\n\n```\n, \n```↯ AttrDict ↯\nfunc - do (🏷️ str)\nargs()\n  0 - 1 (🏷️ int)\n  1 - 0 (🏷️ int)\nkwargs\n  c - 36 (🏷️ int)\ntb - NotImplementedError: 🤔 (🏷️ str)\nerr_type - NotImplementedError (🏷️ str)\n\n```\n]\n\n\nThere’s onlly one usecase where you would want to send in a list by yourself - when you want to append your errors to an existing list. The sensible default is to always store the errors, especially because this is a debugging tool.\nJust access all the errors in a dataframe like so\n\nimport random\n\nrandom.seed(10)\n\n\n@tryy(silence_errors=True)\ndef do(a, b, c):\n    if c < 50:\n        return 1 / 0\n    else:\n        raise NotImplementedError(\"🤔\")\n\n\nfor _ in range(4):\n    do(1, random.randint(0, 10), c=random.randint(0, 100))\n\ndo.error_summary()\n\n\n\n\n\n\n\n\nfunc\nargs\nkwargs\ntb\nerr_type\n\n\n\n\n0\ndo\n[1, 9]\n{'c': 4}\nZeroDivisionError: division by zero\nZeroDivisionError\n\n\n1\ndo\n[1, 6]\n{'c': 61}\nNotImplementedError: 🤔\nNotImplementedError\n\n\n2\ndo\n[1, 9]\n{'c': 1}\nZeroDivisionError: division by zero\nZeroDivisionError\n\n\n3\ndo\n[1, 3]\n{'c': 59}\nNotImplementedError: 🤔\nNotImplementedError\n\n\n\n\n\n\n\nand the actual list of errors like so\n\ndo.error_store\n\n[\n ```↯ AttrDict ↯\n func - do (🏷️ str)\n args()\n   0 - 1 (🏷️ int)\n   1 - 9 (🏷️ int)\n kwargs\n   c - 4 (🏷️ int)\n tb - ZeroDivisionError: division by zero (🏷️ str)\n err_type - ZeroDivisionError (🏷️ str)\n \n ```,\n \n ```↯ AttrDict ↯\n func - do (🏷️ str)\n args()\n   0 - 1 (🏷️ int)\n   1 - 6 (🏷️ int)\n kwargs\n   c - 61 (🏷️ int)\n tb - NotImplementedError: 🤔 (🏷️ str)\n err_type - NotImplementedError (🏷️ str)\n \n ```,\n \n ```↯ AttrDict ↯\n func - do (🏷️ str)\n args()\n   0 - 1 (🏷️ int)\n   1 - 9 (🏷️ int)\n kwargs\n   c - 1 (🏷️ int)\n tb - ZeroDivisionError: division by zero (🏷️ str)\n err_type - ZeroDivisionError (🏷️ str)\n \n ```,\n \n ```↯ AttrDict ↯\n func - do (🏷️ str)\n args()\n   0 - 1 (🏷️ int)\n   1 - 3 (🏷️ int)\n kwargs\n   c - 59 (🏷️ int)\n tb - NotImplementedError: 🤔 (🏷️ str)\n err_type - NotImplementedError (🏷️ str)\n \n ```]\n\n\nFinally, you want to run the function (without try) to reproduce the error and actually start debugging. Just use the .F attribute to access the original function that you created\n\nix = 2\ndata = do.error_store[ix]\ndo.F(*data.args, **data.kwargs)\n\n\n---------------------------------------------------------------------------\nZeroDivisionError                         Traceback (most recent call last)\nCell In[19], line 3\n      1 ix = 2\n      2 data = do.error_store[ix]\n----> 3 do.F(*data.args, **data.kwargs)\n\nCell In[17], line 9, in do(a, b, c)\n      6 @tryy(silence_errors=True)\n      7 def do(a, b, c):\n      8     if c < 50:\n----> 9         return 1 / 0\n     10     else:\n     11         raise NotImplementedError(\"🤔\")\n\nZeroDivisionError: division by zero\n\n\n\n\ndef deco(decorator):\n    @wraps(decorator)\n    def wrapper(*args, **kwargs):\n        def real_decorator(func):\n            @wraps(func)\n            def inner_wrapper(*fargs, **fkwargs):\n                return decorator(func, *fargs, **fkwargs)\n\n            return inner_wrapper\n\n        if len(args) == 1 and callable(args[0]) and not kwargs:\n            # Case when B is used without arguments\n            return real_decorator(args[0])\n        else:\n            # Case when B is used with arguments\n            def custom_decorator(func):\n                return decorator(func, **kwargs)\n\n            return custom_decorator\n\n    return wrapper\n\n\n@deco\ndef B(func, *args, deco_param=\"default\", **kwargs):\n    print(\"B\", deco_param, print(\"args\", *args, \"kwargs\", **kwargs))\n    return func(*args, **kwargs)\n\n\n@B\ndef C(a, b, c):\n    print(\"C\")\n    return a + (b * c)\n\n\nC(1, 2, 3)\n\n\n@B(deco_param=\"new_param\")\ndef C(a, b, c):\n    return a + (b * c)\n\n\n# Testing\nprint(C(1, 2, 3))  # Outputs 'new_param' then the result 7",
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+    "text": "Adapters\nTo convert data from/to various formats\n\n\n\nfile_2_bytes\n\n file_2_bytes (fpath)\n\n\n\n\nbytes_2_file\n\n bytes_2_file (input:bytes, fpath:Union[str,pathlib.Path],\n               silent:bool=False)\n\nSave bytes input at given fpath\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\ninput\nbytes\n\nbytes\n\n\nfpath\nUnion\n\nPlace where you want to save the file\n\n\nsilent\nbool\nFalse\n\n\n\nReturns\nNone\n\n\n\n\n\n\n\n\nb64_2_file\n\n b64_2_file (input:str, fpath:Union[str,pathlib.Path])\n\nSave a file encoded as a base64 input at given fpath\n\n\n\n\nType\nDetails\n\n\n\n\ninput\nstr\nbase64 encoded string\n\n\nfpath\nUnion\nPlace where you want to save the file\n\n\nReturns\nNone\n\n\n\n\n\n\n\nb64_2_np\n\n b64_2_np (input:str)\n\n*Converts a base64 encoded image to a NumPy array.\nArgs: input (str): The base64 encoded image.\nReturns: np.ndarray: The NumPy array representation of the image in RGB format.*\n\n\n\nnp_2_b64\n\n np_2_b64 (image:numpy.ndarray)\n\nConvert a numpy image to base64 string\n\n\n\ncvat_2_csvs\n\n cvat_2_csvs (xmlfile, csvs_folder)\n\n*Convert CVAT XML annotations to CSV files.\nArgs: xmlfile (str): Path to the CVAT XML file. csvs_folder (str): Path to the folder where the CSV files will be saved.\nReturns: None*\n\n\n\ncsvs_2_cvat\n\n csvs_2_cvat (images_folder, csvs_folder, xml_output_file, items=None,\n              parquet=False, relative_df=True, default_label='Background',\n              extension='jpg')\n\n*Convert CSV annotations to CVAT XML format.\nArgs: images_folder (str): Path to the folder containing the images. csvs_folder (str): Path to the folder containing the CSV annotations. xml_output_file (str): Path to the output XML file. items (list, optional): List of items to process. If None, all items will be processed. Defaults to None. parquet (bool, optional): Whether the annotations are stored in Parquet format. Defaults to False. relative_df (bool, optional): Whether the bounding box coordinates in the CSV are relative to the image size. Defaults to True. default_label (str, optional): Default label for the bounding boxes. Defaults to “Background”. extension (str, optional): Image file extension. Defaults to “jpg”.\nReturns: None*\n\n\n\nyolo_2_df\n\n yolo_2_df (yolo, h, w, id2class, class_column)\n\n\n\n\ndf_2_yolo\n\n df_2_yolo (df, h, w, class2id, class_column)",
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+    "title": "Capsule (Tutorial)",
+    "section": "",
+    "text": "Let’s load the iris dataset first\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\n\ndata = load_iris()\nX, y = data.data, data.target\nX_trn, X_val, y_trn, y_val = train_test_split(X, y, random_state=42)\n\n… and create the data loaders\n\nfrom torch_snippets.markup2 import AD\nfrom torch.utils.data import TensorDataset\n\ntrn_ds = TensorDataset(*[torch.Tensor(i) for i in [X_trn, y_trn]])\ntrn_dl = DataLoader(trn_ds, batch_size=32)\n\nval_ds = TensorDataset(*[torch.Tensor(i) for i in [X_val, y_val]])\nval_dl = DataLoader(val_ds, batch_size=32)\n\nAD(next(iter(val_dl)))\n\n\n```↯ AttrDict ↯\nnext(iter(val_dl))[]\n  0 - 🔦tensor[32, 4] n=128 x∈[0.100, 7.900] μ=3.488 σ=2.034 - ID:#56f55949\n  1 - 🔦tensor[32] x∈[0., 2.000] μ=0.969 σ=0.861 - ID:#b365fa0a\n\n```\n\n\nNext we’ll import Capsule and a few decorators that will tell the model to change it’s mode to train/test during the fit function\nfrom torch_snippets.trainer.capsule import Capsule, train, validate, predict\nCreate the neural network and define it’s forward function as usual pytorch business. Only difference now is that you’ll also add self.loss_fn and self.optimizer attributes in the init\nclass IrisModel(Capsule):\n    def __init__(self):\n        super().__init__()\n        self.model = nn.Sequential(\n            nn.BatchNorm1d(4),\n            nn.Linear(4, 16),\n            nn.Dropout(0.2),\n            nn.BatchNorm1d(16),\n            nn.ReLU(inplace=True),\n            nn.Linear(16, 8),\n            nn.Dropout(0.2),\n            nn.BatchNorm1d(8),\n            nn.ReLU(inplace=True),\n            nn.Linear(8, 3),\n        )\n        self.loss_fn = nn.CrossEntropyLoss()\n        self.optimizer = optim.Adam(self.parameters())\n\n    def forward(self, x):\n        return self.model(x)\nTo fully describe the model’s behaviour we still need to define three functions\n1. train_batch\n2. validate_batch and,\n3. predict which is optional\nlike so\n\n    @train\n    def train_batch(self, batch):\n        x, y = batch\n        _y = self.forward(x)\n        loss = self.loss_fn(_y, y.long())\n        return {\"loss\": loss}\n\n    @validate\n    def validate_batch(self, batch=None, dl=None):\n        if dl is not None:\n            output = []\n            for batch in dl:\n                output.extend(self.predict(batch=batch)[\"val_acc\"])\n            return np.mean(output)\n        x, y = batch\n        _y = self.forward(x)\n        loss = self.loss_fn(_y, y.long())\n        acc = (y == _y.max(-1)[1]).float().mean()\n        return {\"val_loss\": loss, \"val_acc\": acc}\n\n    @predict\n    def predict(self, batch=None, dl=None):\n        if dl is not None:\n            output = []\n            for batch in dl:\n                output.extend(self.predict(batch=batch))\n            return output\n        x, y = batch\n        _y = self.forward(x)\n        o = _y.max(-1)[1].cpu().detach().numpy().tolist()\n        return o\n\nEnsure you return dictionaries of losses, accuracy metrics in train_batch and validate_batch functions. You can return as many metrics during training and validation, they will be auto logged.\n\nAlso make sure at least one of the keys in train_batch is the key loss, as this is used to compute gradients.*\n\nWe could now create the model…\n\nmodel = IrisModel()\nmodel.device = \"cpu\"\n\n… and run model.fit with an optional number of logs to print to the console\n\nmodel.fit(trn_dl, val_dl, num_epochs=100, print_total=2, device=\"cpu\")\n\nEPOCH: 1.000  val_acc: 0.281  loss: 1.118  val_loss: 1.159  (0.05s - 5.02s remaining)\nEPOCH: 50.000  val_acc: 1.000  loss: 0.490  val_loss: 0.406  (0.18s - 0.18s remaining)\nEPOCH: 100.000  val_acc: 1.000  loss: 0.238  val_loss: 0.162  (0.31s - 0.00s remaining)\n\n\n\n\n\n\n\n\n\nmodel.evaluate accepts a validation data loader that will repeatedly call validate_batch and return aggregated metrics\n\nmodel.evaluate(val_dl, device=\"cpu\")\n\nEPOCH: 1.000  val_acc: 1.000  val_loss: 0.164  (0.00s - 0.00s remaining)\n\n\n{'epoch_val_acc': np.float64(1.0),\n 'epoch_val_loss': np.float64(0.16414348781108856)}",
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+      "Capsule (Tutorial)"
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+    "objectID": "inspector.html",
+    "href": "inspector.html",
+    "title": "Inspect",
+    "section": "",
+    "text": "import torch, numpy as np\nfrom torch_snippets import inspect\n\ninspect(torch.randint(0, 100, size=(4, 3, 5)), np.random.randint(-10, 10, (9, 19, 1)))\n\n══════════════════════════════════════════════════════════════════\n\n\n\nTensor  Shape: torch.Size([4, 3, 5])    Min: 2.000      Max: 97.000     Mean: 46.317    dtype: torch.int64 @ cpu\n\n\n\n══════════════════════════════════════════════════════════════════\n\n\n\nndarray Shape: (9, 19, 1)       Min: -10.000    Max: 9.000      Mean: -0.345    dtype: int64\n\n\n\n══════════════════════════════════════════════════════════════════\n\n\n\n\nx = {\n    \"a\": [0, 1, 2, 3],\n    \"b\": torch.rand(10, 10),\n    \"c\": {\n        \"d\": np.arange(10),\n        \"e\": [\n            \"np.arange\",\n            {},\n            tuple(\n                [\n                    1,\n                    2,\n                ]\n            ),\n            set([1, 2, 3]),\n            [],\n            [11, 10],\n        ],\n    },\n}\ninspect(x)\n\n══════════════════════════════════════════════════════════════════\n\n\n\ndict of 3 items\n\n\n\n        A:\n        list of 4 items\n\n\n\n                int: 0\n\n\n\n                int: 1\n\n\n\n                int: 2\n\n\n\n                int: 3\n\n\n\n        B:\n        Tensor  Shape: torch.Size([10, 10])     Min: 0.000      Max: 0.989      Mean: 0.463     dtype: \ntorch.float32 @ cpu\n\n\n\n        C:\n        dict of 2 items\n\n\n\n                D:\n                ndarray Shape: (10,)    Min: 0.000      Max: 9.000      Mean: 4.500     dtype: int64\n\n\n\n                E:\n                list of 6 items\n\n\n\n                        str `np.arange`\n\n\n\n                        dict of 0 items\n\n\n\n                        tuple of 2 items\n\n\n\n                                int: 1\n\n\n\n                                int: 2\n\n\n\n                        set Length: 3\n\n\n\n                        list of 0 items\n\n\n\n                        and ... ... 1 more item(s)\n\n\n\n══════════════════════════════════════════════════════════════════",
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+      "Inspect"
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+    "href": "index.html",
+    "title": "Utilities for simple needs",
+    "section": "",
+    "text": "# Time it\nfrom torch_snippets import *\n\nCPU times: user 263 ms, sys: 53.9 ms, total: 317 ms\nWall time: 407 ms\nBelow we are trying to extract the __all__ list from all Python files of the torch_snippets directory.\nThrough the code, you can already see some of the elements of torch-snippets in action.\nimport ast\n\nos.environ[\n    \"AD_MAX_ITEMS\"\n] = (  # os is already imported by torch_snippets, along with many other useful libraries\n    \"1000\"  # Set the maximum number of items to display in the AD object\n)\n\n\n@tryy  # This is a decorator that catches exceptions\ndef extract_all_list(file_path):\n    file = readfile(file_path, silent=True)  # Read the file\n    tree = ast.parse(file, filename=file_path)\n\n    for node in tree.body:\n        if isinstance(node, ast.Assign):\n            for target in node.targets:\n                if isinstance(target, ast.Name) and target.id == \"__all__\":\n                    if isinstance(node.value, ast.List):\n                        all_list = [\n                            elt.value\n                            for elt in node.value.elts\n                            if isinstance(elt, ast.Constant)\n                        ]\n                        return all_list\n    return None\n\n\ndef print_all_lists_in_directory(directory):\n    dir = P(directory)  # Create a pathlib.Path object\n    for f in dir.ls():  # Iterate over all files in the directory\n        if f.extn == \"py\" and f.stem not in [\n            \"__init__\",\n            \"_nbdev\",\n        ]:  # If it's a Python file and not __init__.py\n            all_list = extract_all_list(f)\n            if all_list is not None and len(all_list) > 0:\n                h2(f.stem)  # Print the name of the file as a heading in jupyter\n                print(\n                    AD({\"items\": all_list})\n                )  # AD is an intelligent dictionary that can display itself nicely\nprint(P().resolve())\n\n/Users/yeshwanth/Code/Personal/torch_snippets/nbs\n# Specify the directory containing the Python files\ndirectory_path = \"../torch_snippets\"\nprint_all_lists_in_directory(directory_path)\n\nmisc\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - Timer (🏷️ str)\n  1 - track2 (🏷️ str)\n  2 - summarize_input (🏷️ str)\n  3 - timeit (🏷️ str)\n  4 - io (🏷️ str)\n  5 - tryy (🏷️ str)\n\n```\n\n\n\nload_defaults\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - ifnone (🏷️ str)\n  1 - exists (🏷️ str)\n  2 - loadifexists (🏷️ str)\n\n```\n\n\n\ntext_utils\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - textify (🏷️ str)\n  1 - find_lines (🏷️ str)\n  2 - find_blocks (🏷️ str)\n  3 - find_substring (🏷️ str)\n  4 - get_line_data_from_word_data (🏷️ str)\n  5 - edit_distance_path (🏷️ str)\n  6 - group_blocks (🏷️ str)\n\n```\n\n\n\npaths\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - valid_methods (🏷️ str)\n  1 - P (🏷️ str)\n  2 - ls (🏷️ str)\n  3 - print_folder_summary (🏷️ str)\n  4 - dill (🏷️ str)\n  5 - input_to_str (🏷️ str)\n  6 - output_to_path (🏷️ str)\n  7 - process_f (🏷️ str)\n  8 - get_fs (🏷️ str)\n  9 - P0 (🏷️ str)\n  10 - stem (🏷️ str)\n  11 - stems (🏷️ str)\n  12 - extn (🏷️ str)\n  13 - remove_file (🏷️ str)\n  14 - isdir (🏷️ str)\n  15 - makedir (🏷️ str)\n  16 - fname (🏷️ str)\n  17 - fname2 (🏷️ str)\n  18 - parent (🏷️ str)\n  19 - Glob (🏷️ str)\n  20 - find (🏷️ str)\n  21 - zip_files (🏷️ str)\n  22 - unzip_file (🏷️ str)\n  23 - list_zip (🏷️ str)\n  24 - md5 (🏷️ str)\n  25 - remove_duplicates (🏷️ str)\n  26 - common_items (🏷️ str)\n  27 - folder_summary (🏷️ str)\n  28 - readlines (🏷️ str)\n  29 - readfile (🏷️ str)\n  30 - writelines (🏷️ str)\n  31 - tree (🏷️ str)\n  32 - folder_structure_to_dict (🏷️ str)\n  33 - folder_structure_to_json (🏷️ str)\n  34 - rename_batch (🏷️ str)\n  35 - dumpdill (🏷️ str)\n  36 - loaddill (🏷️ str)\n\n```\n\n\n\ncharts\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - alt (🏷️ str)\n  1 - Chart (🏷️ str)\n  2 - CM (🏷️ str)\n  3 - radar (🏷️ str)\n  4 - confusion_matrix (🏷️ str)\n  5 - spider (🏷️ str)\n  6 - upsetaltair_top_level_configuration (🏷️ str)\n  7 - UpSetAltair (🏷️ str)\n\n```\n\n\n\npdf_loader\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - PDF (🏷️ str)\n\n```\n\n\n\ninteractive_show\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - COLORS (🏷️ str)\n  1 - to_networkx (🏷️ str)\n  2 - plot_image (🏷️ str)\n  3 - plot_graph (🏷️ str)\n  4 - tonp (🏷️ str)\n  5 - tolist (🏷️ str)\n  6 - convert_to_nx (🏷️ str)\n  7 - viz2 (🏷️ str)\n  8 - df2graph_nodes (🏷️ str)\n  9 - ishow (🏷️ str)\n\n```\n\n\n\nregistry\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - Config (🏷️ str)\n  1 - AttrDict (🏷️ str)\n  2 - registry (🏷️ str)\n  3 - tryeval (🏷️ str)\n  4 - parse_base (🏷️ str)\n  5 - parse (🏷️ str)\n  6 - parse_and_resolve (🏷️ str)\n  7 - parse_string (🏷️ str)\n\n```\n\n\n\nmarkup2\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - AD (🏷️ str)\n  1 - Config (🏷️ str)\n  2 - isnamedtupleinstance (🏷️ str)\n  3 - unpack (🏷️ str)\n  4 - AttrDict (🏷️ str)\n  5 - pretty_json (🏷️ str)\n  6 - read_json (🏷️ str)\n  7 - write_json (🏷️ str)\n  8 - write_jsonl (🏷️ str)\n  9 - read_jsonl (🏷️ str)\n  10 - read_yaml (🏷️ str)\n  11 - write_yaml (🏷️ str)\n  12 - read_xml (🏷️ str)\n  13 - write_xml (🏷️ str)\n\n```\n\n\n\ninspector\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - inspect (🏷️ str)\n\n```\n\n\n\ntorch_loader\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - torch (🏷️ str)\n  1 - th (🏷️ str)\n  2 - torchvision (🏷️ str)\n  3 - T (🏷️ str)\n  4 - transforms (🏷️ str)\n  5 - nn (🏷️ str)\n  6 - np (🏷️ str)\n  7 - F (🏷️ str)\n  8 - Dataset (🏷️ str)\n  9 - DataLoader (🏷️ str)\n  10 - optim (🏷️ str)\n  11 - Report (🏷️ str)\n  12 - Reshape (🏷️ str)\n  13 - Permute (🏷️ str)\n  14 - device (🏷️ str)\n  15 - save_torch_model_weights_from (🏷️ str)\n  16 - load_torch_model_weights_to (🏷️ str)\n  17 - detach (🏷️ str)\n  18 - cat_with_padding (🏷️ str)\n\n```\n\n\n\nlogger\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - console (🏷️ str)\n  1 - reset_logger_width (🏷️ str)\n  2 - logger (🏷️ str)\n  3 - Trace (🏷️ str)\n  4 - Debug (🏷️ str)\n  5 - Info (🏷️ str)\n  6 - Warn (🏷️ str)\n  7 - Excep (🏷️ str)\n  8 - warn_mode (🏷️ str)\n  9 - info_mode (🏷️ str)\n  10 - debug_mode (🏷️ str)\n  11 - trace_mode (🏷️ str)\n  12 - excep_mode (🏷️ str)\n  13 - in_warn_mode (🏷️ str)\n  14 - in_info_mode (🏷️ str)\n  15 - in_debug_mode (🏷️ str)\n  16 - in_trace_mode (🏷️ str)\n  17 - in_excep_mode (🏷️ str)\n  18 - frames (🏷️ str)\n  19 - get_console (🏷️ str)\n  20 - reset_logger (🏷️ str)\n  21 - get_logger_level (🏷️ str)\n  22 - logger_mode (🏷️ str)\n  23 - in_logger_mode (🏷️ str)\n  24 - notify_waiting (🏷️ str)\n\n```\n\n\n\nmarkup\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - AttrDict (🏷️ str)\n  1 - Config (🏷️ str)\n  2 - isnamedtupleinstance (🏷️ str)\n  3 - unpack (🏷️ str)\n  4 - hash_tensor (🏷️ str)\n  5 - hash_pandas_dataframe (🏷️ str)\n  6 - AttrDictDeprecated (🏷️ str)\n  7 - decompose (🏷️ str)\n  8 - pretty_json (🏷️ str)\n  9 - read_json (🏷️ str)\n  10 - write_json (🏷️ str)\n  11 - write_jsonl (🏷️ str)\n  12 - read_jsonl (🏷️ str)\n  13 - read_yaml (🏷️ str)\n  14 - write_yaml (🏷️ str)\n  15 - read_xml (🏷️ str)\n  16 - write_xml (🏷️ str)\n\n```\n\n\n\nsklegos\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - ColumnSelector (🏷️ str)\n  1 - GroupedPredictor (🏷️ str)\n  2 - EstimatorTransformer (🏷️ str)\n  3 - train_test_split (🏷️ str)\n  4 - MakeFrame (🏷️ str)\n  5 - ImputeMissingValues (🏷️ str)\n  6 - LambdaTransformer (🏷️ str)\n  7 - Cat2Num (🏷️ str)\n  8 - SplitDateColumn (🏷️ str)\n\n```\n\n\n\nipython\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - save_notebook (🏷️ str)\n  1 - backup_this_notebook (🏷️ str)\n  2 - backup_all_notebooks (🏷️ str)\n  3 - backup_folders_of_nbs (🏷️ str)\n  4 - display_dfs_side_by_side (🏷️ str)\n  5 - show_big_dataframe (🏷️ str)\n  6 - h1 (🏷️ str)\n  7 - h2 (🏷️ str)\n  8 - h3 (🏷️ str)\n  9 - h4 (🏷️ str)\n  10 - h5 (🏷️ str)\n  11 - h6 (🏷️ str)\n  12 - store_scrap (🏷️ str)\n  13 - shutdown_current_notebook (🏷️ str)\n\n```\n\n\n\n../torch_snippets/loader.py:532: SyntaxWarning: invalid escape sequence '\\$'\n  puttext(ax, text.replace(\"$\", \"\\$\"), tuple(bbs[ix][:2]), size=text_sz)\n\n\nloader\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - B (🏷️ str)\n  1 - Blank (🏷️ str)\n  2 - batchify (🏷️ str)\n  3 - C (🏷️ str)\n  4 - choose (🏷️ str)\n  5 - common (🏷️ str)\n  6 - crop_from_bb (🏷️ str)\n  7 - diff (🏷️ str)\n  8 - E (🏷️ str)\n  9 - flatten (🏷️ str)\n  10 - Image (🏷️ str)\n  11 - jitter (🏷️ str)\n  12 - L (🏷️ str)\n  13 - lzip (🏷️ str)\n  14 - line (🏷️ str)\n  15 - lines (🏷️ str)\n  16 - to_absolute (🏷️ str)\n  17 - to_relative (🏷️ str)\n  18 - enlarge_bbs (🏷️ str)\n  19 - shrink_bbs (🏷️ str)\n  20 - logger (🏷️ str)\n  21 - np (🏷️ str)\n  22 - now (🏷️ str)\n  23 - nunique (🏷️ str)\n  24 - os (🏷️ str)\n  25 - pad (🏷️ str)\n  26 - pd (🏷️ str)\n  27 - pdfilter (🏷️ str)\n  28 - pdb (🏷️ str)\n  29 - PIL (🏷️ str)\n  30 - print (🏷️ str)\n  31 - puttext (🏷️ str)\n  32 - randint (🏷️ str)\n  33 - rand (🏷️ str)\n  34 - re (🏷️ str)\n  35 - read (🏷️ str)\n  36 - readPIL (🏷️ str)\n  37 - rect (🏷️ str)\n  38 - resize (🏷️ str)\n  39 - rotate (🏷️ str)\n  40 - see (🏷️ str)\n  41 - show (🏷️ str)\n  42 - store_attr (🏷️ str)\n  43 - subplots (🏷️ str)\n  44 - sys (🏷️ str)\n  45 - toss (🏷️ str)\n  46 - track (🏷️ str)\n  47 - tqdm (🏷️ str)\n  48 - Tqdm (🏷️ str)\n  49 - trange (🏷️ str)\n  50 - unique (🏷️ str)\n  51 - uint (🏷️ str)\n  52 - write (🏷️ str)\n  53 - BB (🏷️ str)\n  54 - bbfy (🏷️ str)\n  55 - xywh2xyXY (🏷️ str)\n  56 - df2bbs (🏷️ str)\n  57 - bbs2df (🏷️ str)\n  58 - Info (🏷️ str)\n  59 - Warn (🏷️ str)\n  60 - Debug (🏷️ str)\n  61 - Excep (🏷️ str)\n  62 - reset_logger (🏷️ str)\n  63 - get_logger_level (🏷️ str)\n  64 - in_debug_mode (🏷️ str)\n  65 - debug_mode (🏷️ str)\n  66 - typedispatch (🏷️ str)\n  67 - defaultdict (🏷️ str)\n  68 - Counter (🏷️ str)\n  69 - dcopy (🏷️ str)\n  70 - patch_to (🏷️ str)\n  71 - split (🏷️ str)\n  72 - train_test_split (🏷️ str)\n  73 - init_plt (🏷️ str)\n  74 - init_cv2 (🏷️ str)\n\n```\n\n\n\nimgaug_loader\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - do (🏷️ str)\n  1 - bw (🏷️ str)\n  2 - rotate (🏷️ str)\n  3 - pad (🏷️ str)\n  4 - get_size (🏷️ str)\n  5 - rescale (🏷️ str)\n  6 - crop (🏷️ str)\n  7 - imgaugbbs2bbs (🏷️ str)\n  8 - bbs2imgaugbbs (🏷️ str)\n\n```\n\n\n\ndates\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - make_uniform_date_format (🏷️ str)\n  1 - ALL_DATE_FORMATS (🏷️ str)\n  2 - are_dates_equal (🏷️ str)\n  3 - today (🏷️ str)\n\n```\n\n\n\nbokeh_loader\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - parse_sz (🏷️ str)\n  1 - get_bplot (🏷️ str)\n\n```\n\n\n\nbb_utils\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - randint (🏷️ str)\n  1 - BB (🏷️ str)\n  2 - df2bbs (🏷️ str)\n  3 - bbs2df (🏷️ str)\n  4 - bbfy (🏷️ str)\n  5 - jitter (🏷️ str)\n  6 - compute_eps (🏷️ str)\n  7 - enlarge_bbs (🏷️ str)\n  8 - shrink_bbs (🏷️ str)\n  9 - iou (🏷️ str)\n  10 - compute_distance_matrix (🏷️ str)\n  11 - compute_distances (🏷️ str)\n  12 - split_bb_to_xyXY (🏷️ str)\n  13 - combine_xyXY_to_bb (🏷️ str)\n  14 - is_absolute (🏷️ str)\n  15 - is_relative (🏷️ str)\n  16 - to_relative (🏷️ str)\n  17 - to_absolute (🏷️ str)\n  18 - merge_by_bb (🏷️ str)\n  19 - isin (🏷️ str)\n\n```\n\n\n\nadapters\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - np_2_b64 (🏷️ str)\n  1 - b64_2_np (🏷️ str)\n  2 - b64_2_file (🏷️ str)\n  3 - bytes_2_file (🏷️ str)\n  4 - file_2_bytes (🏷️ str)\n  5 - csvs_2_cvat (🏷️ str)\n  6 - cvat_2_csvs (🏷️ str)\n  7 - df_2_yolo (🏷️ str)\n  8 - yolo_2_df (🏷️ str)\n\n```\n\n\n\ndecorators\n\n\n\n```↯ AttrDict ↯\nitems[]\n  0 - format (🏷️ str)\n  1 - warn_on_fail (🏷️ str)\n  2 - timeit (🏷️ str)\n  3 - io (🏷️ str)\n  4 - check_kwargs_not_none (🏷️ str)\n\n```",
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\ No newline at end of file
diff --git a/_proc/docs/show.html b/_proc/docs/show.html
new file mode 100644
index 0000000..2dcbf63
--- /dev/null
+++ b/_proc/docs/show.html
@@ -0,0 +1,1028 @@
+
+
+
+
+
+
+
+
+
+show – torch_snippets
+
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+  
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+  

+    
+  
+

+
+

+
+  
+

+
+    
+
+

+
+
+
+
+

+
Show

+
+
Show is intended to show numpy-arrays/PIL-images

+

+
from torch_snippets import *
+
+im = np.random.rand(100, 100)
+show(im)

+

+

+

+

+

+

+

+

+

+
show(im, sz=4)

+

+

+

+

+

+

+

+

+
Show will even accept pytorch Tensors and show them as images, even if they are on GPU and have channels first

+
It can accept bounding boxes as tuples of (x,y,X,Y) which can be integers (i.e., absolute coordinates) or fractions (between \([0,1]\)). There’s provision to give bb_colors and texts as well

+

+
show(im, bbs=[(0, 0, 0.5, 0.35), (0, 0.2, 0.35, 0.95)])
+
+show(im, bbs=[(0, 0, 0.5, 0.35), (0, 0.2, 0.35, 0.95)], bb_colors=["r", "g"])
+
+show(
+    im,
+    bbs=[(0, 0, 0.5, 0.35), (0, 0.2, 0.35, 0.95)],
+    bb_colors=["b", "g"],
+    texts=["bb1", "bb2"],
+    sz=10,
+    text_sz=15,
+)

+

+
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

+

+

+

+

+

+

+

+

+

+
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

+

+

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+

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+

+
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

+

+

+

+

+

+

+

+

+

+
It can also read a file path directly and display the image

+

+
show("../docs/images/company_logo_big.png", sz=3)

+

+

+

+

+

+

+

+

+
if the input is not an image or string, show will simply display the given input as intended by jupyter notebook

+

+
df = pd.DataFrame(np.random.rand(100, 2))
+show(df)

+

+

+
+
+
+
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+
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01
00.2424780.929517
10.9718900.506750
20.1398260.753227
30.8647990.524166
40.5636100.135709
.........
950.3798740.639771
960.9937310.811343
970.6216830.763054
980.8445090.915156
990.3142720.392418

+
+
100 rows × 2 columns

+

+

+

+

+
chart = Chart(df).mark_circle().encode(x="0:Q", y="1:Q")
+show(chart.interactive())

+

+
+

+
+

+

+

+
Subplots

+
display multiple images Subplots is a wapper around plt.subplots that accepts a list of images, number of columns as nc and additional kwargs

+

+
from torch_snippets import subplots
+
+ims = [np.random.rand(100, 100) for _ in range(16)]
+
+subplots(ims, nc=4, sz=5)

+

+

+

+

+

+

+

+

+

+
subplots(
+    ims,
+    nc=2,
+    sz=(5, 15),
+    suptitle="RANDOM IMAGES",
+    titles=[f"random_{i}" for i in range(16)],
+)

+

+

+

+

+

+

+

+

+
+
+

+

+
+
 
+
+
 
+
+
+
+
+
\ No newline at end of file
diff --git a/_proc/docs/show_files/figure-html/cell-2-output-1.png b/_proc/docs/show_files/figure-html/cell-2-output-1.png
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diff --git a/_proc/docs/show_files/figure-html/cell-9-output-1.png b/_proc/docs/show_files/figure-html/cell-9-output-1.png
new file mode 100644
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diff --git a/_proc/docs/site_libs/bootstrap/bootstrap-icons.css b/_proc/docs/site_libs/bootstrap/bootstrap-icons.css
new file mode 100644
index 0000000..285e444
--- /dev/null
+++ b/_proc/docs/site_libs/bootstrap/bootstrap-icons.css
@@ -0,0 +1,2078 @@
+/*!
+ * Bootstrap Icons v1.11.1 (https://icons.getbootstrap.com/)
+ * Copyright 2019-2023 The Bootstrap Authors
+ * Licensed under MIT (https://github.com/twbs/icons/blob/main/LICENSE)
+ */
+
+@font-face {
+  font-display: block;
+  font-family: "bootstrap-icons";
+  src: 
+url("./bootstrap-icons.woff?2820a3852bdb9a5832199cc61cec4e65") format("woff");
+}
+
+.bi::before,
+[class^="bi-"]::before,
+[class*=" bi-"]::before {
+  display: inline-block;
+  font-family: bootstrap-icons !important;
+  font-style: normal;
+  font-weight: normal !important;
+  font-variant: normal;
+  text-transform: none;
+  line-height: 1;
+  vertical-align: -.125em;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+
+.bi-123::before { content: "\f67f"; }
+.bi-alarm-fill::before { content: "\f101"; }
+.bi-alarm::before { content: "\f102"; }
+.bi-align-bottom::before { content: "\f103"; }
+.bi-align-center::before { content: "\f104"; }
+.bi-align-end::before { content: "\f105"; }
+.bi-align-middle::before { content: "\f106"; }
+.bi-align-start::before { content: "\f107"; }
+.bi-align-top::before { content: "\f108"; }
+.bi-alt::before { content: "\f109"; }
+.bi-app-indicator::before { content: "\f10a"; }
+.bi-app::before { content: "\f10b"; }
+.bi-archive-fill::before { content: "\f10c"; }
+.bi-archive::before { content: "\f10d"; }
+.bi-arrow-90deg-down::before { content: "\f10e"; }
+.bi-arrow-90deg-left::before { content: "\f10f"; }
+.bi-arrow-90deg-right::before { content: "\f110"; }
+.bi-arrow-90deg-up::before { content: "\f111"; }
+.bi-arrow-bar-down::before { content: "\f112"; }
+.bi-arrow-bar-left::before { content: "\f113"; }
+.bi-arrow-bar-right::before { content: "\f114"; }
+.bi-arrow-bar-up::before { content: "\f115"; }
+.bi-arrow-clockwise::before { content: "\f116"; }
+.bi-arrow-counterclockwise::before { content: "\f117"; }
+.bi-arrow-down-circle-fill::before { content: "\f118"; }
+.bi-arrow-down-circle::before { content: "\f119"; }
+.bi-arrow-down-left-circle-fill::before { content: "\f11a"; }
+.bi-arrow-down-left-circle::before { content: "\f11b"; }
+.bi-arrow-down-left-square-fill::before { content: "\f11c"; }
+.bi-arrow-down-left-square::before { content: "\f11d"; }
+.bi-arrow-down-left::before { content: "\f11e"; }
+.bi-arrow-down-right-circle-fill::before { content: "\f11f"; }
+.bi-arrow-down-right-circle::before { content: "\f120"; }
+.bi-arrow-down-right-square-fill::before { content: "\f121"; }
+.bi-arrow-down-right-square::before { content: "\f122"; }
+.bi-arrow-down-right::before { content: "\f123"; }
+.bi-arrow-down-short::before { content: "\f124"; }
+.bi-arrow-down-square-fill::before { content: "\f125"; }
+.bi-arrow-down-square::before { content: "\f126"; }
+.bi-arrow-down-up::before { content: "\f127"; }
+.bi-arrow-down::before { content: "\f128"; }
+.bi-arrow-left-circle-fill::before { content: "\f129"; }
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diff --git a/_proc/docs/site_libs/bootstrap/bootstrap-icons.woff b/_proc/docs/site_libs/bootstrap/bootstrap-icons.woff
new file mode 100644
index 0000000..dbeeb05
Binary files /dev/null and b/_proc/docs/site_libs/bootstrap/bootstrap-icons.woff differ
diff --git a/_proc/docs/site_libs/bootstrap/bootstrap.min.css b/_proc/docs/site_libs/bootstrap/bootstrap.min.css
new file mode 100644
index 0000000..476a93a
--- /dev/null
+++ b/_proc/docs/site_libs/bootstrap/bootstrap.min.css
@@ -0,0 +1,12 @@
+/*!
+ * Bootstrap  v5.3.1 (https://getbootstrap.com/)
+ * Copyright 2011-2023 The Bootstrap Authors
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+ */@import"https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@300;400;700&display=swap";:root,[data-bs-theme=light]{--bs-blue: #2780e3;--bs-indigo: #6610f2;--bs-purple: #613d7c;--bs-pink: #e83e8c;--bs-red: #ff0039;--bs-orange: #f0ad4e;--bs-yellow: #ff7518;--bs-green: #3fb618;--bs-teal: #20c997;--bs-cyan: #9954bb;--bs-black: #000;--bs-white: #fff;--bs-gray: #6c757d;--bs-gray-dark: #343a40;--bs-gray-100: #f8f9fa;--bs-gray-200: #e9ecef;--bs-gray-300: #dee2e6;--bs-gray-400: #ced4da;--bs-gray-500: #adb5bd;--bs-gray-600: #6c757d;--bs-gray-700: #495057;--bs-gray-800: #343a40;--bs-gray-900: #212529;--bs-default: #343a40;--bs-primary: #2780e3;--bs-secondary: #343a40;--bs-success: #3fb618;--bs-info: #9954bb;--bs-warning: #ff7518;--bs-danger: #ff0039;--bs-light: #f8f9fa;--bs-dark: #343a40;--bs-default-rgb: 52, 58, 64;--bs-primary-rgb: 39, 128, 227;--bs-secondary-rgb: 52, 58, 64;--bs-success-rgb: 63, 182, 24;--bs-info-rgb: 153, 84, 187;--bs-warning-rgb: 255, 117, 24;--bs-danger-rgb: 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0.25;--bs-focus-ring-color: rgba(39, 128, 227, 0.25);--bs-form-valid-color: #3fb618;--bs-form-valid-border-color: #3fb618;--bs-form-invalid-color: #ff0039;--bs-form-invalid-border-color: #ff0039}[data-bs-theme=dark]{color-scheme:dark;--bs-body-color: #dee2e6;--bs-body-color-rgb: 222, 226, 230;--bs-body-bg: #212529;--bs-body-bg-rgb: 33, 37, 41;--bs-emphasis-color: #fff;--bs-emphasis-color-rgb: 255, 255, 255;--bs-secondary-color: rgba(222, 226, 230, 0.75);--bs-secondary-color-rgb: 222, 226, 230;--bs-secondary-bg: #343a40;--bs-secondary-bg-rgb: 52, 58, 64;--bs-tertiary-color: rgba(222, 226, 230, 0.5);--bs-tertiary-color-rgb: 222, 226, 230;--bs-tertiary-bg: #2b3035;--bs-tertiary-bg-rgb: 43, 48, 53;--bs-primary-text-emphasis: #7db3ee;--bs-secondary-text-emphasis: #85898c;--bs-success-text-emphasis: #8cd374;--bs-info-text-emphasis: #c298d6;--bs-warning-text-emphasis: #ffac74;--bs-danger-text-emphasis: #ff6688;--bs-light-text-emphasis: #f8f9fa;--bs-dark-text-emphasis: 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.nav-link{width:100%}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.navbar{--bs-navbar-padding-x: 0;--bs-navbar-padding-y: 0.5rem;--bs-navbar-color: #fdfeff;--bs-navbar-hover-color: rgba(253, 253, 255, 0.8);--bs-navbar-disabled-color: rgba(253, 254, 255, 0.75);--bs-navbar-active-color: #fdfdff;--bs-navbar-brand-padding-y: 0.3125rem;--bs-navbar-brand-margin-end: 1rem;--bs-navbar-brand-font-size: 1.25rem;--bs-navbar-brand-color: #fdfeff;--bs-navbar-brand-hover-color: #fdfdff;--bs-navbar-nav-link-padding-x: 0.5rem;--bs-navbar-toggler-padding-y: 0.25;--bs-navbar-toggler-padding-x: 0;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23fdfeff' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(253, 254, 255, 0);--bs-navbar-toggler-border-radius: 0.25rem;--bs-navbar-toggler-focus-width: 0.25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) var(--bs-navbar-padding-x)}.navbar>.container,.navbar>.container-fluid,.navbar>.container-sm,.navbar>.container-md,.navbar>.container-lg,.navbar>.container-xl,.navbar>.container-xxl{display:flex;display:-webkit-flex;flex-wrap:inherit;-webkit-flex-wrap:inherit;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between}.navbar-brand{padding-top:var(--bs-navbar-brand-padding-y);padding-bottom:var(--bs-navbar-brand-padding-y);margin-right:var(--bs-navbar-brand-margin-end);font-size:var(--bs-navbar-brand-font-size);color:var(--bs-navbar-brand-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;white-space:nowrap}.navbar-brand:hover,.navbar-brand:focus{color:var(--bs-navbar-brand-hover-color)}.navbar-nav{--bs-nav-link-padding-x: 0;--bs-nav-link-padding-y: 0.5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-navbar-color);--bs-nav-link-hover-color: var(--bs-navbar-hover-color);--bs-nav-link-disabled-color: var(--bs-navbar-disabled-color);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;list-style:none}.navbar-nav .nav-link.active,.navbar-nav .nav-link.show{color:var(--bs-navbar-active-color)}.navbar-nav .dropdown-menu{position:static}.navbar-text{padding-top:.5rem;padding-bottom:.5rem;color:var(--bs-navbar-color)}.navbar-text a,.navbar-text a:hover,.navbar-text a:focus{color:var(--bs-navbar-active-color)}.navbar-collapse{flex-basis:100%;-webkit-flex-basis:100%;flex-grow:1;-webkit-flex-grow:1;align-items:center;-webkit-align-items:center}.navbar-toggler{padding:var(--bs-navbar-toggler-padding-y) var(--bs-navbar-toggler-padding-x);font-size:var(--bs-navbar-toggler-font-size);line-height:1;color:var(--bs-navbar-color);background-color:rgba(0,0,0,0);border:var(--bs-border-width) solid var(--bs-navbar-toggler-border-color);transition:var(--bs-navbar-toggler-transition)}@media(prefers-reduced-motion: reduce){.navbar-toggler{transition:none}}.navbar-toggler:hover{text-decoration:none}.navbar-toggler:focus{text-decoration:none;outline:0;box-shadow:0 0 0 var(--bs-navbar-toggler-focus-width)}.navbar-toggler-icon{display:inline-block;width:1.5em;height:1.5em;vertical-align:middle;background-image:var(--bs-navbar-toggler-icon-bg);background-repeat:no-repeat;background-position:center;background-size:100%}.navbar-nav-scroll{max-height:var(--bs-scroll-height, 75vh);overflow-y:auto}@media(min-width: 576px){.navbar-expand-sm{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-sm .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-sm .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-sm .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-sm .navbar-nav-scroll{overflow:visible}.navbar-expand-sm .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-sm .navbar-toggler{display:none}.navbar-expand-sm .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-sm .offcanvas .offcanvas-header{display:none}.navbar-expand-sm .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 768px){.navbar-expand-md{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-md .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1200px){.navbar-expand-xl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xl .navbar-nav-scroll{overflow:visible}.navbar-expand-xl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xl .navbar-toggler{display:none}.navbar-expand-xl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #fdfeff;--bs-navbar-hover-color: rgba(253, 253, 255, 0.8);--bs-navbar-disabled-color: rgba(253, 254, 255, 0.75);--bs-navbar-active-color: #fdfdff;--bs-navbar-brand-color: #fdfeff;--bs-navbar-brand-hover-color: #fdfdff;--bs-navbar-toggler-border-color: rgba(253, 254, 255, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23fdfeff' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23fdfeff' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #fff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #343a40;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23343a40'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2310335b'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #93c0f1;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #10335b;--bs-accordion-active-bg: #d4e6f9}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 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#b94ba8;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #b94ba8;color:#fff}.bg-gradient-cyan-red{--bslib-color-fg: #fff;--bslib-color-bg: #c23287;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #c23287;color:#fff}.bg-gradient-cyan-orange{--bslib-color-fg: #fff;--bslib-color-bg: #bc788f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #bc788f;color:#fff}.bg-gradient-cyan-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #c2617a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #c2617a;color:#fff}.bg-gradient-cyan-green{--bslib-color-fg: #fff;--bslib-color-bg: #757b7a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #757b7a;color:#fff}.bg-gradient-cyan-teal{--bslib-color-fg: #fff;--bslib-color-bg: #6983ad;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #6983ad;color:#fff}:root{--bslib-value-box-shadow: none;--bslib-value-box-border-width-auto-yes: var(--bslib-value-box-border-width-baseline);--bslib-value-box-border-width-auto-no: 0;--bslib-value-box-border-width-baseline: 1px}.bslib-value-box{border-width:var(--bslib-value-box-border-width-auto-no, var(--bslib-value-box-border-width-baseline));container-name:bslib-value-box;container-type:inline-size}.bslib-value-box.card{box-shadow:var(--bslib-value-box-shadow)}.bslib-value-box.border-auto{border-width:var(--bslib-value-box-border-width-auto-yes, var(--bslib-value-box-border-width-baseline))}.bslib-value-box.default{--bslib-value-box-bg-default: var(--bs-card-bg, #fff);--bslib-value-box-border-color-default: var(--bs-card-border-color, rgba(0, 0, 0, 0.175));color:var(--bslib-value-box-color);background-color:var(--bslib-value-box-bg, var(--bslib-value-box-bg-default));border-color:var(--bslib-value-box-border-color, var(--bslib-value-box-border-color-default))}.bslib-value-box .value-box-grid{display:grid;grid-template-areas:"left right";align-items:center;overflow:hidden}.bslib-value-box .value-box-showcase{height:100%;max-height:var(---bslib-value-box-showcase-max-h, 100%)}.bslib-value-box .value-box-showcase,.bslib-value-box .value-box-showcase>.html-fill-item{width:100%}.bslib-value-box[data-full-screen=true] .value-box-showcase{max-height:var(---bslib-value-box-showcase-max-h-fs, 100%)}@media screen and (min-width: 575.98px){@container bslib-value-box (max-width: 300px){.bslib-value-box:not(.showcase-bottom) .value-box-grid{grid-template-columns:1fr !important;grid-template-rows:auto auto;grid-template-areas:"top" "bottom"}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-showcase{grid-area:top !important}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-area{grid-area:bottom !important;justify-content:end}}}.bslib-value-box .value-box-area{justify-content:center;padding:1.5rem 1rem;font-size:.9rem;font-weight:500}.bslib-value-box .value-box-area *{margin-bottom:0;margin-top:0}.bslib-value-box .value-box-title{font-size:1rem;margin-top:0;margin-bottom:.5rem;font-weight:400;line-height:1.2}.bslib-value-box .value-box-title:empty::after{content:" "}.bslib-value-box .value-box-value{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:400;line-height:1.2}@media(min-width: 1200px){.bslib-value-box .value-box-value{font-size:1.65rem}}.bslib-value-box .value-box-value:empty::after{content:" "}.bslib-value-box .value-box-showcase{align-items:center;justify-content:center;margin-top:auto;margin-bottom:auto;padding:1rem}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{opacity:.85;min-width:50px;max-width:125%}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{font-size:4rem}.bslib-value-box.showcase-top-right .value-box-grid{grid-template-columns:1fr var(---bslib-value-box-showcase-w, 50%)}.bslib-value-box.showcase-top-right .value-box-grid .value-box-showcase{grid-area:right;margin-left:auto;align-self:start;align-items:end;padding-left:0;padding-bottom:0}.bslib-value-box.showcase-top-right .value-box-grid .value-box-area{grid-area:left;align-self:end}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid{grid-template-columns:auto var(---bslib-value-box-showcase-w-fs, 1fr)}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid>div{align-self:center}.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-showcase{margin-top:0}@container bslib-value-box (max-width: 300px){.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-grid .value-box-showcase{padding-left:1rem}}.bslib-value-box.showcase-left-center .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w, 30%) auto}.bslib-value-box.showcase-left-center[data-full-screen=true] .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid .value-box-area{grid-area:top}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid{grid-template-rows:1fr var(---bslib-value-box-showcase-h-fs, 2fr)}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid .value-box-showcase{padding:1rem}[data-bs-theme=dark] .bslib-value-box{--bslib-value-box-shadow: 0 0.5rem 1rem rgb(0 0 0 / 50%)}@media(min-width: 576px){.nav:not(.nav-hidden){display:flex !important;display:-webkit-flex !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column){float:none !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.bslib-nav-spacer{margin-left:auto !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.form-inline{margin-top:auto;margin-bottom:auto}.nav:not(.nav-hidden).nav-stacked{flex-direction:column;-webkit-flex-direction:column;height:100%}.nav:not(.nav-hidden).nav-stacked>.bslib-nav-spacer{margin-top:auto !important}}.bslib-card{overflow:auto}.bslib-card .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card .card-header .form-group{margin-bottom:0}.bslib-card .card-header .selectize-control{margin-bottom:0}.bslib-card .card-header .selectize-control .item{margin-right:1.15rem}.bslib-card .card-footer{margin-top:auto}.bslib-card .bslib-navs-card-title{display:flex;flex-wrap:wrap;justify-content:space-between;align-items:center}.bslib-card .bslib-navs-card-title .nav{margin-left:auto}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border=true]){border:none}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border-radius=true]){border-top-left-radius:0;border-top-right-radius:0}[data-full-screen=true]{position:fixed;inset:3.5rem 1rem 1rem;height:auto !important;max-height:none !important;width:auto !important;z-index:1070}.bslib-full-screen-enter{display:none;position:absolute;bottom:var(--bslib-full-screen-enter-bottom, 0.2rem);right:var(--bslib-full-screen-enter-right, 0);top:var(--bslib-full-screen-enter-top);left:var(--bslib-full-screen-enter-left);color:var(--bslib-color-fg, var(--bs-card-color));background-color:var(--bslib-color-bg, var(--bs-card-bg, var(--bs-body-bg)));border:var(--bs-card-border-width) solid var(--bslib-color-fg, var(--bs-card-border-color));box-shadow:0 2px 4px rgba(0,0,0,.15);margin:.2rem .4rem;padding:.55rem !important;font-size:.8rem;cursor:pointer;opacity:.7;z-index:1070}.bslib-full-screen-enter:hover{opacity:1}.card[data-full-screen=false]:hover>*>.bslib-full-screen-enter{display:block}.bslib-has-full-screen .card:hover>*>.bslib-full-screen-enter{display:none}@media(max-width: 575.98px){.bslib-full-screen-enter{display:none !important}}.bslib-full-screen-exit{position:relative;top:1.35rem;font-size:.9rem;cursor:pointer;text-decoration:none;display:flex;float:right;margin-right:2.15rem;align-items:center;color:rgba(var(--bs-body-bg-rgb), 0.8)}.bslib-full-screen-exit:hover{color:rgba(var(--bs-body-bg-rgb), 1)}.bslib-full-screen-exit svg{margin-left:.5rem;font-size:1.5rem}#bslib-full-screen-overlay{position:fixed;inset:0;background-color:rgba(var(--bs-body-color-rgb), 0.6);backdrop-filter:blur(2px);-webkit-backdrop-filter:blur(2px);z-index:1069;animation:bslib-full-screen-overlay-enter 400ms cubic-bezier(0.6, 0.02, 0.65, 1) forwards}@keyframes bslib-full-screen-overlay-enter{0%{opacity:0}100%{opacity:1}}.bslib-grid{display:grid !important;gap:var(--bslib-spacer, 1rem);height:var(--bslib-grid-height)}.bslib-grid.grid{grid-template-columns:repeat(var(--bs-columns, 12), minmax(0, 1fr));grid-template-rows:unset;grid-auto-rows:var(--bslib-grid--row-heights);--bslib-grid--row-heights--xs: unset;--bslib-grid--row-heights--sm: unset;--bslib-grid--row-heights--md: unset;--bslib-grid--row-heights--lg: unset;--bslib-grid--row-heights--xl: unset;--bslib-grid--row-heights--xxl: unset}.bslib-grid.grid.bslib-grid--row-heights--xs{--bslib-grid--row-heights: var(--bslib-grid--row-heights--xs)}@media(min-width: 576px){.bslib-grid.grid.bslib-grid--row-heights--sm{--bslib-grid--row-heights: var(--bslib-grid--row-heights--sm)}}@media(min-width: 768px){.bslib-grid.grid.bslib-grid--row-heights--md{--bslib-grid--row-heights: var(--bslib-grid--row-heights--md)}}@media(min-width: 992px){.bslib-grid.grid.bslib-grid--row-heights--lg{--bslib-grid--row-heights: var(--bslib-grid--row-heights--lg)}}@media(min-width: 1200px){.bslib-grid.grid.bslib-grid--row-heights--xl{--bslib-grid--row-heights: var(--bslib-grid--row-heights--xl)}}@media(min-width: 1400px){.bslib-grid.grid.bslib-grid--row-heights--xxl{--bslib-grid--row-heights: var(--bslib-grid--row-heights--xxl)}}.bslib-grid>*>.shiny-input-container{width:100%}.bslib-grid-item{grid-column:auto/span 1}@media(max-width: 767.98px){.bslib-grid-item{grid-column:1/-1}}@media(max-width: 575.98px){.bslib-grid{grid-template-columns:1fr !important;height:var(--bslib-grid-height-mobile)}.bslib-grid.grid{height:unset !important;grid-auto-rows:var(--bslib-grid--row-heights--xs, auto)}}.accordion .accordion-header{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:400;line-height:1.2;color:var(--bs-heading-color);margin-bottom:0}@media(min-width: 1200px){.accordion .accordion-header{font-size:1.65rem}}.accordion .accordion-icon:not(:empty){margin-right:.75rem;display:flex}.accordion .accordion-button:not(.collapsed){box-shadow:none}.accordion 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diff --git a/_proc/docs/site_libs/bootstrap/bootstrap.min.js b/_proc/docs/site_libs/bootstrap/bootstrap.min.js
new file mode 100644
index 0000000..e8f21f7
--- /dev/null
+++ b/_proc/docs/site_libs/bootstrap/bootstrap.min.js
@@ -0,0 +1,7 @@
+/*!
+  * Bootstrap v5.3.1 (https://getbootstrap.com/)
+  * Copyright 2011-2023 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
+  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+  */
+!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?module.exports=e():"function"==typeof define&&define.amd?define(e):(t="undefined"!=typeof globalThis?globalThis:t||self).bootstrap=e()}(this,(function(){"use strict";const t=new Map,e={set(e,i,n){t.has(e)||t.set(e,new Map);const s=t.get(e);s.has(i)||0===s.size?s.set(i,n):console.error(`Bootstrap doesn't allow more than one instance per element. 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m?m(Object.assign({},e.rects,{placement:e.placement})):m,O="number"==typeof C?{mainAxis:C,altAxis:C}:Object.assign({mainAxis:0,altAxis:0},C),x=e.modifiersData.offset?e.modifiersData.offset[e.placement]:null,k={x:0,y:0};if(A){if(o){var L,S="y"===y?zt:Vt,D="y"===y?Rt:qt,$="y"===y?"height":"width",I=A[y],N=I+g[S],P=I-g[D],M=f?-T[$]/2:0,j=b===Xt?E[$]:T[$],F=b===Xt?-T[$]:-E[$],H=e.elements.arrow,W=f&&H?Ce(H):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},z=B[S],R=B[D],q=Ne(0,E[$],W[$]),V=v?E[$]/2-M-q-z-O.mainAxis:j-q-z-O.mainAxis,K=v?-E[$]/2+M+q+R+O.mainAxis:F+q+R+O.mainAxis,Q=e.elements.arrow&&$e(e.elements.arrow),X=Q?"y"===y?Q.clientTop||0:Q.clientLeft||0:0,Y=null!=(L=null==x?void 0:x[y])?L:0,U=I+K-Y,G=Ne(f?ye(N,I+V-Y-X):N,I,f?ve(P,U):P);A[y]=G,k[y]=G-I}if(a){var J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const 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W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
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NAME(){return"toast"}show(){N.trigger(this._element,Zs).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(eo),d(this._element),this._element.classList.add(io,no),this._queueCallback((()=>{this._element.classList.remove(no),N.trigger(this._element,to),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(N.trigger(this._element,Gs).defaultPrevented||(this._element.classList.add(no),this._queueCallback((()=>{this._element.classList.add(eo),this._element.classList.remove(no,io),N.trigger(this._element,Js)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(io),super.dispose()}isShown(){return this._element.classList.contains(io)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){N.on(this._element,Qs,(t=>this._onInteraction(t,!0))),N.on(this._element,Xs,(t=>this._onInteraction(t,!1))),N.on(this._element,Ys,(t=>this._onInteraction(t,!0))),N.on(this._element,Us,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=ro.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return R(ro),m(ro),{Alert:Q,Button:Y,Carousel:xt,Collapse:Bt,Dropdown:qi,Modal:On,Offcanvas:qn,Popover:us,ScrollSpy:Es,Tab:Vs,Toast:ro,Tooltip:cs}}));
+//# sourceMappingURL=bootstrap.bundle.min.js.map
\ No newline at end of file
diff --git a/_proc/docs/site_libs/clipboard/clipboard.min.js b/_proc/docs/site_libs/clipboard/clipboard.min.js
new file mode 100644
index 0000000..1103f81
--- /dev/null
+++ b/_proc/docs/site_libs/clipboard/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.11
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
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+// @license-end
\ No newline at end of file
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new file mode 100644
index 0000000..e3726d7
--- /dev/null
+++ b/_proc/docs/site_libs/quarto-html/popper.min.js
@@ -0,0 +1,6 @@
+/**
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+ */
+
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+
diff --git a/_proc/docs/site_libs/quarto-html/quarto-syntax-highlighting.css b/_proc/docs/site_libs/quarto-html/quarto-syntax-highlighting.css
new file mode 100644
index 0000000..b30ce57
--- /dev/null
+++ b/_proc/docs/site_libs/quarto-html/quarto-syntax-highlighting.css
@@ -0,0 +1,205 @@
+/* quarto syntax highlight colors */
+:root {
+  --quarto-hl-ot-color: #003B4F;
+  --quarto-hl-at-color: #657422;
+  --quarto-hl-ss-color: #20794D;
+  --quarto-hl-an-color: #5E5E5E;
+  --quarto-hl-fu-color: #4758AB;
+  --quarto-hl-st-color: #20794D;
+  --quarto-hl-cf-color: #003B4F;
+  --quarto-hl-op-color: #5E5E5E;
+  --quarto-hl-er-color: #AD0000;
+  --quarto-hl-bn-color: #AD0000;
+  --quarto-hl-al-color: #AD0000;
+  --quarto-hl-va-color: #111111;
+  --quarto-hl-bu-color: inherit;
+  --quarto-hl-ex-color: inherit;
+  --quarto-hl-pp-color: #AD0000;
+  --quarto-hl-in-color: #5E5E5E;
+  --quarto-hl-vs-color: #20794D;
+  --quarto-hl-wa-color: #5E5E5E;
+  --quarto-hl-do-color: #5E5E5E;
+  --quarto-hl-im-color: #00769E;
+  --quarto-hl-ch-color: #20794D;
+  --quarto-hl-dt-color: #AD0000;
+  --quarto-hl-fl-color: #AD0000;
+  --quarto-hl-co-color: #5E5E5E;
+  --quarto-hl-cv-color: #5E5E5E;
+  --quarto-hl-cn-color: #8f5902;
+  --quarto-hl-sc-color: #5E5E5E;
+  --quarto-hl-dv-color: #AD0000;
+  --quarto-hl-kw-color: #003B4F;
+}
+
+/* other quarto variables */
+:root {
+  --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
+}
+
+pre > code.sourceCode > span {
+  color: #003B4F;
+}
+
+code span {
+  color: #003B4F;
+}
+
+code.sourceCode > span {
+  color: #003B4F;
+}
+
+div.sourceCode,
+div.sourceCode pre.sourceCode {
+  color: #003B4F;
+}
+
+code span.ot {
+  color: #003B4F;
+  font-style: inherit;
+}
+
+code span.at {
+  color: #657422;
+  font-style: inherit;
+}
+
+code span.ss {
+  color: #20794D;
+  font-style: inherit;
+}
+
+code span.an {
+  color: #5E5E5E;
+  font-style: inherit;
+}
+
+code span.fu {
+  color: #4758AB;
+  font-style: inherit;
+}
+
+code span.st {
+  color: #20794D;
+  font-style: inherit;
+}
+
+code span.cf {
+  color: #003B4F;
+  font-weight: bold;
+  font-style: inherit;
+}
+
+code span.op {
+  color: #5E5E5E;
+  font-style: inherit;
+}
+
+code span.er {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.bn {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.al {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.va {
+  color: #111111;
+  font-style: inherit;
+}
+
+code span.bu {
+  font-style: inherit;
+}
+
+code span.ex {
+  font-style: inherit;
+}
+
+code span.pp {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.in {
+  color: #5E5E5E;
+  font-style: inherit;
+}
+
+code span.vs {
+  color: #20794D;
+  font-style: inherit;
+}
+
+code span.wa {
+  color: #5E5E5E;
+  font-style: italic;
+}
+
+code span.do {
+  color: #5E5E5E;
+  font-style: italic;
+}
+
+code span.im {
+  color: #00769E;
+  font-style: inherit;
+}
+
+code span.ch {
+  color: #20794D;
+  font-style: inherit;
+}
+
+code span.dt {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.fl {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.co {
+  color: #5E5E5E;
+  font-style: inherit;
+}
+
+code span.cv {
+  color: #5E5E5E;
+  font-style: italic;
+}
+
+code span.cn {
+  color: #8f5902;
+  font-style: inherit;
+}
+
+code span.sc {
+  color: #5E5E5E;
+  font-style: inherit;
+}
+
+code span.dv {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.kw {
+  color: #003B4F;
+  font-weight: bold;
+  font-style: inherit;
+}
+
+.prevent-inlining {
+  content: " {
+  // Find any conflicting margin elements and add margins to the
+  // top to prevent overlap
+  const marginChildren = window.document.querySelectorAll(
+    ".column-margin.column-container > *, .margin-caption, .aside"
+  );
+
+  let lastBottom = 0;
+  for (const marginChild of marginChildren) {
+    if (marginChild.offsetParent !== null) {
+      // clear the top margin so we recompute it
+      marginChild.style.marginTop = null;
+      const top = marginChild.getBoundingClientRect().top + window.scrollY;
+      if (top < lastBottom) {
+        const marginChildStyle = window.getComputedStyle(marginChild);
+        const marginBottom = parseFloat(marginChildStyle["marginBottom"]);
+        const margin = lastBottom - top + marginBottom;
+        marginChild.style.marginTop = `${margin}px`;
+      }
+      const styles = window.getComputedStyle(marginChild);
+      const marginTop = parseFloat(styles["marginTop"]);
+      lastBottom = top + marginChild.getBoundingClientRect().height + marginTop;
+    }
+  }
+};
+
+window.document.addEventListener("DOMContentLoaded", function (_event) {
+  // Recompute the position of margin elements anytime the body size changes
+  if (window.ResizeObserver) {
+    const resizeObserver = new window.ResizeObserver(
+      throttle(() => {
+        layoutMarginEls();
+        if (
+          window.document.body.getBoundingClientRect().width < 990 &&
+          isReaderMode()
+        ) {
+          quartoToggleReader();
+        }
+      }, 50)
+    );
+    resizeObserver.observe(window.document.body);
+  }
+
+  const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]');
+  const sidebarEl = window.document.getElementById("quarto-sidebar");
+  const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left");
+  const marginSidebarEl = window.document.getElementById(
+    "quarto-margin-sidebar"
+  );
+  // function to determine whether the element has a previous sibling that is active
+  const prevSiblingIsActiveLink = (el) => {
+    const sibling = el.previousElementSibling;
+    if (sibling && sibling.tagName === "A") {
+      return sibling.classList.contains("active");
+    } else {
+      return false;
+    }
+  };
+
+  // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior)
+  function fireSlideEnter(e) {
+    const event = window.document.createEvent("Event");
+    event.initEvent("slideenter", true, true);
+    window.document.dispatchEvent(event);
+  }
+  const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]');
+  tabs.forEach((tab) => {
+    tab.addEventListener("shown.bs.tab", fireSlideEnter);
+  });
+
+  // fire slideEnter for tabby tab activations (for htmlwidget resize behavior)
+  document.addEventListener("tabby", fireSlideEnter, false);
+
+  // Track scrolling and mark TOC links as active
+  // get table of contents and sidebar (bail if we don't have at least one)
+  const tocLinks = tocEl
+    ? [...tocEl.querySelectorAll("a[data-scroll-target]")]
+    : [];
+  const makeActive = (link) => tocLinks[link].classList.add("active");
+  const removeActive = (link) => tocLinks[link].classList.remove("active");
+  const removeAllActive = () =>
+    [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link));
+
+  // activate the anchor for a section associated with this TOC entry
+  tocLinks.forEach((link) => {
+    link.addEventListener("click", () => {
+      if (link.href.indexOf("#") !== -1) {
+        const anchor = link.href.split("#")[1];
+        const heading = window.document.querySelector(
+          `[data-anchor-id="${anchor}"]`
+        );
+        if (heading) {
+          // Add the class
+          heading.classList.add("reveal-anchorjs-link");
+
+          // function to show the anchor
+          const handleMouseout = () => {
+            heading.classList.remove("reveal-anchorjs-link");
+            heading.removeEventListener("mouseout", handleMouseout);
+          };
+
+          // add a function to clear the anchor when the user mouses out of it
+          heading.addEventListener("mouseout", handleMouseout);
+        }
+      }
+    });
+  });
+
+  const sections = tocLinks.map((link) => {
+    const target = link.getAttribute("data-scroll-target");
+    if (target.startsWith("#")) {
+      return window.document.getElementById(decodeURI(`${target.slice(1)}`));
+    } else {
+      return window.document.querySelector(decodeURI(`${target}`));
+    }
+  });
+
+  const sectionMargin = 200;
+  let currentActive = 0;
+  // track whether we've initialized state the first time
+  let init = false;
+
+  const updateActiveLink = () => {
+    // The index from bottom to top (e.g. reversed list)
+    let sectionIndex = -1;
+    if (
+      window.innerHeight + window.pageYOffset >=
+      window.document.body.offsetHeight
+    ) {
+      // This is the no-scroll case where last section should be the active one
+      sectionIndex = 0;
+    } else {
+      // This finds the last section visible on screen that should be made active
+      sectionIndex = [...sections].reverse().findIndex((section) => {
+        if (section) {
+          return window.pageYOffset >= section.offsetTop - sectionMargin;
+        } else {
+          return false;
+        }
+      });
+    }
+    if (sectionIndex > -1) {
+      const current = sections.length - sectionIndex - 1;
+      if (current !== currentActive) {
+        removeAllActive();
+        currentActive = current;
+        makeActive(current);
+        if (init) {
+          window.dispatchEvent(sectionChanged);
+        }
+        init = true;
+      }
+    }
+  };
+
+  const inHiddenRegion = (top, bottom, hiddenRegions) => {
+    for (const region of hiddenRegions) {
+      if (top <= region.bottom && bottom >= region.top) {
+        return true;
+      }
+    }
+    return false;
+  };
+
+  const categorySelector = "header.quarto-title-block .quarto-category";
+  const activateCategories = (href) => {
+    // Find any categories
+    // Surround them with a link pointing back to:
+    // #category=Authoring
+    try {
+      const categoryEls = window.document.querySelectorAll(categorySelector);
+      for (const categoryEl of categoryEls) {
+        const categoryText = categoryEl.textContent;
+        if (categoryText) {
+          const link = `${href}#category=${encodeURIComponent(categoryText)}`;
+          const linkEl = window.document.createElement("a");
+          linkEl.setAttribute("href", link);
+          for (const child of categoryEl.childNodes) {
+            linkEl.append(child);
+          }
+          categoryEl.appendChild(linkEl);
+        }
+      }
+    } catch {
+      // Ignore errors
+    }
+  };
+  function hasTitleCategories() {
+    return window.document.querySelector(categorySelector) !== null;
+  }
+
+  function offsetRelativeUrl(url) {
+    const offset = getMeta("quarto:offset");
+    return offset ? offset + url : url;
+  }
+
+  function offsetAbsoluteUrl(url) {
+    const offset = getMeta("quarto:offset");
+    const baseUrl = new URL(offset, window.location);
+
+    const projRelativeUrl = url.replace(baseUrl, "");
+    if (projRelativeUrl.startsWith("/")) {
+      return projRelativeUrl;
+    } else {
+      return "/" + projRelativeUrl;
+    }
+  }
+
+  // read a meta tag value
+  function getMeta(metaName) {
+    const metas = window.document.getElementsByTagName("meta");
+    for (let i = 0; i < metas.length; i++) {
+      if (metas[i].getAttribute("name") === metaName) {
+        return metas[i].getAttribute("content");
+      }
+    }
+    return "";
+  }
+
+  async function findAndActivateCategories() {
+    const currentPagePath = offsetAbsoluteUrl(window.location.href);
+    const response = await fetch(offsetRelativeUrl("listings.json"));
+    if (response.status == 200) {
+      return response.json().then(function (listingPaths) {
+        const listingHrefs = [];
+        for (const listingPath of listingPaths) {
+          const pathWithoutLeadingSlash = listingPath.listing.substring(1);
+          for (const item of listingPath.items) {
+            if (
+              item === currentPagePath ||
+              item === currentPagePath + "index.html"
+            ) {
+              // Resolve this path against the offset to be sure
+              // we already are using the correct path to the listing
+              // (this adjusts the listing urls to be rooted against
+              // whatever root the page is actually running against)
+              const relative = offsetRelativeUrl(pathWithoutLeadingSlash);
+              const baseUrl = window.location;
+              const resolvedPath = new URL(relative, baseUrl);
+              listingHrefs.push(resolvedPath.pathname);
+              break;
+            }
+          }
+        }
+
+        // Look up the tree for a nearby linting and use that if we find one
+        const nearestListing = findNearestParentListing(
+          offsetAbsoluteUrl(window.location.pathname),
+          listingHrefs
+        );
+        if (nearestListing) {
+          activateCategories(nearestListing);
+        } else {
+          // See if the referrer is a listing page for this item
+          const referredRelativePath = offsetAbsoluteUrl(document.referrer);
+          const referrerListing = listingHrefs.find((listingHref) => {
+            const isListingReferrer =
+              listingHref === referredRelativePath ||
+              listingHref === referredRelativePath + "index.html";
+            return isListingReferrer;
+          });
+
+          if (referrerListing) {
+            // Try to use the referrer if possible
+            activateCategories(referrerListing);
+          } else if (listingHrefs.length > 0) {
+            // Otherwise, just fall back to the first listing
+            activateCategories(listingHrefs[0]);
+          }
+        }
+      });
+    }
+  }
+  if (hasTitleCategories()) {
+    findAndActivateCategories();
+  }
+
+  const findNearestParentListing = (href, listingHrefs) => {
+    if (!href || !listingHrefs) {
+      return undefined;
+    }
+    // Look up the tree for a nearby linting and use that if we find one
+    const relativeParts = href.substring(1).split("/");
+    while (relativeParts.length > 0) {
+      const path = relativeParts.join("/");
+      for (const listingHref of listingHrefs) {
+        if (listingHref.startsWith(path)) {
+          return listingHref;
+        }
+      }
+      relativeParts.pop();
+    }
+
+    return undefined;
+  };
+
+  const manageSidebarVisiblity = (el, placeholderDescriptor) => {
+    let isVisible = true;
+    let elRect;
+
+    return (hiddenRegions) => {
+      if (el === null) {
+        return;
+      }
+
+      // Find the last element of the TOC
+      const lastChildEl = el.lastElementChild;
+
+      if (lastChildEl) {
+        // Converts the sidebar to a menu
+        const convertToMenu = () => {
+          for (const child of el.children) {
+            child.style.opacity = 0;
+            child.style.overflow = "hidden";
+            child.style.pointerEvents = "none";
+          }
+
+          nexttick(() => {
+            const toggleContainer = window.document.createElement("div");
+            toggleContainer.style.width = "100%";
+            toggleContainer.classList.add("zindex-over-content");
+            toggleContainer.classList.add("quarto-sidebar-toggle");
+            toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom
+            toggleContainer.id = placeholderDescriptor.id;
+            toggleContainer.style.position = "fixed";
+
+            const toggleIcon = window.document.createElement("i");
+            toggleIcon.classList.add("quarto-sidebar-toggle-icon");
+            toggleIcon.classList.add("bi");
+            toggleIcon.classList.add("bi-caret-down-fill");
+
+            const toggleTitle = window.document.createElement("div");
+            const titleEl = window.document.body.querySelector(
+              placeholderDescriptor.titleSelector
+            );
+            if (titleEl) {
+              toggleTitle.append(
+                titleEl.textContent || titleEl.innerText,
+                toggleIcon
+              );
+            }
+            toggleTitle.classList.add("zindex-over-content");
+            toggleTitle.classList.add("quarto-sidebar-toggle-title");
+            toggleContainer.append(toggleTitle);
+
+            const toggleContents = window.document.createElement("div");
+            toggleContents.classList = el.classList;
+            toggleContents.classList.add("zindex-over-content");
+            toggleContents.classList.add("quarto-sidebar-toggle-contents");
+            for (const child of el.children) {
+              if (child.id === "toc-title") {
+                continue;
+              }
+
+              const clone = child.cloneNode(true);
+              clone.style.opacity = 1;
+              clone.style.pointerEvents = null;
+              clone.style.display = null;
+              toggleContents.append(clone);
+            }
+            toggleContents.style.height = "0px";
+            const positionToggle = () => {
+              // position the element (top left of parent, same width as parent)
+              if (!elRect) {
+                elRect = el.getBoundingClientRect();
+              }
+              toggleContainer.style.left = `${elRect.left}px`;
+              toggleContainer.style.top = `${elRect.top}px`;
+              toggleContainer.style.width = `${elRect.width}px`;
+            };
+            positionToggle();
+
+            toggleContainer.append(toggleContents);
+            el.parentElement.prepend(toggleContainer);
+
+            // Process clicks
+            let tocShowing = false;
+            // Allow the caller to control whether this is dismissed
+            // when it is clicked (e.g. sidebar navigation supports
+            // opening and closing the nav tree, so don't dismiss on click)
+            const clickEl = placeholderDescriptor.dismissOnClick
+              ? toggleContainer
+              : toggleTitle;
+
+            const closeToggle = () => {
+              if (tocShowing) {
+                toggleContainer.classList.remove("expanded");
+                toggleContents.style.height = "0px";
+                tocShowing = false;
+              }
+            };
+
+            // Get rid of any expanded toggle if the user scrolls
+            window.document.addEventListener(
+              "scroll",
+              throttle(() => {
+                closeToggle();
+              }, 50)
+            );
+
+            // Handle positioning of the toggle
+            window.addEventListener(
+              "resize",
+              throttle(() => {
+                elRect = undefined;
+                positionToggle();
+              }, 50)
+            );
+
+            window.addEventListener("quarto-hrChanged", () => {
+              elRect = undefined;
+            });
+
+            // Process the click
+            clickEl.onclick = () => {
+              if (!tocShowing) {
+                toggleContainer.classList.add("expanded");
+                toggleContents.style.height = null;
+                tocShowing = true;
+              } else {
+                closeToggle();
+              }
+            };
+          });
+        };
+
+        // Converts a sidebar from a menu back to a sidebar
+        const convertToSidebar = () => {
+          for (const child of el.children) {
+            child.style.opacity = 1;
+            child.style.overflow = null;
+            child.style.pointerEvents = null;
+          }
+
+          const placeholderEl = window.document.getElementById(
+            placeholderDescriptor.id
+          );
+          if (placeholderEl) {
+            placeholderEl.remove();
+          }
+
+          el.classList.remove("rollup");
+        };
+
+        if (isReaderMode()) {
+          convertToMenu();
+          isVisible = false;
+        } else {
+          // Find the top and bottom o the element that is being managed
+          const elTop = el.offsetTop;
+          const elBottom =
+            elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;
+
+          if (!isVisible) {
+            // If the element is current not visible reveal if there are
+            // no conflicts with overlay regions
+            if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {
+              convertToSidebar();
+              isVisible = true;
+            }
+          } else {
+            // If the element is visible, hide it if it conflicts with overlay regions
+            // and insert a placeholder toggle (or if we're in reader mode)
+            if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {
+              convertToMenu();
+              isVisible = false;
+            }
+          }
+        }
+      }
+    };
+  };
+
+  const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]');
+  for (const tabEl of tabEls) {
+    const id = tabEl.getAttribute("data-bs-target");
+    if (id) {
+      const columnEl = document.querySelector(
+        `${id} .column-margin, .tabset-margin-content`
+      );
+      if (columnEl)
+        tabEl.addEventListener("shown.bs.tab", function (event) {
+          const el = event.srcElement;
+          if (el) {
+            const visibleCls = `${el.id}-margin-content`;
+            // walk up until we find a parent tabset
+            let panelTabsetEl = el.parentElement;
+            while (panelTabsetEl) {
+              if (panelTabsetEl.classList.contains("panel-tabset")) {
+                break;
+              }
+              panelTabsetEl = panelTabsetEl.parentElement;
+            }
+
+            if (panelTabsetEl) {
+              const prevSib = panelTabsetEl.previousElementSibling;
+              if (
+                prevSib &&
+                prevSib.classList.contains("tabset-margin-container")
+              ) {
+                const childNodes = prevSib.querySelectorAll(
+                  ".tabset-margin-content"
+                );
+                for (const childEl of childNodes) {
+                  if (childEl.classList.contains(visibleCls)) {
+                    childEl.classList.remove("collapse");
+                  } else {
+                    childEl.classList.add("collapse");
+                  }
+                }
+              }
+            }
+          }
+
+          layoutMarginEls();
+        });
+    }
+  }
+
+  // Manage the visibility of the toc and the sidebar
+  const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
+    id: "quarto-toc-toggle",
+    titleSelector: "#toc-title",
+    dismissOnClick: true,
+  });
+  const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
+    id: "quarto-sidebarnav-toggle",
+    titleSelector: ".title",
+    dismissOnClick: false,
+  });
+  let tocLeftScrollVisibility;
+  if (leftTocEl) {
+    tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
+      id: "quarto-lefttoc-toggle",
+      titleSelector: "#toc-title",
+      dismissOnClick: true,
+    });
+  }
+
+  // Find the first element that uses formatting in special columns
+  const conflictingEls = window.document.body.querySelectorAll(
+    '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
+  );
+
+  // Filter all the possibly conflicting elements into ones
+  // the do conflict on the left or ride side
+  const arrConflictingEls = Array.from(conflictingEls);
+  const leftSideConflictEls = arrConflictingEls.filter((el) => {
+    if (el.tagName === "ASIDE") {
+      return false;
+    }
+    return Array.from(el.classList).find((className) => {
+      return (
+        className !== "column-body" &&
+        className.startsWith("column-") &&
+        !className.endsWith("right") &&
+        !className.endsWith("container") &&
+        className !== "column-margin"
+      );
+    });
+  });
+  const rightSideConflictEls = arrConflictingEls.filter((el) => {
+    if (el.tagName === "ASIDE") {
+      return true;
+    }
+
+    const hasMarginCaption = Array.from(el.classList).find((className) => {
+      return className == "margin-caption";
+    });
+    if (hasMarginCaption) {
+      return true;
+    }
+
+    return Array.from(el.classList).find((className) => {
+      return (
+        className !== "column-body" &&
+        !className.endsWith("container") &&
+        className.startsWith("column-") &&
+        !className.endsWith("left")
+      );
+    });
+  });
+
+  const kOverlapPaddingSize = 10;
+  function toRegions(els) {
+    return els.map((el) => {
+      const boundRect = el.getBoundingClientRect();
+      const top =
+        boundRect.top +
+        document.documentElement.scrollTop -
+        kOverlapPaddingSize;
+      return {
+        top,
+        bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
+      };
+    });
+  }
+
+  let hasObserved = false;
+  const visibleItemObserver = (els) => {
+    let visibleElements = [...els];
+    const intersectionObserver = new IntersectionObserver(
+      (entries, _observer) => {
+        entries.forEach((entry) => {
+          if (entry.isIntersecting) {
+            if (visibleElements.indexOf(entry.target) === -1) {
+              visibleElements.push(entry.target);
+            }
+          } else {
+            visibleElements = visibleElements.filter((visibleEntry) => {
+              return visibleEntry !== entry;
+            });
+          }
+        });
+
+        if (!hasObserved) {
+          hideOverlappedSidebars();
+        }
+        hasObserved = true;
+      },
+      {}
+    );
+    els.forEach((el) => {
+      intersectionObserver.observe(el);
+    });
+
+    return {
+      getVisibleEntries: () => {
+        return visibleElements;
+      },
+    };
+  };
+
+  const rightElementObserver = visibleItemObserver(rightSideConflictEls);
+  const leftElementObserver = visibleItemObserver(leftSideConflictEls);
+
+  const hideOverlappedSidebars = () => {
+    marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries()));
+    sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries()));
+    if (tocLeftScrollVisibility) {
+      tocLeftScrollVisibility(
+        toRegions(leftElementObserver.getVisibleEntries())
+      );
+    }
+  };
+
+  window.quartoToggleReader = () => {
+    // Applies a slow class (or removes it)
+    // to update the transition speed
+    const slowTransition = (slow) => {
+      const manageTransition = (id, slow) => {
+        const el = document.getElementById(id);
+        if (el) {
+          if (slow) {
+            el.classList.add("slow");
+          } else {
+            el.classList.remove("slow");
+          }
+        }
+      };
+
+      manageTransition("TOC", slow);
+      manageTransition("quarto-sidebar", slow);
+    };
+    const readerMode = !isReaderMode();
+    setReaderModeValue(readerMode);
+
+    // If we're entering reader mode, slow the transition
+    if (readerMode) {
+      slowTransition(readerMode);
+    }
+    highlightReaderToggle(readerMode);
+    hideOverlappedSidebars();
+
+    // If we're exiting reader mode, restore the non-slow transition
+    if (!readerMode) {
+      slowTransition(!readerMode);
+    }
+  };
+
+  const highlightReaderToggle = (readerMode) => {
+    const els = document.querySelectorAll(".quarto-reader-toggle");
+    if (els) {
+      els.forEach((el) => {
+        if (readerMode) {
+          el.classList.add("reader");
+        } else {
+          el.classList.remove("reader");
+        }
+      });
+    }
+  };
+
+  const setReaderModeValue = (val) => {
+    if (window.location.protocol !== "file:") {
+      window.localStorage.setItem("quarto-reader-mode", val);
+    } else {
+      localReaderMode = val;
+    }
+  };
+
+  const isReaderMode = () => {
+    if (window.location.protocol !== "file:") {
+      return window.localStorage.getItem("quarto-reader-mode") === "true";
+    } else {
+      return localReaderMode;
+    }
+  };
+  let localReaderMode = null;
+
+  const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded");
+  const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1;
+
+  // Walk the TOC and collapse/expand nodes
+  // Nodes are expanded if:
+  // - they are top level
+  // - they have children that are 'active' links
+  // - they are directly below an link that is 'active'
+  const walk = (el, depth) => {
+    // Tick depth when we enter a UL
+    if (el.tagName === "UL") {
+      depth = depth + 1;
+    }
+
+    // It this is active link
+    let isActiveNode = false;
+    if (el.tagName === "A" && el.classList.contains("active")) {
+      isActiveNode = true;
+    }
+
+    // See if there is an active child to this element
+    let hasActiveChild = false;
+    for (child of el.children) {
+      hasActiveChild = walk(child, depth) || hasActiveChild;
+    }
+
+    // Process the collapse state if this is an UL
+    if (el.tagName === "UL") {
+      if (tocOpenDepth === -1 && depth > 1) {
+        // toc-expand: false
+        el.classList.add("collapse");
+      } else if (
+        depth <= tocOpenDepth ||
+        hasActiveChild ||
+        prevSiblingIsActiveLink(el)
+      ) {
+        el.classList.remove("collapse");
+      } else {
+        el.classList.add("collapse");
+      }
+
+      // untick depth when we leave a UL
+      depth = depth - 1;
+    }
+    return hasActiveChild || isActiveNode;
+  };
+
+  // walk the TOC and expand / collapse any items that should be shown
+  if (tocEl) {
+    updateActiveLink();
+    walk(tocEl, 0);
+  }
+
+  // Throttle the scroll event and walk peridiocally
+  window.document.addEventListener(
+    "scroll",
+    throttle(() => {
+      if (tocEl) {
+        updateActiveLink();
+        walk(tocEl, 0);
+      }
+      if (!isReaderMode()) {
+        hideOverlappedSidebars();
+      }
+    }, 5)
+  );
+  window.addEventListener(
+    "resize",
+    throttle(() => {
+      if (tocEl) {
+        updateActiveLink();
+        walk(tocEl, 0);
+      }
+      if (!isReaderMode()) {
+        hideOverlappedSidebars();
+      }
+    }, 10)
+  );
+  hideOverlappedSidebars();
+  highlightReaderToggle(isReaderMode());
+});
+
+// grouped tabsets
+window.addEventListener("pageshow", (_event) => {
+  function getTabSettings() {
+    const data = localStorage.getItem("quarto-persistent-tabsets-data");
+    if (!data) {
+      localStorage.setItem("quarto-persistent-tabsets-data", "{}");
+      return {};
+    }
+    if (data) {
+      return JSON.parse(data);
+    }
+  }
+
+  function setTabSettings(data) {
+    localStorage.setItem(
+      "quarto-persistent-tabsets-data",
+      JSON.stringify(data)
+    );
+  }
+
+  function setTabState(groupName, groupValue) {
+    const data = getTabSettings();
+    data[groupName] = groupValue;
+    setTabSettings(data);
+  }
+
+  function toggleTab(tab, active) {
+    const tabPanelId = tab.getAttribute("aria-controls");
+    const tabPanel = document.getElementById(tabPanelId);
+    if (active) {
+      tab.classList.add("active");
+      tabPanel.classList.add("active");
+    } else {
+      tab.classList.remove("active");
+      tabPanel.classList.remove("active");
+    }
+  }
+
+  function toggleAll(selectedGroup, selectorsToSync) {
+    for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
+      const active = selectedGroup === thisGroup;
+      for (const tab of tabs) {
+        toggleTab(tab, active);
+      }
+    }
+  }
+
+  function findSelectorsToSyncByLanguage() {
+    const result = {};
+    const tabs = Array.from(
+      document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
+    );
+    for (const item of tabs) {
+      const div = item.parentElement.parentElement.parentElement;
+      const group = div.getAttribute("data-group");
+      if (!result[group]) {
+        result[group] = {};
+      }
+      const selectorsToSync = result[group];
+      const value = item.innerHTML;
+      if (!selectorsToSync[value]) {
+        selectorsToSync[value] = [];
+      }
+      selectorsToSync[value].push(item);
+    }
+    return result;
+  }
+
+  function setupSelectorSync() {
+    const selectorsToSync = findSelectorsToSyncByLanguage();
+    Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
+      Object.entries(tabSetsByValue).forEach(([value, items]) => {
+        items.forEach((item) => {
+          item.addEventListener("click", (_event) => {
+            setTabState(group, value);
+            toggleAll(value, selectorsToSync[group]);
+          });
+        });
+      });
+    });
+    return selectorsToSync;
+  }
+
+  const selectorsToSync = setupSelectorSync();
+  for (const [group, selectedName] of Object.entries(getTabSettings())) {
+    const selectors = selectorsToSync[group];
+    // it's possible that stale state gives us empty selections, so we explicitly check here.
+    if (selectors) {
+      toggleAll(selectedName, selectors);
+    }
+  }
+});
+
+function throttle(func, wait) {
+  let waiting = false;
+  return function () {
+    if (!waiting) {
+      func.apply(this, arguments);
+      waiting = true;
+      setTimeout(function () {
+        waiting = false;
+      }, wait);
+    }
+  };
+}
+
+function nexttick(func) {
+  return setTimeout(func, 0);
+}
diff --git a/_proc/docs/site_libs/quarto-html/tippy.css b/_proc/docs/site_libs/quarto-html/tippy.css
new file mode 100644
index 0000000..e6ae635
--- /dev/null
+++ b/_proc/docs/site_libs/quarto-html/tippy.css
@@ -0,0 +1 @@
+.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 10px)}.tippy-box{position:relative;background-color:#333;color:#fff;border-radius:4px;font-size:14px;line-height:1.4;white-space:normal;outline:0;transition-property:transform,visibility,opacity}.tippy-box[data-placement^=top]>.tippy-arrow{bottom:0}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-7px;left:0;border-width:8px 8px 0;border-top-color:initial;transform-origin:center top}.tippy-box[data-placement^=bottom]>.tippy-arrow{top:0}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-7px;left:0;border-width:0 8px 8px;border-bottom-color:initial;transform-origin:center bottom}.tippy-box[data-placement^=left]>.tippy-arrow{right:0}.tippy-box[data-placement^=left]>.tippy-arrow:before{border-width:8px 0 8px 8px;border-left-color:initial;right:-7px;transform-origin:center left}.tippy-box[data-placement^=right]>.tippy-arrow{left:0}.tippy-box[data-placement^=right]>.tippy-arrow:before{left:-7px;border-width:8px 8px 8px 0;border-right-color:initial;transform-origin:center right}.tippy-box[data-inertia][data-state=visible]{transition-timing-function:cubic-bezier(.54,1.5,.38,1.11)}.tippy-arrow{width:16px;height:16px;color:#333}.tippy-arrow:before{content:"";position:absolute;border-color:transparent;border-style:solid}.tippy-content{position:relative;padding:5px 9px;z-index:1}
\ No newline at end of file
diff --git a/_proc/docs/site_libs/quarto-html/tippy.umd.min.js b/_proc/docs/site_libs/quarto-html/tippy.umd.min.js
new file mode 100644
index 0000000..ca292be
--- /dev/null
+++ b/_proc/docs/site_libs/quarto-html/tippy.umd.min.js
@@ -0,0 +1,2 @@
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t(require("@popperjs/core")):"function"==typeof define&&define.amd?define(["@popperjs/core"],t):(e=e||self).tippy=t(e.Popper)}(this,(function(e){"use strict";var t={passive:!0,capture:!0},n=function(){return document.body};function r(e,t,n){if(Array.isArray(e)){var r=e[t];return null==r?Array.isArray(n)?n[t]:n:r}return e}function o(e,t){var n={}.toString.call(e);return 0===n.indexOf("[object")&&n.indexOf(t+"]")>-1}function i(e,t){return"function"==typeof e?e.apply(void 0,t):e}function a(e,t){return 0===t?e:function(r){clearTimeout(n),n=setTimeout((function(){e(r)}),t)};var n}function s(e,t){var n=Object.assign({},e);return t.forEach((function(e){delete n[e]})),n}function u(e){return[].concat(e)}function c(e,t){-1===e.indexOf(t)&&e.push(t)}function p(e){return e.split("-")[0]}function f(e){return[].slice.call(e)}function l(e){return Object.keys(e).reduce((function(t,n){return void 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diff --git a/_proc/docs/site_libs/quarto-nav/headroom.min.js b/_proc/docs/site_libs/quarto-nav/headroom.min.js
new file mode 100644
index 0000000..b08f1df
--- /dev/null
+++ b/_proc/docs/site_libs/quarto-nav/headroom.min.js
@@ -0,0 +1,7 @@
+/*!
+ * headroom.js v0.12.0 - Give your page some headroom. Hide your header until you need it
+ * Copyright (c) 2020 Nick Williams - http://wicky.nillia.ms/headroom.js
+ * License: MIT
+ */
+
+!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?module.exports=n():"function"==typeof define&&define.amd?define(n):(t=t||self).Headroom=n()}(this,function(){"use strict";function t(){return"undefined"!=typeof window}function d(t){return function(t){return t&&t.document&&function(t){return 9===t.nodeType}(t.document)}(t)?function(t){var n=t.document,o=n.body,s=n.documentElement;return{scrollHeight:function(){return Math.max(o.scrollHeight,s.scrollHeight,o.offsetHeight,s.offsetHeight,o.clientHeight,s.clientHeight)},height:function(){return t.innerHeight||s.clientHeight||o.clientHeight},scrollY:function(){return void 0!==t.pageYOffset?t.pageYOffset:(s||o.parentNode||o).scrollTop}}}(t):function(t){return{scrollHeight:function(){return Math.max(t.scrollHeight,t.offsetHeight,t.clientHeight)},height:function(){return Math.max(t.offsetHeight,t.clientHeight)},scrollY:function(){return t.scrollTop}}}(t)}function n(t,s,e){var n,o=function(){var n=!1;try{var t={get passive(){n=!0}};window.addEventListener("test",t,t),window.removeEventListener("test",t,t)}catch(t){n=!1}return n}(),i=!1,r=d(t),l=r.scrollY(),a={};function c(){var t=Math.round(r.scrollY()),n=r.height(),o=r.scrollHeight();a.scrollY=t,a.lastScrollY=l,a.direction=ls.tolerance[a.direction],e(a),l=t,i=!1}function h(){i||(i=!0,n=requestAnimationFrame(c))}var u=!!o&&{passive:!0,capture:!1};return t.addEventListener("scroll",h,u),c(),{destroy:function(){cancelAnimationFrame(n),t.removeEventListener("scroll",h,u)}}}function o(t){return t===Object(t)?t:{down:t,up:t}}function s(t,n){n=n||{},Object.assign(this,s.options,n),this.classes=Object.assign({},s.options.classes,n.classes),this.elem=t,this.tolerance=o(this.tolerance),this.offset=o(this.offset),this.initialised=!1,this.frozen=!1}return s.prototype={constructor:s,init:function(){return s.cutsTheMustard&&!this.initialised&&(this.addClass("initial"),this.initialised=!0,setTimeout(function(t){t.scrollTracker=n(t.scroller,{offset:t.offset,tolerance:t.tolerance},t.update.bind(t))},100,this)),this},destroy:function(){this.initialised=!1,Object.keys(this.classes).forEach(this.removeClass,this),this.scrollTracker.destroy()},unpin:function(){!this.hasClass("pinned")&&this.hasClass("unpinned")||(this.addClass("unpinned"),this.removeClass("pinned"),this.onUnpin&&this.onUnpin.call(this))},pin:function(){this.hasClass("unpinned")&&(this.addClass("pinned"),this.removeClass("unpinned"),this.onPin&&this.onPin.call(this))},freeze:function(){this.frozen=!0,this.addClass("frozen")},unfreeze:function(){this.frozen=!1,this.removeClass("frozen")},top:function(){this.hasClass("top")||(this.addClass("top"),this.removeClass("notTop"),this.onTop&&this.onTop.call(this))},notTop:function(){this.hasClass("notTop")||(this.addClass("notTop"),this.removeClass("top"),this.onNotTop&&this.onNotTop.call(this))},bottom:function(){this.hasClass("bottom")||(this.addClass("bottom"),this.removeClass("notBottom"),this.onBottom&&this.onBottom.call(this))},notBottom:function(){this.hasClass("notBottom")||(this.addClass("notBottom"),this.removeClass("bottom"),this.onNotBottom&&this.onNotBottom.call(this))},shouldUnpin:function(t){return"down"===t.direction&&!t.top&&t.toleranceExceeded},shouldPin:function(t){return"up"===t.direction&&t.toleranceExceeded||t.top},addClass:function(t){this.elem.classList.add.apply(this.elem.classList,this.classes[t].split(" "))},removeClass:function(t){this.elem.classList.remove.apply(this.elem.classList,this.classes[t].split(" "))},hasClass:function(t){return this.classes[t].split(" ").every(function(t){return this.classList.contains(t)},this.elem)},update:function(t){t.isOutOfBounds||!0!==this.frozen&&(t.top?this.top():this.notTop(),t.bottom?this.bottom():this.notBottom(),this.shouldUnpin(t)?this.unpin():this.shouldPin(t)&&this.pin())}},s.options={tolerance:{up:0,down:0},offset:0,scroller:t()?window:null,classes:{frozen:"headroom--frozen",pinned:"headroom--pinned",unpinned:"headroom--unpinned",top:"headroom--top",notTop:"headroom--not-top",bottom:"headroom--bottom",notBottom:"headroom--not-bottom",initial:"headroom"}},s.cutsTheMustard=!!(t()&&function(){}.bind&&"classList"in document.documentElement&&Object.assign&&Object.keys&&requestAnimationFrame),s});
diff --git a/_proc/docs/site_libs/quarto-nav/quarto-nav.js b/_proc/docs/site_libs/quarto-nav/quarto-nav.js
new file mode 100644
index 0000000..38cc430
--- /dev/null
+++ b/_proc/docs/site_libs/quarto-nav/quarto-nav.js
@@ -0,0 +1,325 @@
+const headroomChanged = new CustomEvent("quarto-hrChanged", {
+  detail: {},
+  bubbles: true,
+  cancelable: false,
+  composed: false,
+});
+
+const announceDismiss = () => {
+  const annEl = window.document.getElementById("quarto-announcement");
+  if (annEl) {
+    annEl.remove();
+
+    const annId = annEl.getAttribute("data-announcement-id");
+    window.localStorage.setItem(`quarto-announce-${annId}`, "true");
+  }
+};
+
+const announceRegister = () => {
+  const annEl = window.document.getElementById("quarto-announcement");
+  if (annEl) {
+    const annId = annEl.getAttribute("data-announcement-id");
+    const isDismissed =
+      window.localStorage.getItem(`quarto-announce-${annId}`) || false;
+    if (isDismissed) {
+      announceDismiss();
+      return;
+    } else {
+      annEl.classList.remove("hidden");
+    }
+
+    const actionEl = annEl.querySelector(".quarto-announcement-action");
+    if (actionEl) {
+      actionEl.addEventListener("click", function (e) {
+        e.preventDefault();
+        // Hide the bar immediately
+        announceDismiss();
+      });
+    }
+  }
+};
+
+window.document.addEventListener("DOMContentLoaded", function () {
+  let init = false;
+
+  announceRegister();
+
+  // Manage the back to top button, if one is present.
+  let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop;
+  const scrollDownBuffer = 5;
+  const scrollUpBuffer = 35;
+  const btn = document.getElementById("quarto-back-to-top");
+  const hideBackToTop = () => {
+    btn.style.display = "none";
+  };
+  const showBackToTop = () => {
+    btn.style.display = "inline-block";
+  };
+  if (btn) {
+    window.document.addEventListener(
+      "scroll",
+      function () {
+        const currentScrollTop =
+          window.pageYOffset || document.documentElement.scrollTop;
+
+        // Shows and hides the button 'intelligently' as the user scrolls
+        if (currentScrollTop - scrollDownBuffer > lastScrollTop) {
+          hideBackToTop();
+          lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop;
+        } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) {
+          showBackToTop();
+          lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop;
+        }
+
+        // Show the button at the bottom, hides it at the top
+        if (currentScrollTop <= 0) {
+          hideBackToTop();
+        } else if (
+          window.innerHeight + currentScrollTop >=
+          document.body.offsetHeight
+        ) {
+          showBackToTop();
+        }
+      },
+      false
+    );
+  }
+
+  function throttle(func, wait) {
+    var timeout;
+    return function () {
+      const context = this;
+      const args = arguments;
+      const later = function () {
+        clearTimeout(timeout);
+        timeout = null;
+        func.apply(context, args);
+      };
+
+      if (!timeout) {
+        timeout = setTimeout(later, wait);
+      }
+    };
+  }
+
+  function headerOffset() {
+    // Set an offset if there is are fixed top navbar
+    const headerEl = window.document.querySelector("header.fixed-top");
+    if (headerEl) {
+      return headerEl.clientHeight;
+    } else {
+      return 0;
+    }
+  }
+
+  function footerOffset() {
+    const footerEl = window.document.querySelector("footer.footer");
+    if (footerEl) {
+      return footerEl.clientHeight;
+    } else {
+      return 0;
+    }
+  }
+
+  function dashboardOffset() {
+    const dashboardNavEl = window.document.getElementById(
+      "quarto-dashboard-header"
+    );
+    if (dashboardNavEl !== null) {
+      return dashboardNavEl.clientHeight;
+    } else {
+      return 0;
+    }
+  }
+
+  function updateDocumentOffsetWithoutAnimation() {
+    updateDocumentOffset(false);
+  }
+
+  function updateDocumentOffset(animated) {
+    // set body offset
+    const topOffset = headerOffset();
+    const bodyOffset = topOffset + footerOffset() + dashboardOffset();
+    const bodyEl = window.document.body;
+    bodyEl.setAttribute("data-bs-offset", topOffset);
+    bodyEl.style.paddingTop = topOffset + "px";
+
+    // deal with sidebar offsets
+    const sidebars = window.document.querySelectorAll(
+      ".sidebar, .headroom-target"
+    );
+    sidebars.forEach((sidebar) => {
+      if (!animated) {
+        sidebar.classList.add("notransition");
+        // Remove the no transition class after the animation has time to complete
+        setTimeout(function () {
+          sidebar.classList.remove("notransition");
+        }, 201);
+      }
+
+      if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) {
+        sidebar.style.top = "0";
+        sidebar.style.maxHeight = "100vh";
+      } else {
+        sidebar.style.top = topOffset + "px";
+        sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)";
+      }
+    });
+
+    // allow space for footer
+    const mainContainer = window.document.querySelector(".quarto-container");
+    if (mainContainer) {
+      mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)";
+    }
+
+    // link offset
+    let linkStyle = window.document.querySelector("#quarto-target-style");
+    if (!linkStyle) {
+      linkStyle = window.document.createElement("style");
+      linkStyle.setAttribute("id", "quarto-target-style");
+      window.document.head.appendChild(linkStyle);
+    }
+    while (linkStyle.firstChild) {
+      linkStyle.removeChild(linkStyle.firstChild);
+    }
+    if (topOffset > 0) {
+      linkStyle.appendChild(
+        window.document.createTextNode(`
+      section:target::before {
+        content: "";
+        display: block;
+        height: ${topOffset}px;
+        margin: -${topOffset}px 0 0;
+      }`)
+      );
+    }
+    if (init) {
+      window.dispatchEvent(headroomChanged);
+    }
+    init = true;
+  }
+
+  // initialize headroom
+  var header = window.document.querySelector("#quarto-header");
+  if (header && window.Headroom) {
+    const headroom = new window.Headroom(header, {
+      tolerance: 5,
+      onPin: function () {
+        const sidebars = window.document.querySelectorAll(
+          ".sidebar, .headroom-target"
+        );
+        sidebars.forEach((sidebar) => {
+          sidebar.classList.remove("sidebar-unpinned");
+        });
+        updateDocumentOffset();
+      },
+      onUnpin: function () {
+        const sidebars = window.document.querySelectorAll(
+          ".sidebar, .headroom-target"
+        );
+        sidebars.forEach((sidebar) => {
+          sidebar.classList.add("sidebar-unpinned");
+        });
+        updateDocumentOffset();
+      },
+    });
+    headroom.init();
+
+    let frozen = false;
+    window.quartoToggleHeadroom = function () {
+      if (frozen) {
+        headroom.unfreeze();
+        frozen = false;
+      } else {
+        headroom.freeze();
+        frozen = true;
+      }
+    };
+  }
+
+  window.addEventListener(
+    "hashchange",
+    function (e) {
+      if (
+        getComputedStyle(document.documentElement).scrollBehavior !== "smooth"
+      ) {
+        window.scrollTo(0, window.pageYOffset - headerOffset());
+      }
+    },
+    false
+  );
+
+  // Observe size changed for the header
+  const headerEl = window.document.querySelector("header.fixed-top");
+  if (headerEl && window.ResizeObserver) {
+    const observer = new window.ResizeObserver(() => {
+      setTimeout(updateDocumentOffsetWithoutAnimation, 0);
+    });
+    observer.observe(headerEl, {
+      attributes: true,
+      childList: true,
+      characterData: true,
+    });
+  } else {
+    window.addEventListener(
+      "resize",
+      throttle(updateDocumentOffsetWithoutAnimation, 50)
+    );
+  }
+  setTimeout(updateDocumentOffsetWithoutAnimation, 250);
+
+  // fixup index.html links if we aren't on the filesystem
+  if (window.location.protocol !== "file:") {
+    const links = window.document.querySelectorAll("a");
+    for (let i = 0; i < links.length; i++) {
+      if (links[i].href) {
+        links[i].dataset.originalHref = links[i].href;
+        links[i].href = links[i].href.replace(/\/index\.html/, "/");
+      }
+    }
+
+    // Fixup any sharing links that require urls
+    // Append url to any sharing urls
+    const sharingLinks = window.document.querySelectorAll(
+      "a.sidebar-tools-main-item, a.quarto-navigation-tool, a.quarto-navbar-tools, a.quarto-navbar-tools-item"
+    );
+    for (let i = 0; i < sharingLinks.length; i++) {
+      const sharingLink = sharingLinks[i];
+      const href = sharingLink.getAttribute("href");
+      if (href) {
+        sharingLink.setAttribute(
+          "href",
+          href.replace("|url|", window.location.href)
+        );
+      }
+    }
+
+    // Scroll the active navigation item into view, if necessary
+    const navSidebar = window.document.querySelector("nav#quarto-sidebar");
+    if (navSidebar) {
+      // Find the active item
+      const activeItem = navSidebar.querySelector("li.sidebar-item a.active");
+      if (activeItem) {
+        // Wait for the scroll height and height to resolve by observing size changes on the
+        // nav element that is scrollable
+        const resizeObserver = new ResizeObserver((_entries) => {
+          // The bottom of the element
+          const elBottom = activeItem.offsetTop;
+          const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight;
+
+          // The element height and scroll height are the same, then we are still loading
+          if (viewBottom !== navSidebar.scrollHeight) {
+            // Determine if the item isn't visible and scroll to it
+            if (elBottom >= viewBottom) {
+              navSidebar.scrollTop = elBottom;
+            }
+
+            // stop observing now since we've completed the scroll
+            resizeObserver.unobserve(navSidebar);
+          }
+        });
+        resizeObserver.observe(navSidebar);
+      }
+    }
+  }
+});
diff --git a/_proc/docs/site_libs/quarto-search/autocomplete.umd.js b/_proc/docs/site_libs/quarto-search/autocomplete.umd.js
new file mode 100644
index 0000000..ae0063a
--- /dev/null
+++ b/_proc/docs/site_libs/quarto-search/autocomplete.umd.js
@@ -0,0 +1,3 @@
+/*! @algolia/autocomplete-js 1.11.1 | MIT License | © Algolia, Inc. and contributors | https://github.com/algolia/autocomplete */
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self)["@algolia/autocomplete-js"]={})}(this,(function(e){"use strict";function t(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function n(e){for(var n=1;n=0||(o[n]=e[n]);return o}(e,t);if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(e);for(r=0;r=0||Object.prototype.propertyIsEnumerable.call(e,n)&&(o[n]=e[n])}return o}function a(e,t){return function(e){if(Array.isArray(e))return e}(e)||function(e,t){var n=null==e?null:"undefined"!=typeof Symbol&&e[Symbol.iterator]||e["@@iterator"];if(null!=n){var r,o,i,u,a=[],l=!0,c=!1;try{if(i=(n=n.call(e)).next,0===t){if(Object(n)!==n)return;l=!1}else for(;!(l=(r=i.call(n)).done)&&(a.push(r.value),a.length!==t);l=!0);}catch(e){c=!0,o=e}finally{try{if(!l&&null!=n.return&&(u=n.return(),Object(u)!==u))return}finally{if(c)throw o}}return a}}(e,t)||c(e,t)||function(){throw new TypeError("Invalid attempt to destructure non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}()}function l(e){return function(e){if(Array.isArray(e))return s(e)}(e)||function(e){if("undefined"!=typeof Symbol&&null!=e[Symbol.iterator]||null!=e["@@iterator"])return Array.from(e)}(e)||c(e)||function(){throw new TypeError("Invalid attempt to spread non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}()}function c(e,t){if(e){if("string"==typeof e)return s(e,t);var n=Object.prototype.toString.call(e).slice(8,-1);return"Object"===n&&e.constructor&&(n=e.constructor.name),"Map"===n||"Set"===n?Array.from(e):"Arguments"===n||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(n)?s(e,t):void 0}}function s(e,t){(null==t||t>e.length)&&(t=e.length);for(var n=0,r=new Array(t);ne.length)&&(t=e.length);for(var n=0,r=new Array(t);ne.length)&&(t=e.length);for(var n=0,r=new Array(t);n=0||(o[n]=e[n]);return o}(e,t);if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(e);for(r=0;r=0||Object.prototype.propertyIsEnumerable.call(e,n)&&(o[n]=e[n])}return o}function x(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function N(e){for(var t=1;t1&&void 0!==arguments[1]?arguments[1]:20,n=[],r=0;r=3||2===n&&r>=4||1===n&&r>=10);function i(t,n,r){if(o&&void 0!==r){var i=r[0].__autocomplete_algoliaCredentials,u={"X-Algolia-Application-Id":i.appId,"X-Algolia-API-Key":i.apiKey};e.apply(void 0,[t].concat(D(n),[{headers:u}]))}else e.apply(void 0,[t].concat(D(n)))}return{init:function(t,n){e("init",{appId:t,apiKey:n})},setUserToken:function(t){e("setUserToken",t)},clickedObjectIDsAfterSearch:function(){for(var e=arguments.length,t=new Array(e),n=0;n0&&i("clickedObjectIDsAfterSearch",B(t),t[0].items)},clickedObjectIDs:function(){for(var e=arguments.length,t=new Array(e),n=0;n0&&i("clickedObjectIDs",B(t),t[0].items)},clickedFilters:function(){for(var t=arguments.length,n=new Array(t),r=0;r0&&e.apply(void 0,["clickedFilters"].concat(n))},convertedObjectIDsAfterSearch:function(){for(var e=arguments.length,t=new Array(e),n=0;n0&&i("convertedObjectIDsAfterSearch",B(t),t[0].items)},convertedObjectIDs:function(){for(var e=arguments.length,t=new Array(e),n=0;n0&&i("convertedObjectIDs",B(t),t[0].items)},convertedFilters:function(){for(var t=arguments.length,n=new 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diff --git a/_proc/docs/site_libs/quarto-search/fuse.min.js b/_proc/docs/site_libs/quarto-search/fuse.min.js
new file mode 100644
index 0000000..adc2835
--- /dev/null
+++ b/_proc/docs/site_libs/quarto-search/fuse.min.js
@@ -0,0 +1,9 @@
+/**
+ * Fuse.js v6.6.2 - Lightweight fuzzy-search (http://fusejs.io)
+ *
+ * Copyright (c) 2022 Kiro Risk (http://kiro.me)
+ * All Rights Reserved. Apache Software License 2.0
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ */
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t=!e.startsWith(this.pattern);return{isMatch:t,score:t?0:1,indices:[0,e.length-1]}}}],[{key:"type",get:function(){return"inverse-prefix-exact"}},{key:"multiRegex",get:function(){return/^!\^"(.*)"$/}},{key:"singleRegex",get:function(){return/^!\^(.*)$/}}]),n}(z),U=function(e){a(n,e);var t=l(n);function n(e){return r(this,n),t.call(this,e)}return o(n,[{key:"search",value:function(e){var t=e.endsWith(this.pattern);return{isMatch:t,score:t?0:1,indices:[e.length-this.pattern.length,e.length-1]}}}],[{key:"type",get:function(){return"suffix-exact"}},{key:"multiRegex",get:function(){return/^"(.*)"\$$/}},{key:"singleRegex",get:function(){return/^(.*)\$$/}}]),n}(z),V=function(e){a(n,e);var t=l(n);function n(e){return r(this,n),t.call(this,e)}return o(n,[{key:"search",value:function(e){var t=!e.endsWith(this.pattern);return{isMatch:t,score:t?0:1,indices:[0,e.length-1]}}}],[{key:"type",get:function(){return"inverse-suffix-exact"}},{key:"multiRegex",get:function(){return/^!"(.*)"\$$/}},{key:"singleRegex",get:function(){return/^!(.*)\$$/}}]),n}(z),G=function(e){a(n,e);var t=l(n);function n(e){var i,o=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},c=o.location,a=void 0===c?I.location:c,s=o.threshold,u=void 0===s?I.threshold:s,h=o.distance,l=void 0===h?I.distance:h,f=o.includeMatches,d=void 0===f?I.includeMatches:f,v=o.findAllMatches,g=void 0===v?I.findAllMatches:v,y=o.minMatchCharLength,p=void 0===y?I.minMatchCharLength:y,m=o.isCaseSensitive,k=void 0===m?I.isCaseSensitive:m,M=o.ignoreLocation,b=void 0===M?I.ignoreLocation:M;return r(this,n),(i=t.call(this,e))._bitapSearch=new T(e,{location:a,threshold:u,distance:l,includeMatches:d,findAllMatches:g,minMatchCharLength:p,isCaseSensitive:k,ignoreLocation:b}),i}return o(n,[{key:"search",value:function(e){return this._bitapSearch.searchIn(e)}}],[{key:"type",get:function(){return"fuzzy"}},{key:"multiRegex",get:function(){return/^"(.*)"$/}},{key:"singleRegex",get:function(){return/^(.*)$/}}]),n}(z),H=function(e){a(n,e);var t=l(n);function n(e){return r(this,n),t.call(this,e)}return o(n,[{key:"search",value:function(e){for(var t,n=0,r=[],i=this.pattern.length;(t=e.indexOf(this.pattern,n))>-1;)n=t+i,r.push([t,n-1]);var o=!!r.length;return{isMatch:o,score:o?0:1,indices:r}}}],[{key:"type",get:function(){return"include"}},{key:"multiRegex",get:function(){return/^'"(.*)"$/}},{key:"singleRegex",get:function(){return/^'(.*)$/}}]),n}(z),Q=[K,H,B,J,V,U,q,G],X=Q.length,Y=/ +(?=(?:[^\"]*\"[^\"]*\")*[^\"]*$)/;function Z(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return e.split("|").map((function(e){for(var n=e.trim().split(Y).filter((function(e){return e&&!!e.trim()})),r=[],i=0,o=n.length;i1&&void 0!==arguments[1]?arguments[1]:{},i=n.isCaseSensitive,o=void 0===i?I.isCaseSensitive:i,c=n.includeMatches,a=void 0===c?I.includeMatches:c,s=n.minMatchCharLength,u=void 0===s?I.minMatchCharLength:s,h=n.ignoreLocation,l=void 0===h?I.ignoreLocation:h,f=n.findAllMatches,d=void 0===f?I.findAllMatches:f,v=n.location,g=void 0===v?I.location:v,y=n.threshold,p=void 0===y?I.threshold:y,m=n.distance,k=void 0===m?I.distance:m;r(this,e),this.query=null,this.options={isCaseSensitive:o,includeMatches:a,minMatchCharLength:u,findAllMatches:d,ignoreLocation:l,location:g,threshold:p,distance:k},this.pattern=o?t:t.toLowerCase(),this.query=Z(this.pattern,this.options)}return o(e,[{key:"searchIn",value:function(e){var t=this.query;if(!t)return{isMatch:!1,score:1};var n=this.options,r=n.includeMatches;e=n.isCaseSensitive?e:e.toLowerCase();for(var i=0,o=[],c=0,a=0,s=t.length;a-1&&(n.refIndex=e.idx),t.matches.push(n)}}))}function ve(e,t){t.score=e.score}function ge(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{},r=n.includeMatches,i=void 0===r?I.includeMatches:r,o=n.includeScore,c=void 0===o?I.includeScore:o,a=[];return i&&a.push(de),c&&a.push(ve),e.map((function(e){var n=e.idx,r={item:t[n],refIndex:n};return a.length&&a.forEach((function(t){t(e,r)})),r}))}var ye=function(){function e(n){var i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},o=arguments.length>2?arguments[2]:void 0;r(this,e),this.options=t(t({},I),i),this.options.useExtendedSearch,this._keyStore=new S(this.options.keys),this.setCollection(n,o)}return o(e,[{key:"setCollection",value:function(e,t){if(this._docs=e,t&&!(t instanceof $))throw new Error("Incorrect 'index' type");this._myIndex=t||F(this.options.keys,this._docs,{getFn:this.options.getFn,fieldNormWeight:this.options.fieldNormWeight})}},{key:"add",value:function(e){k(e)&&(this._docs.push(e),this._myIndex.add(e))}},{key:"remove",value:function(){for(var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:function(){return!1},t=[],n=0,r=this._docs.length;n1&&void 0!==arguments[1]?arguments[1]:{},n=t.limit,r=void 0===n?-1:n,i=this.options,o=i.includeMatches,c=i.includeScore,a=i.shouldSort,s=i.sortFn,u=i.ignoreFieldNorm,h=g(e)?g(this._docs[0])?this._searchStringList(e):this._searchObjectList(e):this._searchLogical(e);return fe(h,{ignoreFieldNorm:u}),a&&h.sort(s),y(r)&&r>-1&&(h=h.slice(0,r)),ge(h,this._docs,{includeMatches:o,includeScore:c})}},{key:"_searchStringList",value:function(e){var t=re(e,this.options),n=this._myIndex.records,r=[];return n.forEach((function(e){var n=e.v,i=e.i,o=e.n;if(k(n)){var c=t.searchIn(n),a=c.isMatch,s=c.score,u=c.indices;a&&r.push({item:n,idx:i,matches:[{score:s,value:n,norm:o,indices:u}]})}})),r}},{key:"_searchLogical",value:function(e){var t=this,n=function(e,t){var n=(arguments.length>2&&void 0!==arguments[2]?arguments[2]:{}).auto,r=void 0===n||n,i=function e(n){var i=Object.keys(n),o=ue(n);if(!o&&i.length>1&&!se(n))return e(le(n));if(he(n)){var c=o?n[ce]:i[0],a=o?n[ae]:n[c];if(!g(a))throw new Error(x(c));var s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=le(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t();
\ No newline at end of file
diff --git a/_proc/docs/site_libs/quarto-search/quarto-search.js b/_proc/docs/site_libs/quarto-search/quarto-search.js
new file mode 100644
index 0000000..d788a95
--- /dev/null
+++ b/_proc/docs/site_libs/quarto-search/quarto-search.js
@@ -0,0 +1,1290 @@
+const kQueryArg = "q";
+const kResultsArg = "show-results";
+
+// If items don't provide a URL, then both the navigator and the onSelect
+// function aren't called (and therefore, the default implementation is used)
+//
+// We're using this sentinel URL to signal to those handlers that this
+// item is a more item (along with the type) and can be handled appropriately
+const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05";
+
+window.document.addEventListener("DOMContentLoaded", function (_event) {
+  // Ensure that search is available on this page. If it isn't,
+  // should return early and not do anything
+  var searchEl = window.document.getElementById("quarto-search");
+  if (!searchEl) return;
+
+  const { autocomplete } = window["@algolia/autocomplete-js"];
+
+  let quartoSearchOptions = {};
+  let language = {};
+  const searchOptionEl = window.document.getElementById(
+    "quarto-search-options"
+  );
+  if (searchOptionEl) {
+    const jsonStr = searchOptionEl.textContent;
+    quartoSearchOptions = JSON.parse(jsonStr);
+    language = quartoSearchOptions.language;
+  }
+
+  // note the search mode
+  if (quartoSearchOptions.type === "overlay") {
+    searchEl.classList.add("type-overlay");
+  } else {
+    searchEl.classList.add("type-textbox");
+  }
+
+  // Used to determine highlighting behavior for this page
+  // A `q` query param is expected when the user follows a search
+  // to this page
+  const currentUrl = new URL(window.location);
+  const query = currentUrl.searchParams.get(kQueryArg);
+  const showSearchResults = currentUrl.searchParams.get(kResultsArg);
+  const mainEl = window.document.querySelector("main");
+
+  // highlight matches on the page
+  if (query && mainEl) {
+    // perform any highlighting
+    highlight(escapeRegExp(query), mainEl);
+
+    // fix up the URL to remove the q query param
+    const replacementUrl = new URL(window.location);
+    replacementUrl.searchParams.delete(kQueryArg);
+    window.history.replaceState({}, "", replacementUrl);
+  }
+
+  // function to clear highlighting on the page when the search query changes
+  // (e.g. if the user edits the query or clears it)
+  let highlighting = true;
+  const resetHighlighting = (searchTerm) => {
+    if (mainEl && highlighting && query && searchTerm !== query) {
+      clearHighlight(query, mainEl);
+      highlighting = false;
+    }
+  };
+
+  // Clear search highlighting when the user scrolls sufficiently
+  const resetFn = () => {
+    resetHighlighting("");
+    window.removeEventListener("quarto-hrChanged", resetFn);
+    window.removeEventListener("quarto-sectionChanged", resetFn);
+  };
+
+  // Register this event after the initial scrolling and settling of events
+  // on the page
+  window.addEventListener("quarto-hrChanged", resetFn);
+  window.addEventListener("quarto-sectionChanged", resetFn);
+
+  // Responsively switch to overlay mode if the search is present on the navbar
+  // Note that switching the sidebar to overlay mode requires more coordinate (not just
+  // the media query since we generate different HTML for sidebar overlays than we do
+  // for sidebar input UI)
+  const detachedMediaQuery =
+    quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)";
+
+  // If configured, include the analytics client to send insights
+  const plugins = configurePlugins(quartoSearchOptions);
+
+  let lastState = null;
+  const { setIsOpen, setQuery, setCollections } = autocomplete({
+    container: searchEl,
+    detachedMediaQuery: detachedMediaQuery,
+    defaultActiveItemId: 0,
+    panelContainer: "#quarto-search-results",
+    panelPlacement: quartoSearchOptions["panel-placement"],
+    debug: false,
+    openOnFocus: true,
+    plugins,
+    classNames: {
+      form: "d-flex",
+    },
+    placeholder: language["search-text-placeholder"],
+    translations: {
+      clearButtonTitle: language["search-clear-button-title"],
+      detachedCancelButtonText: language["search-detached-cancel-button-title"],
+      submitButtonTitle: language["search-submit-button-title"],
+    },
+    initialState: {
+      query,
+    },
+    getItemUrl({ item }) {
+      return item.href;
+    },
+    onStateChange({ state }) {
+      // If this is a file URL, note that
+
+      // Perhaps reset highlighting
+      resetHighlighting(state.query);
+
+      // If the panel just opened, ensure the panel is positioned properly
+      if (state.isOpen) {
+        if (lastState && !lastState.isOpen) {
+          setTimeout(() => {
+            positionPanel(quartoSearchOptions["panel-placement"]);
+          }, 150);
+        }
+      }
+
+      // Perhaps show the copy link
+      showCopyLink(state.query, quartoSearchOptions);
+
+      lastState = state;
+    },
+    reshape({ sources, state }) {
+      return sources.map((source) => {
+        try {
+          const items = source.getItems();
+
+          // Validate the items
+          validateItems(items);
+
+          // group the items by document
+          const groupedItems = new Map();
+          items.forEach((item) => {
+            const hrefParts = item.href.split("#");
+            const baseHref = hrefParts[0];
+            const isDocumentItem = hrefParts.length === 1;
+
+            const items = groupedItems.get(baseHref);
+            if (!items) {
+              groupedItems.set(baseHref, [item]);
+            } else {
+              // If the href for this item matches the document
+              // exactly, place this item first as it is the item that represents
+              // the document itself
+              if (isDocumentItem) {
+                items.unshift(item);
+              } else {
+                items.push(item);
+              }
+              groupedItems.set(baseHref, items);
+            }
+          });
+
+          const reshapedItems = [];
+          let count = 1;
+          for (const [_key, value] of groupedItems) {
+            const firstItem = value[0];
+            reshapedItems.push({
+              ...firstItem,
+              type: kItemTypeDoc,
+            });
+
+            const collapseMatches = quartoSearchOptions["collapse-after"];
+            const collapseCount =
+              typeof collapseMatches === "number" ? collapseMatches : 1;
+
+            if (value.length > 1) {
+              const target = `search-more-${count}`;
+              const isExpanded =
+                state.context.expanded &&
+                state.context.expanded.includes(target);
+
+              const remainingCount = value.length - collapseCount;
+
+              for (let i = 1; i < value.length; i++) {
+                if (collapseMatches && i === collapseCount) {
+                  reshapedItems.push({
+                    target,
+                    title: isExpanded
+                      ? language["search-hide-matches-text"]
+                      : remainingCount === 1
+                      ? `${remainingCount} ${language["search-more-match-text"]}`
+                      : `${remainingCount} ${language["search-more-matches-text"]}`,
+                    type: kItemTypeMore,
+                    href: kItemTypeMoreHref,
+                  });
+                }
+
+                if (isExpanded || !collapseMatches || i < collapseCount) {
+                  reshapedItems.push({
+                    ...value[i],
+                    type: kItemTypeItem,
+                    target,
+                  });
+                }
+              }
+            }
+            count += 1;
+          }
+
+          return {
+            ...source,
+            getItems() {
+              return reshapedItems;
+            },
+          };
+        } catch (error) {
+          // Some form of error occurred
+          return {
+            ...source,
+            getItems() {
+              return [
+                {
+                  title: error.name || "An Error Occurred While Searching",
+                  text:
+                    error.message ||
+                    "An unknown error occurred while attempting to perform the requested search.",
+                  type: kItemTypeError,
+                },
+              ];
+            },
+          };
+        }
+      });
+    },
+    navigator: {
+      navigate({ itemUrl }) {
+        if (itemUrl !== offsetURL(kItemTypeMoreHref)) {
+          window.location.assign(itemUrl);
+        }
+      },
+      navigateNewTab({ itemUrl }) {
+        if (itemUrl !== offsetURL(kItemTypeMoreHref)) {
+          const windowReference = window.open(itemUrl, "_blank", "noopener");
+          if (windowReference) {
+            windowReference.focus();
+          }
+        }
+      },
+      navigateNewWindow({ itemUrl }) {
+        if (itemUrl !== offsetURL(kItemTypeMoreHref)) {
+          window.open(itemUrl, "_blank", "noopener");
+        }
+      },
+    },
+    getSources({ state, setContext, setActiveItemId, refresh }) {
+      return [
+        {
+          sourceId: "documents",
+          getItemUrl({ item }) {
+            if (item.href) {
+              return offsetURL(item.href);
+            } else {
+              return undefined;
+            }
+          },
+          onSelect({
+            item,
+            state,
+            setContext,
+            setIsOpen,
+            setActiveItemId,
+            refresh,
+          }) {
+            if (item.type === kItemTypeMore) {
+              toggleExpanded(item, state, setContext, setActiveItemId, refresh);
+
+              // Toggle more
+              setIsOpen(true);
+            }
+          },
+          getItems({ query }) {
+            if (query === null || query === "") {
+              return [];
+            }
+
+            const limit = quartoSearchOptions.limit;
+            if (quartoSearchOptions.algolia) {
+              return algoliaSearch(query, limit, quartoSearchOptions.algolia);
+            } else {
+              // Fuse search options
+              const fuseSearchOptions = {
+                isCaseSensitive: false,
+                shouldSort: true,
+                minMatchCharLength: 2,
+                limit: limit,
+              };
+
+              return readSearchData().then(function (fuse) {
+                return fuseSearch(query, fuse, fuseSearchOptions);
+              });
+            }
+          },
+          templates: {
+            noResults({ createElement }) {
+              const hasQuery = lastState.query;
+
+              return createElement(
+                "div",
+                {
+                  class: `quarto-search-no-results${
+                    hasQuery ? "" : " no-query"
+                  }`,
+                },
+                language["search-no-results-text"]
+              );
+            },
+            header({ items, createElement }) {
+              // count the documents
+              const count = items.filter((item) => {
+                return item.type === kItemTypeDoc;
+              }).length;
+
+              if (count > 0) {
+                return createElement(
+                  "div",
+                  { class: "search-result-header" },
+                  `${count} ${language["search-matching-documents-text"]}`
+                );
+              } else {
+                return createElement(
+                  "div",
+                  { class: "search-result-header-no-results" },
+                  ``
+                );
+              }
+            },
+            footer({ _items, createElement }) {
+              if (
+                quartoSearchOptions.algolia &&
+                quartoSearchOptions.algolia["show-logo"]
+              ) {
+                const libDir = quartoSearchOptions.algolia["libDir"];
+                const logo = createElement("img", {
+                  src: offsetURL(
+                    `${libDir}/quarto-search/search-by-algolia.svg`
+                  ),
+                  class: "algolia-search-logo",
+                });
+                return createElement(
+                  "a",
+                  { href: "http://www.algolia.com/" },
+                  logo
+                );
+              }
+            },
+
+            item({ item, createElement }) {
+              return renderItem(
+                item,
+                createElement,
+                state,
+                setActiveItemId,
+                setContext,
+                refresh,
+                quartoSearchOptions
+              );
+            },
+          },
+        },
+      ];
+    },
+  });
+
+  window.quartoOpenSearch = () => {
+    setIsOpen(false);
+    setIsOpen(true);
+    focusSearchInput();
+  };
+
+  document.addEventListener("keyup", (event) => {
+    const { key } = event;
+    const kbds = quartoSearchOptions["keyboard-shortcut"];
+    const focusedEl = document.activeElement;
+
+    const isFormElFocused = [
+      "input",
+      "select",
+      "textarea",
+      "button",
+      "option",
+    ].find((tag) => {
+      return focusedEl.tagName.toLowerCase() === tag;
+    });
+
+    if (
+      kbds &&
+      kbds.includes(key) &&
+      !isFormElFocused &&
+      !document.activeElement.isContentEditable
+    ) {
+      event.preventDefault();
+      window.quartoOpenSearch();
+    }
+  });
+
+  // Remove the labeleledby attribute since it is pointing
+  // to a non-existent label
+  if (quartoSearchOptions.type === "overlay") {
+    const inputEl = window.document.querySelector(
+      "#quarto-search .aa-Autocomplete"
+    );
+    if (inputEl) {
+      inputEl.removeAttribute("aria-labelledby");
+    }
+  }
+
+  function throttle(func, wait) {
+    let waiting = false;
+    return function () {
+      if (!waiting) {
+        func.apply(this, arguments);
+        waiting = true;
+        setTimeout(function () {
+          waiting = false;
+        }, wait);
+      }
+    };
+  }
+
+  // If the main document scrolls dismiss the search results
+  // (otherwise, since they're floating in the document they can scroll with the document)
+  window.document.body.onscroll = throttle(() => {
+    // Only do this if we're not detached
+    // Bug #7117
+    // This will happen when the keyboard is shown on ios (resulting in a scroll)
+    // which then closed the search UI
+    if (!window.matchMedia(detachedMediaQuery).matches) {
+      setIsOpen(false);
+    }
+  }, 50);
+
+  if (showSearchResults) {
+    setIsOpen(true);
+    focusSearchInput();
+  }
+});
+
+function configurePlugins(quartoSearchOptions) {
+  const autocompletePlugins = [];
+  const algoliaOptions = quartoSearchOptions.algolia;
+  if (
+    algoliaOptions &&
+    algoliaOptions["analytics-events"] &&
+    algoliaOptions["search-only-api-key"] &&
+    algoliaOptions["application-id"]
+  ) {
+    const apiKey = algoliaOptions["search-only-api-key"];
+    const appId = algoliaOptions["application-id"];
+
+    // Aloglia insights may not be loaded because they require cookie consent
+    // Use deferred loading so events will start being recorded when/if consent
+    // is granted.
+    const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => {
+      if (
+        window.aa &&
+        window["@algolia/autocomplete-plugin-algolia-insights"]
+      ) {
+        window.aa("init", {
+          appId,
+          apiKey,
+          useCookie: true,
+        });
+
+        const { createAlgoliaInsightsPlugin } =
+          window["@algolia/autocomplete-plugin-algolia-insights"];
+        // Register the insights client
+        const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({
+          insightsClient: window.aa,
+          onItemsChange({ insights, insightsEvents }) {
+            const events = insightsEvents.flatMap((event) => {
+              // This API limits the number of items per event to 20
+              const chunkSize = 20;
+              const itemChunks = [];
+              const eventItems = event.items;
+              for (let i = 0; i < eventItems.length; i += chunkSize) {
+                itemChunks.push(eventItems.slice(i, i + chunkSize));
+              }
+              // Split the items into multiple events that can be sent
+              const events = itemChunks.map((items) => {
+                return {
+                  ...event,
+                  items,
+                };
+              });
+              return events;
+            });
+
+            for (const event of events) {
+              insights.viewedObjectIDs(event);
+            }
+          },
+        });
+        return algoliaInsightsPlugin;
+      }
+    });
+
+    // Add the plugin
+    autocompletePlugins.push(algoliaInsightsDeferredPlugin);
+    return autocompletePlugins;
+  }
+}
+
+// For plugins that may not load immediately, create a wrapper
+// plugin and forward events and plugin data once the plugin
+// is initialized. This is useful for cases like cookie consent
+// which may prevent the analytics insights event plugin from initializing
+// immediately.
+function deferredLoadPlugin(createPlugin) {
+  let plugin = undefined;
+  let subscribeObj = undefined;
+  const wrappedPlugin = () => {
+    if (!plugin && subscribeObj) {
+      plugin = createPlugin();
+      if (plugin && plugin.subscribe) {
+        plugin.subscribe(subscribeObj);
+      }
+    }
+    return plugin;
+  };
+
+  return {
+    subscribe: (obj) => {
+      subscribeObj = obj;
+    },
+    onStateChange: (obj) => {
+      const plugin = wrappedPlugin();
+      if (plugin && plugin.onStateChange) {
+        plugin.onStateChange(obj);
+      }
+    },
+    onSubmit: (obj) => {
+      const plugin = wrappedPlugin();
+      if (plugin && plugin.onSubmit) {
+        plugin.onSubmit(obj);
+      }
+    },
+    onReset: (obj) => {
+      const plugin = wrappedPlugin();
+      if (plugin && plugin.onReset) {
+        plugin.onReset(obj);
+      }
+    },
+    getSources: (obj) => {
+      const plugin = wrappedPlugin();
+      if (plugin && plugin.getSources) {
+        return plugin.getSources(obj);
+      } else {
+        return Promise.resolve([]);
+      }
+    },
+    data: (obj) => {
+      const plugin = wrappedPlugin();
+      if (plugin && plugin.data) {
+        plugin.data(obj);
+      }
+    },
+  };
+}
+
+function validateItems(items) {
+  // Validate the first item
+  if (items.length > 0) {
+    const item = items[0];
+    const missingFields = [];
+    if (item.href == undefined) {
+      missingFields.push("href");
+    }
+    if (!item.title == undefined) {
+      missingFields.push("title");
+    }
+    if (!item.text == undefined) {
+      missingFields.push("text");
+    }
+
+    if (missingFields.length === 1) {
+      throw {
+        name: `Error: Search index is missing the ${missingFields[0]} field.`,
+        message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`,
+      };
+    } else if (missingFields.length > 1) {
+      const missingFieldList = missingFields
+        .map((field) => {
+          return `${field}`;
+        })
+        .join(", ");
+
+      throw {
+        name: `Error: Search index is missing the following fields: ${missingFieldList}.`,
+        message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`,
+      };
+    }
+  }
+}
+
+let lastQuery = null;
+function showCopyLink(query, options) {
+  const language = options.language;
+  lastQuery = query;
+  // Insert share icon
+  const inputSuffixEl = window.document.body.querySelector(
+    ".aa-Form .aa-InputWrapperSuffix"
+  );
+
+  if (inputSuffixEl) {
+    let copyButtonEl = window.document.body.querySelector(
+      ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton"
+    );
+
+    if (copyButtonEl === null) {
+      copyButtonEl = window.document.createElement("button");
+      copyButtonEl.setAttribute("class", "aa-CopyButton");
+      copyButtonEl.setAttribute("type", "button");
+      copyButtonEl.setAttribute("title", language["search-copy-link-title"]);
+      copyButtonEl.onmousedown = (e) => {
+        e.preventDefault();
+        e.stopPropagation();
+      };
+
+      const linkIcon = "bi-clipboard";
+      const checkIcon = "bi-check2";
+
+      const shareIconEl = window.document.createElement("i");
+      shareIconEl.setAttribute("class", `bi ${linkIcon}`);
+      copyButtonEl.appendChild(shareIconEl);
+      inputSuffixEl.prepend(copyButtonEl);
+
+      const clipboard = new window.ClipboardJS(".aa-CopyButton", {
+        text: function (_trigger) {
+          const copyUrl = new URL(window.location);
+          copyUrl.searchParams.set(kQueryArg, lastQuery);
+          copyUrl.searchParams.set(kResultsArg, "1");
+          return copyUrl.toString();
+        },
+      });
+      clipboard.on("success", function (e) {
+        // Focus the input
+
+        // button target
+        const button = e.trigger;
+        const icon = button.querySelector("i.bi");
+
+        // flash "checked"
+        icon.classList.add(checkIcon);
+        icon.classList.remove(linkIcon);
+        setTimeout(function () {
+          icon.classList.remove(checkIcon);
+          icon.classList.add(linkIcon);
+        }, 1000);
+      });
+    }
+
+    // If there is a query, show the link icon
+    if (copyButtonEl) {
+      if (lastQuery && options["copy-button"]) {
+        copyButtonEl.style.display = "flex";
+      } else {
+        copyButtonEl.style.display = "none";
+      }
+    }
+  }
+}
+
+/* Search Index Handling */
+// create the index
+var fuseIndex = undefined;
+var shownWarning = false;
+
+// fuse index options
+const kFuseIndexOptions = {
+  keys: [
+    { name: "title", weight: 20 },
+    { name: "section", weight: 20 },
+    { name: "text", weight: 10 },
+  ],
+  ignoreLocation: true,
+  threshold: 0.1,
+};
+
+async function readSearchData() {
+  // Initialize the search index on demand
+  if (fuseIndex === undefined) {
+    if (window.location.protocol === "file:" && !shownWarning) {
+      window.alert(
+        "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server."
+      );
+      shownWarning = true;
+      return;
+    }
+    const fuse = new window.Fuse([], kFuseIndexOptions);
+
+    // fetch the main search.json
+    const response = await fetch(offsetURL("search.json"));
+    if (response.status == 200) {
+      return response.json().then(function (searchDocs) {
+        searchDocs.forEach(function (searchDoc) {
+          fuse.add(searchDoc);
+        });
+        fuseIndex = fuse;
+        return fuseIndex;
+      });
+    } else {
+      return Promise.reject(
+        new Error(
+          "Unexpected status from search index request: " + response.status
+        )
+      );
+    }
+  }
+
+  return fuseIndex;
+}
+
+function inputElement() {
+  return window.document.body.querySelector(".aa-Form .aa-Input");
+}
+
+function focusSearchInput() {
+  setTimeout(() => {
+    const inputEl = inputElement();
+    if (inputEl) {
+      inputEl.focus();
+    }
+  }, 50);
+}
+
+/* Panels */
+const kItemTypeDoc = "document";
+const kItemTypeMore = "document-more";
+const kItemTypeItem = "document-item";
+const kItemTypeError = "error";
+
+function renderItem(
+  item,
+  createElement,
+  state,
+  setActiveItemId,
+  setContext,
+  refresh,
+  quartoSearchOptions
+) {
+  switch (item.type) {
+    case kItemTypeDoc:
+      return createDocumentCard(
+        createElement,
+        "file-richtext",
+        item.title,
+        item.section,
+        item.text,
+        item.href,
+        item.crumbs,
+        quartoSearchOptions
+      );
+    case kItemTypeMore:
+      return createMoreCard(
+        createElement,
+        item,
+        state,
+        setActiveItemId,
+        setContext,
+        refresh
+      );
+    case kItemTypeItem:
+      return createSectionCard(
+        createElement,
+        item.section,
+        item.text,
+        item.href
+      );
+    case kItemTypeError:
+      return createErrorCard(createElement, item.title, item.text);
+    default:
+      return undefined;
+  }
+}
+
+function createDocumentCard(
+  createElement,
+  icon,
+  title,
+  section,
+  text,
+  href,
+  crumbs,
+  quartoSearchOptions
+) {
+  const iconEl = createElement("i", {
+    class: `bi bi-${icon} search-result-icon`,
+  });
+  const titleEl = createElement("p", { class: "search-result-title" }, title);
+  const titleContents = [iconEl, titleEl];
+  const showParent = quartoSearchOptions["show-item-context"];
+  if (crumbs && showParent) {
+    let crumbsOut = undefined;
+    const crumbClz = ["search-result-crumbs"];
+    if (showParent === "root") {
+      crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined;
+    } else if (showParent === "parent") {
+      crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined;
+    } else {
+      crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined;
+      crumbClz.push("search-result-crumbs-wrap");
+    }
+
+    const crumbEl = createElement(
+      "p",
+      { class: crumbClz.join(" ") },
+      crumbsOut
+    );
+    titleContents.push(crumbEl);
+  }
+
+  const titleContainerEl = createElement(
+    "div",
+    { class: "search-result-title-container" },
+    titleContents
+  );
+
+  const textEls = [];
+  if (section) {
+    const sectionEl = createElement(
+      "p",
+      { class: "search-result-section" },
+      section
+    );
+    textEls.push(sectionEl);
+  }
+  const descEl = createElement("p", {
+    class: "search-result-text",
+    dangerouslySetInnerHTML: {
+      __html: text,
+    },
+  });
+  textEls.push(descEl);
+
+  const textContainerEl = createElement(
+    "div",
+    { class: "search-result-text-container" },
+    textEls
+  );
+
+  const containerEl = createElement(
+    "div",
+    {
+      class: "search-result-container",
+    },
+    [titleContainerEl, textContainerEl]
+  );
+
+  const linkEl = createElement(
+    "a",
+    {
+      href: offsetURL(href),
+      class: "search-result-link",
+    },
+    containerEl
+  );
+
+  const classes = ["search-result-doc", "search-item"];
+  if (!section) {
+    classes.push("document-selectable");
+  }
+
+  return createElement(
+    "div",
+    {
+      class: classes.join(" "),
+    },
+    linkEl
+  );
+}
+
+function createMoreCard(
+  createElement,
+  item,
+  state,
+  setActiveItemId,
+  setContext,
+  refresh
+) {
+  const moreCardEl = createElement(
+    "div",
+    {
+      class: "search-result-more search-item",
+      onClick: (e) => {
+        // Handle expanding the sections by adding the expanded
+        // section to the list of expanded sections
+        toggleExpanded(item, state, setContext, setActiveItemId, refresh);
+        e.stopPropagation();
+      },
+    },
+    item.title
+  );
+
+  return moreCardEl;
+}
+
+function toggleExpanded(item, state, setContext, setActiveItemId, refresh) {
+  const expanded = state.context.expanded || [];
+  if (expanded.includes(item.target)) {
+    setContext({
+      expanded: expanded.filter((target) => target !== item.target),
+    });
+  } else {
+    setContext({ expanded: [...expanded, item.target] });
+  }
+
+  refresh();
+  setActiveItemId(item.__autocomplete_id);
+}
+
+function createSectionCard(createElement, section, text, href) {
+  const sectionEl = createSection(createElement, section, text, href);
+  return createElement(
+    "div",
+    {
+      class: "search-result-doc-section search-item",
+    },
+    sectionEl
+  );
+}
+
+function createSection(createElement, title, text, href) {
+  const descEl = createElement("p", {
+    class: "search-result-text",
+    dangerouslySetInnerHTML: {
+      __html: text,
+    },
+  });
+
+  const titleEl = createElement("p", { class: "search-result-section" }, title);
+  const linkEl = createElement(
+    "a",
+    {
+      href: offsetURL(href),
+      class: "search-result-link",
+    },
+    [titleEl, descEl]
+  );
+  return linkEl;
+}
+
+function createErrorCard(createElement, title, text) {
+  const descEl = createElement("p", {
+    class: "search-error-text",
+    dangerouslySetInnerHTML: {
+      __html: text,
+    },
+  });
+
+  const titleEl = createElement("p", {
+    class: "search-error-title",
+    dangerouslySetInnerHTML: {
+      __html: ` ${title}`,
+    },
+  });
+  const errorEl = createElement("div", { class: "search-error" }, [
+    titleEl,
+    descEl,
+  ]);
+  return errorEl;
+}
+
+function positionPanel(pos) {
+  const panelEl = window.document.querySelector(
+    "#quarto-search-results .aa-Panel"
+  );
+  const inputEl = window.document.querySelector(
+    "#quarto-search .aa-Autocomplete"
+  );
+
+  if (panelEl && inputEl) {
+    panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`;
+    if (pos === "start") {
+      panelEl.style.left = `${Math.round(inputEl.left)}px`;
+    } else {
+      panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`;
+    }
+  }
+}
+
+/* Highlighting */
+// highlighting functions
+function highlightMatch(query, text) {
+  if (text) {
+    const start = text.toLowerCase().indexOf(query.toLowerCase());
+    if (start !== -1) {
+      const startMark = "";
+      const endMark = "";
+
+      const end = start + query.length;
+      text =
+        text.slice(0, start) +
+        startMark +
+        text.slice(start, end) +
+        endMark +
+        text.slice(end);
+      const startInfo = clipStart(text, start);
+      const endInfo = clipEnd(
+        text,
+        startInfo.position + startMark.length + endMark.length
+      );
+      text =
+        startInfo.prefix +
+        text.slice(startInfo.position, endInfo.position) +
+        endInfo.suffix;
+
+      return text;
+    } else {
+      return text;
+    }
+  } else {
+    return text;
+  }
+}
+
+function clipStart(text, pos) {
+  const clipStart = pos - 50;
+  if (clipStart < 0) {
+    // This will just return the start of the string
+    return {
+      position: 0,
+      prefix: "",
+    };
+  } else {
+    // We're clipping before the start of the string, walk backwards to the first space.
+    const spacePos = findSpace(text, pos, -1);
+    return {
+      position: spacePos.position,
+      prefix: "",
+    };
+  }
+}
+
+function clipEnd(text, pos) {
+  const clipEnd = pos + 200;
+  if (clipEnd > text.length) {
+    return {
+      position: text.length,
+      suffix: "",
+    };
+  } else {
+    const spacePos = findSpace(text, clipEnd, 1);
+    return {
+      position: spacePos.position,
+      suffix: spacePos.clipped ? "…" : "",
+    };
+  }
+}
+
+function findSpace(text, start, step) {
+  let stepPos = start;
+  while (stepPos > -1 && stepPos < text.length) {
+    const char = text[stepPos];
+    if (char === " " || char === "," || char === ":") {
+      return {
+        position: step === 1 ? stepPos : stepPos - step,
+        clipped: stepPos > 1 && stepPos < text.length,
+      };
+    }
+    stepPos = stepPos + step;
+  }
+
+  return {
+    position: stepPos - step,
+    clipped: false,
+  };
+}
+
+// removes highlighting as implemented by the mark tag
+function clearHighlight(searchterm, el) {
+  const childNodes = el.childNodes;
+  for (let i = childNodes.length - 1; i >= 0; i--) {
+    const node = childNodes[i];
+    if (node.nodeType === Node.ELEMENT_NODE) {
+      if (
+        node.tagName === "MARK" &&
+        node.innerText.toLowerCase() === searchterm.toLowerCase()
+      ) {
+        el.replaceChild(document.createTextNode(node.innerText), node);
+      } else {
+        clearHighlight(searchterm, node);
+      }
+    }
+  }
+}
+
+function escapeRegExp(string) {
+  return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+}
+
+// highlight matches
+function highlight(term, el) {
+  const termRegex = new RegExp(term, "ig");
+  const childNodes = el.childNodes;
+
+  // walk back to front avoid mutating elements in front of us
+  for (let i = childNodes.length - 1; i >= 0; i--) {
+    const node = childNodes[i];
+
+    if (node.nodeType === Node.TEXT_NODE) {
+      // Search text nodes for text to highlight
+      const text = node.nodeValue;
+
+      let startIndex = 0;
+      let matchIndex = text.search(termRegex);
+      if (matchIndex > -1) {
+        const markFragment = document.createDocumentFragment();
+        while (matchIndex > -1) {
+          const prefix = text.slice(startIndex, matchIndex);
+          markFragment.appendChild(document.createTextNode(prefix));
+
+          const mark = document.createElement("mark");
+          mark.appendChild(
+            document.createTextNode(
+              text.slice(matchIndex, matchIndex + term.length)
+            )
+          );
+          markFragment.appendChild(mark);
+
+          startIndex = matchIndex + term.length;
+          matchIndex = text.slice(startIndex).search(new RegExp(term, "ig"));
+          if (matchIndex > -1) {
+            matchIndex = startIndex + matchIndex;
+          }
+        }
+        if (startIndex < text.length) {
+          markFragment.appendChild(
+            document.createTextNode(text.slice(startIndex, text.length))
+          );
+        }
+
+        el.replaceChild(markFragment, node);
+      }
+    } else if (node.nodeType === Node.ELEMENT_NODE) {
+      // recurse through elements
+      highlight(term, node);
+    }
+  }
+}
+
+/* Link Handling */
+// get the offset from this page for a given site root relative url
+function offsetURL(url) {
+  var offset = getMeta("quarto:offset");
+  return offset ? offset + url : url;
+}
+
+// read a meta tag value
+function getMeta(metaName) {
+  var metas = window.document.getElementsByTagName("meta");
+  for (let i = 0; i < metas.length; i++) {
+    if (metas[i].getAttribute("name") === metaName) {
+      return metas[i].getAttribute("content");
+    }
+  }
+  return "";
+}
+
+function algoliaSearch(query, limit, algoliaOptions) {
+  const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"];
+
+  const applicationId = algoliaOptions["application-id"];
+  const searchOnlyApiKey = algoliaOptions["search-only-api-key"];
+  const indexName = algoliaOptions["index-name"];
+  const indexFields = algoliaOptions["index-fields"];
+  const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey);
+  const searchParams = algoliaOptions["params"];
+  const searchAnalytics = !!algoliaOptions["analytics-events"];
+
+  return getAlgoliaResults({
+    searchClient,
+    queries: [
+      {
+        indexName: indexName,
+        query,
+        params: {
+          hitsPerPage: limit,
+          clickAnalytics: searchAnalytics,
+          ...searchParams,
+        },
+      },
+    ],
+    transformResponse: (response) => {
+      if (!indexFields) {
+        return response.hits.map((hit) => {
+          return hit.map((item) => {
+            return {
+              ...item,
+              text: highlightMatch(query, item.text),
+            };
+          });
+        });
+      } else {
+        const remappedHits = response.hits.map((hit) => {
+          return hit.map((item) => {
+            const newItem = { ...item };
+            ["href", "section", "title", "text", "crumbs"].forEach(
+              (keyName) => {
+                const mappedName = indexFields[keyName];
+                if (
+                  mappedName &&
+                  item[mappedName] !== undefined &&
+                  mappedName !== keyName
+                ) {
+                  newItem[keyName] = item[mappedName];
+                  delete newItem[mappedName];
+                }
+              }
+            );
+            newItem.text = highlightMatch(query, newItem.text);
+            return newItem;
+          });
+        });
+        return remappedHits;
+      }
+    },
+  });
+}
+
+let subSearchTerm = undefined;
+let subSearchFuse = undefined;
+const kFuseMaxWait = 125;
+
+async function fuseSearch(query, fuse, fuseOptions) {
+  let index = fuse;
+  // Fuse.js using the Bitap algorithm for text matching which runs in
+  // O(nm) time (no matter the structure of the text). In our case this
+  // means that long search terms mixed with large index gets very slow
+  //
+  // This injects a subIndex that will be used once the terms get long enough
+  // Usually making this subindex is cheap since there will typically be
+  // a subset of results matching the existing query
+  if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) {
+    // Use the existing subSearchFuse
+    index = subSearchFuse;
+  } else if (subSearchFuse !== undefined) {
+    // The term changed, discard the existing fuse
+    subSearchFuse = undefined;
+    subSearchTerm = undefined;
+  }
+
+  // Search using the active fuse
+  const then = performance.now();
+  const resultsRaw = await index.search(query, fuseOptions);
+  const now = performance.now();
+
+  const results = resultsRaw.map((result) => {
+    const addParam = (url, name, value) => {
+      const anchorParts = url.split("#");
+      const baseUrl = anchorParts[0];
+      const sep = baseUrl.search("\\?") > 0 ? "&" : "?";
+      anchorParts[0] = baseUrl + sep + name + "=" + value;
+      return anchorParts.join("#");
+    };
+
+    return {
+      title: result.item.title,
+      section: result.item.section,
+      href: addParam(result.item.href, kQueryArg, query),
+      text: highlightMatch(query, result.item.text),
+      crumbs: result.item.crumbs,
+    };
+  });
+
+  // If we don't have a subfuse and the query is long enough, go ahead
+  // and create a subfuse to use for subsequent queries
+  if (
+    now - then > kFuseMaxWait &&
+    subSearchFuse === undefined &&
+    resultsRaw.length < fuseOptions.limit
+  ) {
+    subSearchTerm = query;
+    subSearchFuse = new window.Fuse([], kFuseIndexOptions);
+    resultsRaw.forEach((rr) => {
+      subSearchFuse.add(rr.item);
+    });
+  }
+  return results;
+}
diff --git a/_proc/docs/sitemap.xml b/_proc/docs/sitemap.xml
new file mode 100644
index 0000000..d117446
--- /dev/null
+++ b/_proc/docs/sitemap.xml
@@ -0,0 +1,95 @@
+
+
+  
+    https://sizhky.github.io/torch_snippets/imgaug_loader.html
+    2024-08-24T07:20:35.662Z
+  
+  
+    https://sizhky.github.io/torch_snippets/bokeh_plotting.html
+    2024-08-24T07:20:35.921Z
+  
+  
+    https://sizhky.github.io/torch_snippets/logging.html
+    2024-08-24T07:25:44.344Z
+  
+  
+    https://sizhky.github.io/torch_snippets/report.html
+    2024-08-24T07:20:35.524Z
+  
+  
+    https://sizhky.github.io/torch_snippets/registry.html
+    2024-08-24T07:20:35.460Z
+  
+  
+    https://sizhky.github.io/torch_snippets/config.html
+    2024-08-24T07:20:35.977Z
+  
+  
+    https://sizhky.github.io/torch_snippets/paths.html
+    2024-08-24T07:20:35.590Z
+  
+  
+    https://sizhky.github.io/torch_snippets/pdf.html
+    2024-08-24T07:20:35.498Z
+  
+  
+    https://sizhky.github.io/torch_snippets/bounding_boxes.html
+    2024-08-24T07:20:35.586Z
+  
+  
+    https://sizhky.github.io/torch_snippets/load_defautls.html
+    2024-08-24T07:20:35.498Z
+  
+  
+    https://sizhky.github.io/torch_snippets/AttrDict.html
+    2024-08-24T07:20:35.038Z
+  
+  
+    https://sizhky.github.io/torch_snippets/decorators.html
+    2024-08-24T07:20:36.418Z
+  
+  
+    https://sizhky.github.io/torch_snippets/markups.html
+    2024-08-24T07:20:35.428Z
+  
+  
+    https://sizhky.github.io/torch_snippets/interactive_show.html
+    2024-08-24T07:20:35.854Z
+  
+  
+    https://sizhky.github.io/torch_snippets/sklegos.html
+    2024-08-24T07:20:35.980Z
+  
+  
+    https://sizhky.github.io/torch_snippets/show.html
+    2024-08-24T07:20:35.235Z
+  
+  
+    https://sizhky.github.io/torch_snippets/charts.html
+    2024-08-24T07:20:36.311Z
+  
+  
+    https://sizhky.github.io/torch_snippets/jupyter_notebook.html
+    2024-08-24T07:20:35.579Z
+  
+  
+    https://sizhky.github.io/torch_snippets/misc.html
+    2024-08-24T07:20:35.806Z
+  
+  
+    https://sizhky.github.io/torch_snippets/adapters.html
+    2024-08-24T07:20:35.573Z
+  
+  
+    https://sizhky.github.io/torch_snippets/capsule.html
+    2024-08-24T07:20:36.534Z
+  
+  
+    https://sizhky.github.io/torch_snippets/inspector.html
+    2024-08-24T07:20:35.598Z
+  
+  
+    https://sizhky.github.io/torch_snippets/index.html
+    2024-08-24T07:25:06.450Z
+  
+
diff --git a/_proc/docs/sklegos.html b/_proc/docs/sklegos.html
new file mode 100644
index 0000000..72a6268
--- /dev/null
+++ b/_proc/docs/sklegos.html
@@ -0,0 +1,846 @@
+
+
+
+
+
+
+
+
+
+sklegos – torch_snippets
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

+  

+    
+  
+

+
+

+
+  
+

+
+    
+
+

+
+
+
+
+

+
SK-Legos

+
Utilities to do common ML tasks

+
+
You can find 1. train_test_split which also resets the dataframes’ indexes 2. MakeFrame 3. ImputeMisingValues 4. Cat2Num 5. Other scikit-lego blocks that I use a lot

+

+

+
MakeFrame

+

+
 MakeFrame (column_names)

+

+
Convert sklearn’s output to a pandas dataframe Especially useful when working with an ensemble of models

+
Usage

+
Call MakeFrame as the last component in your pipeline with the desired column names.

+
pipeline = Pipeline([
+    ...,
+    ('output', MakeFrame(['outlier', 'class'])),
+])

+
+

+

+

+
ImputeMissingValues

+

+
 ImputeMissingValues (num_mode=<function mean at 0x10695b6f0>,
+                      cat_mode='MISSING')

+

+
*DataFrame input - DataFrame output During fit - 1. Store imputable value for each column During transform - 2. Impute missing values with imputable value 3. Create a ’{col}_na’ boolean column to tell if cells contained missing value*

+

+

+

+
LambdaTransformer

+

+
 LambdaTransformer (fn)

+

+
*Base class for all estimators in scikit-learn.

+
Inheriting from this class provides default implementations of:

+
    
+
  • setting and getting parameters used by GridSearchCV and friends;
    • 
+
  • textual and HTML representation displayed in terminals and IDEs;
    • 
+
  • estimator serialization;
    • 
+
  • parameters validation;
    • 
+
  • data validation;
    • 
+
  • feature names validation.
    • 
+

+
Read more in the :ref:User Guide <rolling_your_own_estimator>.*

+

+

+

+
MakeFrame

+

+
 MakeFrame (column_names)

+

+
*Base class for all estimators in scikit-learn.

+
Inheriting from this class provides default implementations of:

+
    
+
  • setting and getting parameters used by GridSearchCV and friends;
    • 
+
  • textual and HTML representation displayed in terminals and IDEs;
    • 
+
  • estimator serialization;
    • 
+
  • parameters validation;
    • 
+
  • data validation;
    • 
+
  • feature names validation.
    • 
+

+
Read more in the :ref:User Guide <rolling_your_own_estimator>.*

+

+

+

+
Cat2Num

+

+
 Cat2Num ()

+

+
*Base class for all estimators in scikit-learn.

+
Inheriting from this class provides default implementations of:

+
    
+
  • setting and getting parameters used by GridSearchCV and friends;
    • 
+
  • textual and HTML representation displayed in terminals and IDEs;
    • 
+
  • estimator serialization;
    • 
+
  • parameters validation;
    • 
+
  • data validation;
    • 
+
  • feature names validation.
    • 
+

+
Read more in the :ref:User Guide <rolling_your_own_estimator>.*

+

+

+

+
SplitDateColumn

+

+
 SplitDateColumn (column_names, has_date, has_time, date_format=None)

+

+
*Base class for all estimators in scikit-learn.

+
Inheriting from this class provides default implementations of:

+
    
+
  • setting and getting parameters used by GridSearchCV and friends;
    • 
+
  • textual and HTML representation displayed in terminals and IDEs;
    • 
+
  • estimator serialization;
    • 
+
  • parameters validation;
    • 
+
  • data validation;
    • 
+
  • feature names validation.
    • 
+

+
Read more in the :ref:User Guide <rolling_your_own_estimator>.*

+
+
+

+

+
+
 
+
+
 
+
+
+
+
+
\ No newline at end of file
diff --git a/_proc/index.ipynb b/_proc/index.ipynb
index de32754..e395072 100644
--- a/_proc/index.ipynb
+++ b/_proc/index.ipynb
@@ -19,15 +19,6 @@
     ""
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "language": "python"
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -39,8 +30,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 304 ms, sys: 65.3 ms, total: 369 ms\n",
-      "Wall time: 469 ms\n"
+      "CPU times: user 263 ms, sys: 53.9 ms, total: 317 ms\n",
+      "Wall time: 407 ms\n"
      ]
     }
    ],
@@ -63,6 +54,76 @@
    "metadata": {
     "language": "python"
    },
+   "outputs": [],
+   "source": [
+    "import ast\n",
+    "\n",
+    "os.environ[\n",
+    "    \"AD_MAX_ITEMS\"\n",
+    "] = (  # os is already imported by torch_snippets, along with many other useful libraries\n",
+    "    \"1000\"  # Set the maximum number of items to display in the AD object\n",
+    ")\n",
+    "\n",
+    "\n",
+    "@tryy  # This is a decorator that catches exceptions\n",
+    "def extract_all_list(file_path):\n",
+    "    file = readfile(file_path, silent=True)  # Read the file\n",
+    "    tree = ast.parse(file, filename=file_path)\n",
+    "\n",
+    "    for node in tree.body:\n",
+    "        if isinstance(node, ast.Assign):\n",
+    "            for target in node.targets:\n",
+    "                if isinstance(target, ast.Name) and target.id == \"__all__\":\n",
+    "                    if isinstance(node.value, ast.List):\n",
+    "                        all_list = [\n",
+    "                            elt.value\n",
+    "                            for elt in node.value.elts\n",
+    "                            if isinstance(elt, ast.Constant)\n",
+    "                        ]\n",
+    "                        return all_list\n",
+    "    return None\n",
+    "\n",
+    "\n",
+    "def print_all_lists_in_directory(directory):\n",
+    "    dir = P(directory)  # Create a pathlib.Path object\n",
+    "    for f in dir.ls():  # Iterate over all files in the directory\n",
+    "        if f.extn == \"py\" and f.stem not in [\n",
+    "            \"__init__\",\n",
+    "            \"_nbdev\",\n",
+    "        ]:  # If it's a Python file and not __init__.py\n",
+    "            all_list = extract_all_list(f)\n",
+    "            if all_list is not None and len(all_list) > 0:\n",
+    "                h2(f.stem)  # Print the name of the file as a heading in jupyter\n",
+    "                print(\n",
+    "                    AD({\"items\": all_list})\n",
+    "                )  # AD is an intelligent dictionary that can display itself nicely"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/yeshwanth/Code/Personal/torch_snippets/nbs\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(P().resolve())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "language": "python"
+   },
    "outputs": [
     {
      "data": {
@@ -464,26 +525,28 @@
       "  0 - console (🏷️ str)\n",
       "  1 - reset_logger_width (🏷️ str)\n",
       "  2 - logger (🏷️ str)\n",
-      "  3 - Debug (🏷️ str)\n",
-      "  4 - Info (🏷️ str)\n",
-      "  5 - Warn (🏷️ str)\n",
-      "  6 - Excep (🏷️ str)\n",
-      "  7 - warn_mode (🏷️ str)\n",
-      "  8 - info_mode (🏷️ str)\n",
-      "  9 - debug_mode (🏷️ str)\n",
-      "  10 - excep_mode (🏷️ str)\n",
-      "  11 - in_warn_mode (🏷️ str)\n",
-      "  12 - in_info_mode (🏷️ str)\n",
-      "  13 - in_debug_mode (🏷️ str)\n",
-      "  14 - in_excep_mode (🏷️ str)\n",
-      "  15 - frames (🏷️ str)\n",
-      "  16 - get_console (🏷️ str)\n",
-      "  17 - reset_logger (🏷️ str)\n",
-      "  18 - enter_exit (🏷️ str)\n",
-      "  19 - get_logger_level (🏷️ str)\n",
-      "  20 - logger_mode (🏷️ str)\n",
-      "  21 - in_logger_mode (🏷️ str)\n",
-      "  22 - notify_waiting (🏷️ str)\n",
+      "  3 - Trace (🏷️ str)\n",
+      "  4 - Debug (🏷️ str)\n",
+      "  5 - Info (🏷️ str)\n",
+      "  6 - Warn (🏷️ str)\n",
+      "  7 - Excep (🏷️ str)\n",
+      "  8 - warn_mode (🏷️ str)\n",
+      "  9 - info_mode (🏷️ str)\n",
+      "  10 - debug_mode (🏷️ str)\n",
+      "  11 - trace_mode (🏷️ str)\n",
+      "  12 - excep_mode (🏷️ str)\n",
+      "  13 - in_warn_mode (🏷️ str)\n",
+      "  14 - in_info_mode (🏷️ str)\n",
+      "  15 - in_debug_mode (🏷️ str)\n",
+      "  16 - in_trace_mode (🏷️ str)\n",
+      "  17 - in_excep_mode (🏷️ str)\n",
+      "  18 - frames (🏷️ str)\n",
+      "  19 - get_console (🏷️ str)\n",
+      "  20 - reset_logger (🏷️ str)\n",
+      "  21 - get_logger_level (🏷️ str)\n",
+      "  22 - logger_mode (🏷️ str)\n",
+      "  23 - in_logger_mode (🏷️ str)\n",
+      "  24 - notify_waiting (🏷️ str)\n",
       "\n",
       "```\n",
       "\n"
@@ -903,58 +966,24 @@
     }
    ],
    "source": [
-    "import ast\n",
-    "\n",
-    "os.environ[\n",
-    "    \"AD_MAX_ITEMS\"\n",
-    "] = (  # os is already imported by torch_snippets, along with many other useful libraries\n",
-    "    \"1000\"  # Set the maximum number of items to display in the AD object\n",
-    ")\n",
-    "\n",
-    "\n",
-    "@tryy  # This is a decorator that catches exceptions\n",
-    "def extract_all_list(file_path):\n",
-    "    file = readfile(file_path, silent=True)  # Read the file\n",
-    "    tree = ast.parse(file, filename=file_path)\n",
-    "\n",
-    "    for node in tree.body:\n",
-    "        if isinstance(node, ast.Assign):\n",
-    "            for target in node.targets:\n",
-    "                if isinstance(target, ast.Name) and target.id == \"__all__\":\n",
-    "                    if isinstance(node.value, ast.List):\n",
-    "                        all_list = [\n",
-    "                            elt.value\n",
-    "                            for elt in node.value.elts\n",
-    "                            if isinstance(elt, ast.Constant)\n",
-    "                        ]\n",
-    "                        return all_list\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "def print_all_lists_in_directory(directory):\n",
-    "    dir = P(directory)  # Create a pathlib.Path object\n",
-    "    for f in dir.ls():  # Iterate over all files in the directory\n",
-    "        if f.extn == \"py\" and f.stem not in [\n",
-    "            \"__init__\",\n",
-    "            \"_nbdev\",\n",
-    "        ]:  # If it's a Python file and not __init__.py\n",
-    "            all_list = extract_all_list(f)\n",
-    "            if all_list is not None and len(all_list) > 0:\n",
-    "                h2(f.stem)  # Print the name of the file as a heading in jupyter\n",
-    "                print(\n",
-    "                    AD({\"items\": all_list})\n",
-    "                )  # AD is an intelligent dictionary that can display itself nicely\n",
-    "\n",
-    "\n",
     "# Specify the directory containing the Python files\n",
     "directory_path = \"../torch_snippets\"\n",
     "print_all_lists_in_directory(directory_path)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/_proc/logging.ipynb b/_proc/logging.ipynb
new file mode 100644
index 0000000..adf38b0
--- /dev/null
+++ b/_proc/logging.ipynb
@@ -0,0 +1,1154 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "output-file: logging.html\n",
+    "title: Rich Logging and printing\n",
+    "\n",
+    "---\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    ""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ez logging\n",
+    "Logging is packed in these 5 functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[08/24/24 12:16:05]\u001b[0m\u001b[2;36m \u001b[0m\u001b[2;34mTRACE   \u001b[0m Testing \u001b[1m{\u001b[0m\u001b[1;36m1\u001b[0m,\u001b[1;36m2\u001b[0m,\u001b[1;36m3\u001b[0m\u001b[1m}\u001b[0m            \u001b]8;id=224366;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/1838145784.py:2\u001b\\\u001b[2m1838145784.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=418987;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/1838145784.py:2#:2\u001b\\\u001b[2m:2\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+       "
\n"
+      ],
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                    INFO     TESTING {1,2,3}            1838145784.py::4\" target=\"_blank\"><module>:4\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[2;33mINFO    \u001b[0m TESTING \u001b[1m{\u001b[0m\u001b[1;36m1\u001b[0m,\u001b[1;36m2\u001b[0m,\u001b[1;36m3\u001b[0m\u001b[1m}\u001b[0m            \u001b]8;id=231274;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/1838145784.py:4\u001b\\\u001b[2m1838145784.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=492146;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/1838145784.py:4#:4\u001b\\\u001b[2m:4\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+       "
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+      ],
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                    ERROR    TESTING {1,2,3}            1838145784.py::6\" target=\"_blank\"><module>:6\n",
+       "                             NoneType: None                                     \n",
+       "                                                                                \n",
+       "                                                                                \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m TESTING \u001b[1m{\u001b[0m\u001b[1;36m1\u001b[0m,\u001b[1;36m2\u001b[0m,\u001b[1;36m3\u001b[0m\u001b[1m}\u001b[0m            \u001b]8;id=714335;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/1838145784.py:6\u001b\\\u001b[2m1838145784.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=108282;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/1838145784.py:6#:6\u001b\\\u001b[2m:6\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                    \u001b[0m         NoneType: \u001b[3;35mNone\u001b[0m             \u001b[2m                        \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m                                    \u001b[2m                        \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m                                    \u001b[2m                        \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "reset_logger(\"trace\", console_width=80)\n",
+    "Trace(\"Testing {1,2,3}\")\n",
+    "Debug(\"TESTING {1,2,3}\")\n",
+    "Info(\"TESTING {1,2,3}\")\n",
+    "Warn(\"TESTING {1,2,3}\")\n",
+    "Excep(\"TESTING {1,2,3}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Excep` will also print a traceback"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
[08/24/24 12:16:07] ERROR    division by zero                 1923207781.py:do:5\n",
+       "                             ZeroDivisionError: division by                     \n",
+       "                             zero                                               \n",
+       "                                                                                \n",
+       "                             ╭─ Traceback (most recent call─╮                   \n",
+       "                             │ in do:3                      │                   \n",
+       "                             │                              │                   \n",
+       "                             │    1 def do():               │                   \n",
+       "                             │    2 │   try:                │                   \n",
+       "                             │ ❱  3 │   │   1 / 0           │                   \n",
+       "                             │    4 │   except Exception as │                   \n",
+       "                             │    5 │   │   Excep(e)        │                   \n",
+       "                             │    6                         │                   \n",
+       "                             ╰──────────────────────────────╯                   \n",
+       "                             ZeroDivisionError: division by                     \n",
+       "                             zero                                               \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[08/24/24 12:16:07]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m division by zero                 \u001b]8;id=376660;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/1923207781.py:5\u001b\\\u001b[2m1923207781.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=229375;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/1923207781.py:5#do:5\u001b\\\u001b[2mdo:5\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                    \u001b[0m         ZeroDivisionError: division by   \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         zero                             \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m                                          \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         \u001b[31m╭─\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call\u001b[0m\u001b[31m─╮\u001b[0m \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         \u001b[31m│\u001b[0m in \u001b[92mdo\u001b[0m:\u001b[94m3\u001b[0m                      \u001b[31m│\u001b[0m \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         \u001b[31m│\u001b[0m                              \u001b[31m│\u001b[0m \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         \u001b[31m│\u001b[0m   \u001b[2m 1 \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mdo\u001b[0m():               \u001b[31m│\u001b[0m \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         \u001b[31m│\u001b[0m   \u001b[2m 2 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mtry\u001b[0m:                \u001b[31m│\u001b[0m \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         \u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 3 \u001b[2m│   │   \u001b[0m\u001b[94m1\u001b[0m / \u001b[94m0\u001b[0m           \u001b[31m│\u001b[0m \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         \u001b[31m│\u001b[0m   \u001b[2m 4 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mexcept\u001b[0m \u001b[96mException\u001b[0m \u001b[94mas\u001b[0m \u001b[31m│\u001b[0m \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         \u001b[31m│\u001b[0m   \u001b[2m 5 \u001b[0m\u001b[2m│   │   \u001b[0mExcep(e)        \u001b[31m│\u001b[0m \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         \u001b[31m│\u001b[0m   \u001b[2m 6 \u001b[0m                        \u001b[31m│\u001b[0m \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         \u001b[31m╰──────────────────────────────╯\u001b[0m \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         \u001b[1;91mZeroDivisionError: \u001b[0mdivision by   \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         zero                             \u001b[2m                  \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def do():\n",
+    "    try:\n",
+    "        1 / 0\n",
+    "    except Exception as e:\n",
+    "        Excep(e)\n",
+    "\n",
+    "def do2():\n",
+    "    do()\n",
+    "\n",
+    "do2()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All logging functions have an optional depth that will be helpful to raise the context level to outer functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
[08/24/24 12:16:15] INFO     In do                             809811040.py:do:2\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[08/24/24 12:16:15]\u001b[0m\u001b[2;36m \u001b[0m\u001b[2;33mINFO    \u001b[0m In do                             \u001b]8;id=244287;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/809811040.py:2\u001b\\\u001b[2m809811040.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=530362;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/809811040.py:2#do:2\u001b\\\u001b[2mdo:2\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def do():\n",
+    "    Info('In do')\n",
+    "\n",
+    "do()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
[08/24/24 12:17:13] INFO     Log will still say it is from   2099333876.py:do2:2\n",
+       "                             `do2` now, to the right of log                     \n",
+       "                             print                                              \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[08/24/24 12:17:13]\u001b[0m\u001b[2;36m \u001b[0m\u001b[2;33mINFO    \u001b[0m Log will still say it is from   \u001b]8;id=608563;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/2099333876.py:2\u001b\\\u001b[2m2099333876.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=696305;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/2099333876.py:2#do2:2\u001b\\\u001b[2mdo2:2\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                    \u001b[0m         `do2` now, to the right of log  \u001b[2m                   \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         print                           \u001b[2m                   \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def do2():\n",
+    "    Info('Log will still say it is from `do2` now, to the right of log print')\n",
+    "\n",
+    "def do():\n",
+    "    do2()\n",
+    "\n",
+    "do()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
[08/24/24 12:17:25] INFO     But now, log will still say it   4054019042.py:do:5\n",
+       "                             is from do                                         \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[08/24/24 12:17:25]\u001b[0m\u001b[2;36m \u001b[0m\u001b[2;33mINFO    \u001b[0m But now, log will still say it   \u001b]8;id=342927;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/4054019042.py:5\u001b\\\u001b[2m4054019042.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=431233;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/4054019042.py:5#do:5\u001b\\\u001b[2mdo:5\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                    \u001b[0m         is from do                       \u001b[2m                  \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def do2():\n",
+    "    Info('But now, log will still say it is from do', depth=1)\n",
+    "\n",
+    "def do():\n",
+    "    do2()\n",
+    "\n",
+    "do()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Logging Level and context"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "has_sd": true,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "---\n",
+       "\n",
+       "### in_logger_mode\n",
+       "\n",
+       ">      in_logger_mode (level:str)\n",
+       "\n",
+       "*return's T/F, checking if logger is in a specific mode or not*"
+      ],
+      "text/plain": [
+       "---\n",
+       "\n",
+       "### in_logger_mode\n",
+       "\n",
+       ">      in_logger_mode (level:str)\n",
+       "\n",
+       "*return's T/F, checking if logger is in a specific mode or not*"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#| echo: false\n",
+    "#| output: asis\n",
+    "show_doc(in_logger_mode)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "has_sd": true,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "---\n",
+       "\n",
+       "### logger_mode\n",
+       "\n",
+       ">      logger_mode (level)\n",
+       "\n",
+       "*temporarily, using `with` context,\n",
+       "set the level to something else*"
+      ],
+      "text/plain": [
+       "---\n",
+       "\n",
+       "### logger_mode\n",
+       "\n",
+       ">      logger_mode (level)\n",
+       "\n",
+       "*temporarily, using `with` context,\n",
+       "set the level to something else*"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#| echo: false\n",
+    "#| output: asis\n",
+    "show_doc(logger_mode)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "has_sd": true,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "---\n",
+       "\n",
+       "### get_logger_level\n",
+       "\n",
+       ">      get_logger_level ()\n",
+       "\n",
+       "*get the current logger's level*"
+      ],
+      "text/plain": [
+       "---\n",
+       "\n",
+       "### get_logger_level\n",
+       "\n",
+       ">      get_logger_level ()\n",
+       "\n",
+       "*get the current logger's level*"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#| echo: false\n",
+    "#| output: asis\n",
+    "show_doc(get_logger_level)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's log every level in the `do` function below.\n",
+    "We can control what we need to log from outside the function's context by  \n",
+    "using `with _model():`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "====================Excep mode====================\n",
+      "in_excep_mode()=True\n",
+      "in_warn_mode()=False\n",
+      "in_info_mode()=False\n",
+      "in_debug_mode()=False\n",
+      "in_trace_mode()=False\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
[08/24/24 12:13:08] ERROR    4                                                                                                                            3432048509.py:do:6\n",
+       "                             NoneType: None                                                                                                                                 \n",
+       "                                                                                                                                                                            \n",
+       "                                                                                                                                                                            \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[08/24/24 12:13:08]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m \u001b[1;36m4\u001b[0m                                                                                                                            \u001b]8;id=916055;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/3432048509.py:6\u001b\\\u001b[2m3432048509.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=120477;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/3432048509.py:6#do:6\u001b\\\u001b[2mdo:6\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                    \u001b[0m         NoneType: \u001b[3;35mNone\u001b[0m                                                                                                               \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m                                                                                                                                      \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m                                                                                                                                      \u001b[2m                  \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "====================Warn mode====================\n",
+      "in_excep_mode()=False\n",
+      "in_warn_mode()=True\n",
+      "in_info_mode()=False\n",
+      "in_debug_mode()=False\n",
+      "in_trace_mode()=False\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
[08/24/24 12:13:08] WARNING  3                                                                                                                            3432048509.py:do:5\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[08/24/24 12:13:08]\u001b[0m\u001b[2;36m \u001b[0m\u001b[2;31mWARNING \u001b[0m \u001b[1;36m3\u001b[0m                                                                                                                            \u001b]8;id=218910;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/3432048509.py:5\u001b\\\u001b[2m3432048509.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=668161;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/3432048509.py:5#do:5\u001b\\\u001b[2mdo:5\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                    ERROR    4                                                                                                                            3432048509.py:do:6\n",
+       "                             NoneType: None                                                                                                                                 \n",
+       "                                                                                                                                                                            \n",
+       "                                                                                                                                                                            \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m \u001b[1;36m4\u001b[0m                                                                                                                            \u001b]8;id=184912;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/3432048509.py:6\u001b\\\u001b[2m3432048509.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=71750;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/3432048509.py:6#do:6\u001b\\\u001b[2mdo:6\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                    \u001b[0m         NoneType: \u001b[3;35mNone\u001b[0m                                                                                                               \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m                                                                                                                                      \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m                                                                                                                                      \u001b[2m                  \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "====================Info mode====================\n",
+      "in_excep_mode()=False\n",
+      "in_warn_mode()=False\n",
+      "in_info_mode()=True\n",
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+     ]
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+       "                             NoneType: None                                                                                                                                 \n",
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+      "====================Debug mode====================\n",
+      "in_excep_mode()=False\n",
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+      "in_info_mode()=False\n",
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+      "in_trace_mode()=False\n"
+     ]
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+       "                             NoneType: None                                                                                                                                 \n",
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "====================Trace mode====================\n",
+      "in_excep_mode()=False\n",
+      "in_warn_mode()=False\n",
+      "in_info_mode()=False\n",
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+      "in_trace_mode()=True\n"
+     ]
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+       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[2;32mDEBUG   \u001b[0m \u001b[1;36m1\u001b[0m                                                                                                                            \u001b]8;id=12861;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/3432048509.py:3\u001b\\\u001b[2m3432048509.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=936132;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/3432048509.py:3#do:3\u001b\\\u001b[2mdo:3\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
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+       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[2;33mINFO    \u001b[0m \u001b[1;36m2\u001b[0m                                                                                                                            \u001b]8;id=464736;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/3432048509.py:4\u001b\\\u001b[2m3432048509.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=856526;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/3432048509.py:4#do:4\u001b\\\u001b[2mdo:4\u001b[0m\u001b]8;;\u001b\\\n"
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+     "output_type": "display_data"
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+    {
+     "data": {
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+       "
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+      ],
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+       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[2;31mWARNING \u001b[0m \u001b[1;36m3\u001b[0m                                                                                                                            \u001b]8;id=696903;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/3432048509.py:5\u001b\\\u001b[2m3432048509.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=821308;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/3432048509.py:5#do:5\u001b\\\u001b[2mdo:5\u001b[0m\u001b]8;;\u001b\\\n"
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                    ERROR    4                                                                                                                            3432048509.py:do:6\n",
+       "                             NoneType: None                                                                                                                                 \n",
+       "                                                                                                                                                                            \n",
+       "                                                                                                                                                                            \n",
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+      ],
+      "text/plain": [
+       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m \u001b[1;36m4\u001b[0m                                                                                                                            \u001b]8;id=181087;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/3432048509.py:6\u001b\\\u001b[2m3432048509.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=999705;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/3432048509.py:6#do:6\u001b\\\u001b[2mdo:6\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                    \u001b[0m         NoneType: \u001b[3;35mNone\u001b[0m                                                                                                               \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m                                                                                                                                      \u001b[2m                  \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m                                                                                                                                      \u001b[2m                  \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def do():\n",
+    "    Trace(0)\n",
+    "    Debug(1)\n",
+    "    Info(2)\n",
+    "    Warn(3)\n",
+    "    Excep(4)\n",
+    "\n",
+    "\n",
+    "def line(x):\n",
+    "    sep = \"=\" * 20\n",
+    "    print(f\"{sep}{x}{sep}\")\n",
+    "    print(f\"{in_excep_mode()=}\")\n",
+    "    print(f\"{in_warn_mode()=}\")\n",
+    "    print(f\"{in_info_mode()=}\")\n",
+    "    print(f\"{in_debug_mode()=}\")\n",
+    "    print(f\"{in_trace_mode()=}\")\n",
+    "\n",
+    "\n",
+    "reset_logger()\n",
+    "\n",
+    "with excep_mode():\n",
+    "    line(\"Excep mode\")\n",
+    "    do()\n",
+    "\n",
+    "with warn_mode():\n",
+    "    line(\"Warn mode\")\n",
+    "    do()\n",
+    "\n",
+    "with info_mode():\n",
+    "    line(\"Info mode\")\n",
+    "    do()\n",
+    "\n",
+    "with debug_mode():\n",
+    "    line(\"Debug mode\")\n",
+    "    do()\n",
+    "\n",
+    "with trace_mode():\n",
+    "    line(\"Trace mode\")\n",
+    "    do()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `in__mode` gives an additional layer of control, to be used for debugging dynamically. Let's say, you want to show an image (for the sake of debugging)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "145.5542982442515\n"
+     ]
+    }
+   ],
+   "source": [
+    "def do(im_path):\n",
+    "    from torch_snippets import show, read\n",
+    "    im = read(im_path)\n",
+    "    show(im, sz=3)\n",
+    "    print(im.mean())\n",
+    "\n",
+    "do('assets/Preamble.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But now you are happy with your code and don't want the show, say the code is going to production. A common way out is to just comment that line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "145.5542982442515\n"
+     ]
+    }
+   ],
+   "source": [
+    "def do(im_path):\n",
+    "    from torch_snippets import show, read\n",
+    "    im = read(im_path)\n",
+    "    # show(im, sz=3) # line is commented, but will need to be re-uncommented any time it needs debugging\n",
+    "    print(im.mean())\n",
+    "\n",
+    "do('assets/Preamble.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But if you want to re-check, it's a pain to again uncomment. Not to mention this method is not scalable to 100s of lines of code. The simple way to deal with such transient code that needs to activate only when you want it to, is to enclose in an `if in__mode` conditional like so"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "145.5542982442515\n"
+     ]
+    }
+   ],
+   "source": [
+    "def do(im_path):\n",
+    "    from torch_snippets import show, read\n",
+    "    im = read(im_path)\n",
+    "    if in_debug_mode():\n",
+    "        show(im, sz=3)\n",
+    "    print(im.mean())\n",
+    "\n",
+    "do('assets/Preamble.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This way, you can always activate the show by calling `do` with a temporary `with_debug_mode` context"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "145.5542982442515\n"
+     ]
+    }
+   ],
+   "source": [
+    "with debug_mode():\n",
+    "    do('assets/Preamble.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Notify Waiting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
[08/24/24 12:36:51] TRACE    Available Spinners: dict_keys(['dots', 'dots2', 'dots3', 'dots4', 'dots5', 'dots6', 'dots7', 'dots8', 'dots9',  3340474699.py:notify_waiting:41\n",
+       "                             'dots10', 'dots11', 'dots12', 'dots8Bit', 'line', 'line2', 'pipe', 'simpleDots', 'simpleDotsScrolling', 'star',                                \n",
+       "                             'star2', 'flip', 'hamburger', 'growVertical', 'growHorizontal', 'balloon', 'balloon2', 'noise', 'bounce',                                      \n",
+       "                             'boxBounce', 'boxBounce2', 'triangle', 'arc', 'circle', 'squareCorners', 'circleQuarters', 'circleHalves',                                     \n",
+       "                             'squish', 'toggle', 'toggle2', 'toggle3', 'toggle4', 'toggle5', 'toggle6', 'toggle7', 'toggle8', 'toggle9',                                    \n",
+       "                             'toggle10', 'toggle11', 'toggle12', 'toggle13', 'arrow', 'arrow2', 'arrow3', 'bouncingBar', 'bouncingBall',                                    \n",
+       "                             'smiley', 'monkey', 'hearts', 'clock', 'earth', 'material', 'moon', 'runner', 'pong', 'shark', 'dqpb',                                         \n",
+       "                             'weather', 'christmas', 'grenade', 'point', 'layer', 'betaWave', 'aesthetic', 'guess', 'guess_the_movie'])                                     \n",
+       "                                                                                                                                                                            \n",
+       "                             Using: clock                                                                                                                                   \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[08/24/24 12:36:51]\u001b[0m\u001b[2;36m \u001b[0m\u001b[2;34mTRACE   \u001b[0m Available Spinners: \u001b[1;35mdict_keys\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[32m'dots'\u001b[0m, \u001b[32m'dots2'\u001b[0m, \u001b[32m'dots3'\u001b[0m, \u001b[32m'dots4'\u001b[0m, \u001b[32m'dots5'\u001b[0m, \u001b[32m'dots6'\u001b[0m, \u001b[32m'dots7'\u001b[0m, \u001b[32m'dots8'\u001b[0m, \u001b[32m'dots9'\u001b[0m,  \u001b]8;id=248925;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/3340474699.py:41\u001b\\\u001b[2m3340474699.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=418244;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_44029/3340474699.py:41#notify_waiting:41\u001b\\\u001b[2mnotify_waiting:41\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                    \u001b[0m         \u001b[32m'dots10'\u001b[0m, \u001b[32m'dots11'\u001b[0m, \u001b[32m'dots12'\u001b[0m, \u001b[32m'dots8Bit'\u001b[0m, \u001b[32m'line'\u001b[0m, \u001b[32m'line2'\u001b[0m, \u001b[32m'pipe'\u001b[0m, \u001b[32m'simpleDots'\u001b[0m, \u001b[32m'simpleDotsScrolling'\u001b[0m, \u001b[32m'star'\u001b[0m, \u001b[2m                               \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         \u001b[32m'star2'\u001b[0m, \u001b[32m'flip'\u001b[0m, \u001b[32m'hamburger'\u001b[0m, \u001b[32m'growVertical'\u001b[0m, \u001b[32m'growHorizontal'\u001b[0m, \u001b[32m'balloon'\u001b[0m, \u001b[32m'balloon2'\u001b[0m, \u001b[32m'noise'\u001b[0m, \u001b[32m'bounce'\u001b[0m,       \u001b[2m                               \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         \u001b[32m'boxBounce'\u001b[0m, \u001b[32m'boxBounce2'\u001b[0m, \u001b[32m'triangle'\u001b[0m, \u001b[32m'arc'\u001b[0m, \u001b[32m'circle'\u001b[0m, \u001b[32m'squareCorners'\u001b[0m, \u001b[32m'circleQuarters'\u001b[0m, \u001b[32m'circleHalves'\u001b[0m,      \u001b[2m                               \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         \u001b[32m'squish'\u001b[0m, \u001b[32m'toggle'\u001b[0m, \u001b[32m'toggle2'\u001b[0m, \u001b[32m'toggle3'\u001b[0m, \u001b[32m'toggle4'\u001b[0m, \u001b[32m'toggle5'\u001b[0m, \u001b[32m'toggle6'\u001b[0m, \u001b[32m'toggle7'\u001b[0m, \u001b[32m'toggle8'\u001b[0m, \u001b[32m'toggle9'\u001b[0m,     \u001b[2m                               \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         \u001b[32m'toggle10'\u001b[0m, \u001b[32m'toggle11'\u001b[0m, \u001b[32m'toggle12'\u001b[0m, \u001b[32m'toggle13'\u001b[0m, \u001b[32m'arrow'\u001b[0m, \u001b[32m'arrow2'\u001b[0m, \u001b[32m'arrow3'\u001b[0m, \u001b[32m'bouncingBar'\u001b[0m, \u001b[32m'bouncingBall'\u001b[0m,     \u001b[2m                               \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         \u001b[32m'smiley'\u001b[0m, \u001b[32m'monkey'\u001b[0m, \u001b[32m'hearts'\u001b[0m, \u001b[32m'clock'\u001b[0m, \u001b[32m'earth'\u001b[0m, \u001b[32m'material'\u001b[0m, \u001b[32m'moon'\u001b[0m, \u001b[32m'runner'\u001b[0m, \u001b[32m'pong'\u001b[0m, \u001b[32m'shark'\u001b[0m, \u001b[32m'dqpb'\u001b[0m,          \u001b[2m                               \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         \u001b[32m'weather'\u001b[0m, \u001b[32m'christmas'\u001b[0m, \u001b[32m'grenade'\u001b[0m, \u001b[32m'point'\u001b[0m, \u001b[32m'layer'\u001b[0m, \u001b[32m'betaWave'\u001b[0m, \u001b[32m'aesthetic'\u001b[0m, \u001b[32m'guess'\u001b[0m, \u001b[32m'guess_the_movie'\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m      \u001b[2m                               \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m                                                                                                                         \u001b[2m                               \u001b[0m\n",
+       "\u001b[2;36m                    \u001b[0m         Using: clock                                                                                                    \u001b[2m                               \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
[08/24/24 12:37:01] INFO     Downloading - Completed in 10.01 s                                                                                   contextlib.py:__exit__:144\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[08/24/24 12:37:01]\u001b[0m\u001b[2;36m \u001b[0m\u001b[2;33mINFO    \u001b[0m Downloading - Completed in \u001b[1;36m10.01\u001b[0m s                                                                                   \u001b]8;id=4266;file:///opt/miniconda3/lib/python3.12/contextlib.py:144\u001b\\\u001b[2mcontextlib.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=46533;file:///opt/miniconda3/lib/python3.12/contextlib.py:144#__exit__:144\u001b\\\u001b[2m__exit__:144\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
[08/24/24 12:37:11] INFO     Downloading - Completed in 10.01 s                                                                                   contextlib.py:__exit__:144\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[08/24/24 12:37:11]\u001b[0m\u001b[2;36m \u001b[0m\u001b[2;33mINFO    \u001b[0m Downloading - Completed in \u001b[1;36m10.01\u001b[0m s                                                                                   \u001b]8;id=137393;file:///opt/miniconda3/lib/python3.12/contextlib.py:144\u001b\\\u001b[2mcontextlib.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=906004;file:///opt/miniconda3/lib/python3.12/contextlib.py:144#__exit__:144\u001b\\\u001b[2m__exit__:144\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
[08/24/24 12:37:14] INFO     One more message - Completed in 3.00 s                                                                               contextlib.py:__exit__:144\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[08/24/24 12:37:14]\u001b[0m\u001b[2;36m \u001b[0m\u001b[2;33mINFO    \u001b[0m One more message - Completed in \u001b[1;36m3.00\u001b[0m s                                                                               \u001b]8;id=247746;file:///opt/miniconda3/lib/python3.12/contextlib.py:144\u001b\\\u001b[2mcontextlib.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=719324;file:///opt/miniconda3/lib/python3.12/contextlib.py:144#__exit__:144\u001b\\\u001b[2m__exit__:144\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "reset_logger(console_width=100)\n",
+    "\n",
+    "with trace_mode():\n",
+    "    with notify_waiting(\"Downloading\"):\n",
+    "        time.sleep(10)\n",
+    "\n",
+    "with notify_waiting(\"Downloading\", spinner=\"guess_the_movie\"):\n",
+    "    time.sleep(10)\n",
+    "\n",
+    "with notify_waiting(\"One more message\", spinner=\"earth\"):\n",
+    "    time.sleep(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
+      "text/html": [
+       "\n",
+       "        \n",
+       "        "
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# | echo: false\n",
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo('HGsG0jVFREY', width=560, height=315)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "0a92af74ccf65e106ae96852a45a9b0a5e11a3c414a16fc8bb2d134868e55bfd"
+   }
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/nbs/index.ipynb b/nbs/index.ipynb
index 05afd17..6c641a3 100644
--- a/nbs/index.ipynb
+++ b/nbs/index.ipynb
@@ -17,13 +17,6 @@
     "Simply call - "
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -33,8 +26,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 304 ms, sys: 65.3 ms, total: 369 ms\n",
-      "Wall time: 469 ms\n"
+      "CPU times: user 263 ms, sys: 53.9 ms, total: 317 ms\n",
+      "Wall time: 407 ms\n"
      ]
     }
    ],
@@ -56,6 +49,72 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ast\n",
+    "\n",
+    "os.environ[\n",
+    "    \"AD_MAX_ITEMS\"\n",
+    "] = (  # os is already imported by torch_snippets, along with many other useful libraries\n",
+    "    \"1000\"  # Set the maximum number of items to display in the AD object\n",
+    ")\n",
+    "\n",
+    "\n",
+    "@tryy  # This is a decorator that catches exceptions\n",
+    "def extract_all_list(file_path):\n",
+    "    file = readfile(file_path, silent=True)  # Read the file\n",
+    "    tree = ast.parse(file, filename=file_path)\n",
+    "\n",
+    "    for node in tree.body:\n",
+    "        if isinstance(node, ast.Assign):\n",
+    "            for target in node.targets:\n",
+    "                if isinstance(target, ast.Name) and target.id == \"__all__\":\n",
+    "                    if isinstance(node.value, ast.List):\n",
+    "                        all_list = [\n",
+    "                            elt.value\n",
+    "                            for elt in node.value.elts\n",
+    "                            if isinstance(elt, ast.Constant)\n",
+    "                        ]\n",
+    "                        return all_list\n",
+    "    return None\n",
+    "\n",
+    "\n",
+    "def print_all_lists_in_directory(directory):\n",
+    "    dir = P(directory)  # Create a pathlib.Path object\n",
+    "    for f in dir.ls():  # Iterate over all files in the directory\n",
+    "        if f.extn == \"py\" and f.stem not in [\n",
+    "            \"__init__\",\n",
+    "            \"_nbdev\",\n",
+    "        ]:  # If it's a Python file and not __init__.py\n",
+    "            all_list = extract_all_list(f)\n",
+    "            if all_list is not None and len(all_list) > 0:\n",
+    "                h2(f.stem)  # Print the name of the file as a heading in jupyter\n",
+    "                print(\n",
+    "                    AD({\"items\": all_list})\n",
+    "                )  # AD is an intelligent dictionary that can display itself nicely"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/yeshwanth/Code/Personal/torch_snippets/nbs\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(P().resolve())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -457,26 +516,28 @@
       "  0 - console (🏷️ str)\n",
       "  1 - reset_logger_width (🏷️ str)\n",
       "  2 - logger (🏷️ str)\n",
-      "  3 - Debug (🏷️ str)\n",
-      "  4 - Info (🏷️ str)\n",
-      "  5 - Warn (🏷️ str)\n",
-      "  6 - Excep (🏷️ str)\n",
-      "  7 - warn_mode (🏷️ str)\n",
-      "  8 - info_mode (🏷️ str)\n",
-      "  9 - debug_mode (🏷️ str)\n",
-      "  10 - excep_mode (🏷️ str)\n",
-      "  11 - in_warn_mode (🏷️ str)\n",
-      "  12 - in_info_mode (🏷️ str)\n",
-      "  13 - in_debug_mode (🏷️ str)\n",
-      "  14 - in_excep_mode (🏷️ str)\n",
-      "  15 - frames (🏷️ str)\n",
-      "  16 - get_console (🏷️ str)\n",
-      "  17 - reset_logger (🏷️ str)\n",
-      "  18 - enter_exit (🏷️ str)\n",
-      "  19 - get_logger_level (🏷️ str)\n",
-      "  20 - logger_mode (🏷️ str)\n",
-      "  21 - in_logger_mode (🏷️ str)\n",
-      "  22 - notify_waiting (🏷️ str)\n",
+      "  3 - Trace (🏷️ str)\n",
+      "  4 - Debug (🏷️ str)\n",
+      "  5 - Info (🏷️ str)\n",
+      "  6 - Warn (🏷️ str)\n",
+      "  7 - Excep (🏷️ str)\n",
+      "  8 - warn_mode (🏷️ str)\n",
+      "  9 - info_mode (🏷️ str)\n",
+      "  10 - debug_mode (🏷️ str)\n",
+      "  11 - trace_mode (🏷️ str)\n",
+      "  12 - excep_mode (🏷️ str)\n",
+      "  13 - in_warn_mode (🏷️ str)\n",
+      "  14 - in_info_mode (🏷️ str)\n",
+      "  15 - in_debug_mode (🏷️ str)\n",
+      "  16 - in_trace_mode (🏷️ str)\n",
+      "  17 - in_excep_mode (🏷️ str)\n",
+      "  18 - frames (🏷️ str)\n",
+      "  19 - get_console (🏷️ str)\n",
+      "  20 - reset_logger (🏷️ str)\n",
+      "  21 - get_logger_level (🏷️ str)\n",
+      "  22 - logger_mode (🏷️ str)\n",
+      "  23 - in_logger_mode (🏷️ str)\n",
+      "  24 - notify_waiting (🏷️ str)\n",
       "\n",
       "```\n",
       "\n"
@@ -896,58 +957,22 @@
     }
    ],
    "source": [
-    "import ast\n",
-    "\n",
-    "os.environ[\n",
-    "    \"AD_MAX_ITEMS\"\n",
-    "] = (  # os is already imported by torch_snippets, along with many other useful libraries\n",
-    "    \"1000\"  # Set the maximum number of items to display in the AD object\n",
-    ")\n",
-    "\n",
-    "\n",
-    "@tryy  # This is a decorator that catches exceptions\n",
-    "def extract_all_list(file_path):\n",
-    "    file = readfile(file_path, silent=True)  # Read the file\n",
-    "    tree = ast.parse(file, filename=file_path)\n",
-    "\n",
-    "    for node in tree.body:\n",
-    "        if isinstance(node, ast.Assign):\n",
-    "            for target in node.targets:\n",
-    "                if isinstance(target, ast.Name) and target.id == \"__all__\":\n",
-    "                    if isinstance(node.value, ast.List):\n",
-    "                        all_list = [\n",
-    "                            elt.value\n",
-    "                            for elt in node.value.elts\n",
-    "                            if isinstance(elt, ast.Constant)\n",
-    "                        ]\n",
-    "                        return all_list\n",
-    "    return None\n",
-    "\n",
-    "\n",
-    "def print_all_lists_in_directory(directory):\n",
-    "    dir = P(directory)  # Create a pathlib.Path object\n",
-    "    for f in dir.ls():  # Iterate over all files in the directory\n",
-    "        if f.extn == \"py\" and f.stem not in [\n",
-    "            \"__init__\",\n",
-    "            \"_nbdev\",\n",
-    "        ]:  # If it's a Python file and not __init__.py\n",
-    "            all_list = extract_all_list(f)\n",
-    "            if all_list is not None and len(all_list) > 0:\n",
-    "                h2(f.stem)  # Print the name of the file as a heading in jupyter\n",
-    "                print(\n",
-    "                    AD({\"items\": all_list})\n",
-    "                )  # AD is an intelligent dictionary that can display itself nicely\n",
-    "\n",
-    "\n",
     "# Specify the directory containing the Python files\n",
     "directory_path = \"../torch_snippets\"\n",
     "print_all_lists_in_directory(directory_path)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/nbs/logging.ipynb b/nbs/logging.ipynb
index af3b64b..2cd6648 100644
--- a/nbs/logging.ipynb
+++ b/nbs/logging.ipynb
@@ -894,7 +894,7 @@
     "    show(im, sz=3)\n",
     "    print(im.mean())\n",
     "\n",
-    "do('../assets/Preamble.png')"
+    "do('assets/Preamble.png')"
    ]
   },
   {
@@ -924,7 +924,7 @@
     "    # show(im, sz=3) # line is commented, but will need to be re-uncommented any time it needs debugging\n",
     "    print(im.mean())\n",
     "\n",
-    "do('../assets/Preamble.png')"
+    "do('assets/Preamble.png')"
    ]
   },
   {
@@ -955,7 +955,7 @@
     "        show(im, sz=3)\n",
     "    print(im.mean())\n",
     "\n",
-    "do('../assets/Preamble.png')"
+    "do('assets/Preamble.png')"
    ]
   },
   {
@@ -990,7 +990,7 @@
    ],
    "source": [
     "with debug_mode():\n",
-    "    do('../assets/Preamble.png')"
+    "    do('assets/Preamble.png')"
    ]
   },
   {
diff --git a/scripts.ipynb b/scripts.ipynb
index f9c6aa5..3e80d04 100644
--- a/scripts.ipynb
+++ b/scripts.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -15,19 +15,19 @@
       "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/load_defaults.py\u001b[0m\n",
       "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/decorators.py\u001b[0m\n",
       "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/inspector.py\u001b[0m\n",
-      "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/misc.py\u001b[0m\n",
       "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/imgaug_loader.py\u001b[0m\n",
       "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/ipython.py\u001b[0m\n",
       "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/pdf_loader.py\u001b[0m\n",
-      "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/adapters.py\u001b[0m\n",
+      "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/misc.py\u001b[0m\n",
       "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/registry.py\u001b[0m\n",
+      "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/adapters.py\u001b[0m\n",
       "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/interactive_show.py\u001b[0m\n",
       "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/logger.py\u001b[0m\n",
       "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/trainer/config.py\u001b[0m\n",
       "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/sklegos.py\u001b[0m\n",
       "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/trainer/capsule.py\u001b[0m\n",
-      "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/bb_utils.py\u001b[0m\n",
       "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/charts.py\u001b[0m\n",
+      "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/bb_utils.py\u001b[0m\n",
       "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/markup.py\u001b[0m\n",
       "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/paths.py\u001b[0m\n",
       "\u001b[1mreformatted /Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/_modidx.py\u001b[0m\n",
@@ -36,121 +36,121 @@
       "\u001b[34m\u001b[1m20 files \u001b[0m\u001b[1mreformatted\u001b[0m, \u001b[34m22 files \u001b[0mleft unchanged.\n",
       "Obtaining file:///Users/yeshwanth/Code/Personal/torch_snippets\n",
       "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
-      "\u001b[?25hRequirement already satisfied: fastcore in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (1.6.3)\n",
-      "Requirement already satisfied: matplotlib in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (3.9.1)\n",
-      "Requirement already satisfied: Pillow in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (10.4.0)\n",
-      "Requirement already satisfied: altair in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (5.3.0)\n",
-      "Requirement already satisfied: dill in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (0.3.8)\n",
-      "Requirement already satisfied: ipython in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (8.26.0)\n",
-      "Requirement already satisfied: loguru in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (0.7.2)\n",
-      "Requirement already satisfied: numpy in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (2.0.1)\n",
-      "Requirement already satisfied: pandas in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (2.2.2)\n",
-      "Requirement already satisfied: tqdm in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (4.66.4)\n",
-      "Requirement already satisfied: rich in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (13.7.1)\n",
-      "Requirement already satisfied: PyYAML in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (6.0.1)\n",
-      "Requirement already satisfied: catalogue in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (2.0.10)\n",
-      "Requirement already satisfied: confection in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (0.1.5)\n",
-      "Requirement already satisfied: pydantic in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (2.8.2)\n",
-      "Requirement already satisfied: typing in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (3.7.4.3)\n",
-      "Requirement already satisfied: srsly in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (2.4.8)\n",
-      "Requirement already satisfied: typing_extensions in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (4.12.2)\n",
-      "Requirement already satisfied: wasabi in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (1.1.3)\n",
-      "Requirement already satisfied: jsonlines in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (4.0.0)\n",
-      "Requirement already satisfied: imgaug>=0.4.0 in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (0.4.0)\n",
-      "Requirement already satisfied: xmltodict in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (0.13.0)\n",
-      "Requirement already satisfied: fuzzywuzzy in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (0.18.0)\n",
-      "Requirement already satisfied: scikit-learn in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (1.5.1)\n",
-      "Requirement already satisfied: nltk in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (3.8.1)\n",
-      "Requirement already satisfied: python-Levenshtein in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (0.25.1)\n",
-      "Requirement already satisfied: pre-commit in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (3.7.1)\n",
-      "Requirement already satisfied: pymupdf in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (1.24.9)\n",
-      "Requirement already satisfied: nbconvert in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (7.16.4)\n",
-      "Requirement already satisfied: nbformat in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (5.10.4)\n",
-      "Requirement already satisfied: icecream in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (2.1.3)\n",
-      "Requirement already satisfied: mergedeep in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (1.3.4)\n",
-      "Requirement already satisfied: typer in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.6.0) (0.12.4)\n",
-      "Requirement already satisfied: six in /opt/miniconda3/lib/python3.12/site-packages (from imgaug>=0.4.0->torch_snippets==0.6.0) (1.16.0)\n",
-      "Requirement already satisfied: scipy in /opt/miniconda3/lib/python3.12/site-packages (from imgaug>=0.4.0->torch_snippets==0.6.0) (1.14.0)\n",
-      "Requirement already satisfied: scikit-image>=0.14.2 in /opt/miniconda3/lib/python3.12/site-packages (from imgaug>=0.4.0->torch_snippets==0.6.0) (0.24.0)\n",
-      "Requirement already satisfied: opencv-python in /opt/miniconda3/lib/python3.12/site-packages (from imgaug>=0.4.0->torch_snippets==0.6.0) (4.10.0.84)\n",
-      "Requirement already satisfied: imageio in /opt/miniconda3/lib/python3.12/site-packages (from imgaug>=0.4.0->torch_snippets==0.6.0) (2.34.2)\n",
-      "Requirement already satisfied: Shapely in /opt/miniconda3/lib/python3.12/site-packages (from imgaug>=0.4.0->torch_snippets==0.6.0) (2.0.5)\n",
-      "Requirement already satisfied: jinja2 in /opt/miniconda3/lib/python3.12/site-packages (from altair->torch_snippets==0.6.0) (3.1.4)\n",
-      "Requirement already satisfied: jsonschema>=3.0 in /opt/miniconda3/lib/python3.12/site-packages (from altair->torch_snippets==0.6.0) (4.23.0)\n",
-      "Requirement already satisfied: packaging in /opt/miniconda3/lib/python3.12/site-packages (from altair->torch_snippets==0.6.0) (23.2)\n",
-      "Requirement already satisfied: toolz in /opt/miniconda3/lib/python3.12/site-packages (from altair->torch_snippets==0.6.0) (0.12.1)\n",
-      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/miniconda3/lib/python3.12/site-packages (from pandas->torch_snippets==0.6.0) (2.9.0.post0)\n",
-      "Requirement already satisfied: pytz>=2020.1 in /opt/miniconda3/lib/python3.12/site-packages (from pandas->torch_snippets==0.6.0) (2024.1)\n",
-      "Requirement already satisfied: tzdata>=2022.7 in /opt/miniconda3/lib/python3.12/site-packages (from pandas->torch_snippets==0.6.0) (2024.1)\n",
-      "Requirement already satisfied: annotated-types>=0.4.0 in /opt/miniconda3/lib/python3.12/site-packages (from pydantic->torch_snippets==0.6.0) (0.7.0)\n",
-      "Requirement already satisfied: pydantic-core==2.20.1 in /opt/miniconda3/lib/python3.12/site-packages (from pydantic->torch_snippets==0.6.0) (2.20.1)\n",
-      "Requirement already satisfied: colorama>=0.3.9 in /opt/miniconda3/lib/python3.12/site-packages (from icecream->torch_snippets==0.6.0) (0.4.6)\n",
-      "Requirement already satisfied: pygments>=2.2.0 in /opt/miniconda3/lib/python3.12/site-packages (from icecream->torch_snippets==0.6.0) (2.18.0)\n",
-      "Requirement already satisfied: executing>=0.3.1 in /opt/miniconda3/lib/python3.12/site-packages (from icecream->torch_snippets==0.6.0) (2.0.1)\n",
-      "Requirement already satisfied: asttokens>=2.0.1 in /opt/miniconda3/lib/python3.12/site-packages (from icecream->torch_snippets==0.6.0) (2.4.1)\n",
-      "Requirement already satisfied: decorator in /opt/miniconda3/lib/python3.12/site-packages (from ipython->torch_snippets==0.6.0) (5.1.1)\n",
-      "Requirement already satisfied: jedi>=0.16 in /opt/miniconda3/lib/python3.12/site-packages (from ipython->torch_snippets==0.6.0) (0.19.1)\n",
-      "Requirement already satisfied: matplotlib-inline in /opt/miniconda3/lib/python3.12/site-packages (from ipython->torch_snippets==0.6.0) (0.1.7)\n",
-      "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /opt/miniconda3/lib/python3.12/site-packages (from ipython->torch_snippets==0.6.0) (3.0.47)\n",
-      "Requirement already satisfied: stack-data in /opt/miniconda3/lib/python3.12/site-packages (from ipython->torch_snippets==0.6.0) (0.6.3)\n",
-      "Requirement already satisfied: traitlets>=5.13.0 in /opt/miniconda3/lib/python3.12/site-packages (from ipython->torch_snippets==0.6.0) (5.14.3)\n",
-      "Requirement already satisfied: pexpect>4.3 in /opt/miniconda3/lib/python3.12/site-packages (from ipython->torch_snippets==0.6.0) (4.9.0)\n",
-      "Requirement already satisfied: attrs>=19.2.0 in /opt/miniconda3/lib/python3.12/site-packages (from jsonlines->torch_snippets==0.6.0) (23.2.0)\n",
-      "Requirement already satisfied: contourpy>=1.0.1 in /opt/miniconda3/lib/python3.12/site-packages (from matplotlib->torch_snippets==0.6.0) (1.2.1)\n",
-      "Requirement already satisfied: cycler>=0.10 in /opt/miniconda3/lib/python3.12/site-packages (from matplotlib->torch_snippets==0.6.0) (0.12.1)\n",
-      "Requirement already satisfied: fonttools>=4.22.0 in /opt/miniconda3/lib/python3.12/site-packages (from matplotlib->torch_snippets==0.6.0) (4.53.1)\n",
-      "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/miniconda3/lib/python3.12/site-packages (from matplotlib->torch_snippets==0.6.0) (1.4.5)\n",
-      "Requirement already satisfied: pyparsing>=2.3.1 in /opt/miniconda3/lib/python3.12/site-packages (from matplotlib->torch_snippets==0.6.0) (3.1.2)\n",
-      "Requirement already satisfied: beautifulsoup4 in /opt/miniconda3/lib/python3.12/site-packages (from nbconvert->torch_snippets==0.6.0) (4.12.3)\n",
-      "Requirement already satisfied: bleach!=5.0.0 in /opt/miniconda3/lib/python3.12/site-packages (from nbconvert->torch_snippets==0.6.0) (6.1.0)\n",
-      "Requirement already satisfied: defusedxml in /opt/miniconda3/lib/python3.12/site-packages (from nbconvert->torch_snippets==0.6.0) (0.7.1)\n",
-      "Requirement already satisfied: jupyter-core>=4.7 in /opt/miniconda3/lib/python3.12/site-packages (from nbconvert->torch_snippets==0.6.0) (5.7.2)\n",
-      "Requirement already satisfied: jupyterlab-pygments in /opt/miniconda3/lib/python3.12/site-packages (from nbconvert->torch_snippets==0.6.0) (0.3.0)\n",
-      "Requirement already satisfied: markupsafe>=2.0 in /opt/miniconda3/lib/python3.12/site-packages (from nbconvert->torch_snippets==0.6.0) (2.1.5)\n",
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-      "Requirement already satisfied: nbclient>=0.5.0 in /opt/miniconda3/lib/python3.12/site-packages (from nbconvert->torch_snippets==0.6.0) (0.10.0)\n",
-      "Requirement already satisfied: pandocfilters>=1.4.1 in /opt/miniconda3/lib/python3.12/site-packages (from nbconvert->torch_snippets==0.6.0) (1.5.1)\n",
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-      "Requirement already satisfied: fastjsonschema>=2.15 in /opt/miniconda3/lib/python3.12/site-packages (from nbformat->torch_snippets==0.6.0) (2.20.0)\n",
-      "Requirement already satisfied: click in /opt/miniconda3/lib/python3.12/site-packages (from nltk->torch_snippets==0.6.0) (8.1.7)\n",
-      "Requirement already satisfied: joblib in /opt/miniconda3/lib/python3.12/site-packages (from nltk->torch_snippets==0.6.0) (1.4.2)\n",
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-      "Requirement already satisfied: cfgv>=2.0.0 in /opt/miniconda3/lib/python3.12/site-packages (from pre-commit->torch_snippets==0.6.0) (3.4.0)\n",
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-      "Requirement already satisfied: nodeenv>=0.11.1 in /opt/miniconda3/lib/python3.12/site-packages (from pre-commit->torch_snippets==0.6.0) (1.9.1)\n",
-      "Requirement already satisfied: virtualenv>=20.10.0 in /opt/miniconda3/lib/python3.12/site-packages (from pre-commit->torch_snippets==0.6.0) (20.26.3)\n",
-      "Requirement already satisfied: PyMuPDFb==1.24.9 in /opt/miniconda3/lib/python3.12/site-packages (from pymupdf->torch_snippets==0.6.0) (1.24.9)\n",
-      "Requirement already satisfied: Levenshtein==0.25.1 in /opt/miniconda3/lib/python3.12/site-packages (from python-Levenshtein->torch_snippets==0.6.0) (0.25.1)\n",
-      "Requirement already satisfied: rapidfuzz<4.0.0,>=3.8.0 in /opt/miniconda3/lib/python3.12/site-packages (from Levenshtein==0.25.1->python-Levenshtein->torch_snippets==0.6.0) (3.9.4)\n",
-      "Requirement already satisfied: markdown-it-py>=2.2.0 in /opt/miniconda3/lib/python3.12/site-packages (from rich->torch_snippets==0.6.0) (3.0.0)\n",
-      "Requirement already satisfied: threadpoolctl>=3.1.0 in /opt/miniconda3/lib/python3.12/site-packages (from scikit-learn->torch_snippets==0.6.0) (3.5.0)\n",
-      "Requirement already satisfied: shellingham>=1.3.0 in /opt/miniconda3/lib/python3.12/site-packages (from typer->torch_snippets==0.6.0) (1.5.4)\n",
-      "Requirement already satisfied: webencodings in /opt/miniconda3/lib/python3.12/site-packages (from bleach!=5.0.0->nbconvert->torch_snippets==0.6.0) (0.5.1)\n",
-      "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /opt/miniconda3/lib/python3.12/site-packages (from jedi>=0.16->ipython->torch_snippets==0.6.0) (0.8.4)\n",
-      "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /opt/miniconda3/lib/python3.12/site-packages (from jsonschema>=3.0->altair->torch_snippets==0.6.0) (2023.12.1)\n",
-      "Requirement already satisfied: referencing>=0.28.4 in /opt/miniconda3/lib/python3.12/site-packages (from jsonschema>=3.0->altair->torch_snippets==0.6.0) (0.35.1)\n",
-      "Requirement already satisfied: rpds-py>=0.7.1 in /opt/miniconda3/lib/python3.12/site-packages (from jsonschema>=3.0->altair->torch_snippets==0.6.0) (0.19.0)\n",
-      "Requirement already satisfied: platformdirs>=2.5 in /opt/miniconda3/lib/python3.12/site-packages (from jupyter-core>=4.7->nbconvert->torch_snippets==0.6.0) (3.10.0)\n",
-      "Requirement already satisfied: mdurl~=0.1 in /opt/miniconda3/lib/python3.12/site-packages (from markdown-it-py>=2.2.0->rich->torch_snippets==0.6.0) (0.1.2)\n",
-      "Requirement already satisfied: jupyter-client>=6.1.12 in /opt/miniconda3/lib/python3.12/site-packages (from nbclient>=0.5.0->nbconvert->torch_snippets==0.6.0) (8.6.2)\n",
-      "Requirement already satisfied: ptyprocess>=0.5 in /opt/miniconda3/lib/python3.12/site-packages (from pexpect>4.3->ipython->torch_snippets==0.6.0) (0.7.0)\n",
-      "Requirement already satisfied: wcwidth in /opt/miniconda3/lib/python3.12/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython->torch_snippets==0.6.0) (0.2.13)\n",
-      "Requirement already satisfied: networkx>=2.8 in /opt/miniconda3/lib/python3.12/site-packages (from scikit-image>=0.14.2->imgaug>=0.4.0->torch_snippets==0.6.0) (3.3)\n",
-      "Requirement already satisfied: tifffile>=2022.8.12 in /opt/miniconda3/lib/python3.12/site-packages (from scikit-image>=0.14.2->imgaug>=0.4.0->torch_snippets==0.6.0) (2024.7.24)\n",
-      "Requirement already satisfied: lazy-loader>=0.4 in /opt/miniconda3/lib/python3.12/site-packages (from scikit-image>=0.14.2->imgaug>=0.4.0->torch_snippets==0.6.0) (0.4)\n",
-      "Requirement already satisfied: distlib<1,>=0.3.7 in /opt/miniconda3/lib/python3.12/site-packages (from virtualenv>=20.10.0->pre-commit->torch_snippets==0.6.0) (0.3.8)\n",
-      "Requirement already satisfied: filelock<4,>=3.12.2 in /opt/miniconda3/lib/python3.12/site-packages (from virtualenv>=20.10.0->pre-commit->torch_snippets==0.6.0) (3.15.4)\n",
-      "Requirement already satisfied: soupsieve>1.2 in /opt/miniconda3/lib/python3.12/site-packages (from beautifulsoup4->nbconvert->torch_snippets==0.6.0) (2.5)\n",
-      "Requirement already satisfied: pure-eval in /opt/miniconda3/lib/python3.12/site-packages (from stack-data->ipython->torch_snippets==0.6.0) (0.2.3)\n",
-      "Requirement already satisfied: pyzmq>=23.0 in /opt/miniconda3/lib/python3.12/site-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert->torch_snippets==0.6.0) (26.0.3)\n",
-      "Requirement already satisfied: tornado>=6.2 in /opt/miniconda3/lib/python3.12/site-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert->torch_snippets==0.6.0) (6.4.1)\n",
+      "\u001b[?25hRequirement already satisfied: fastcore in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (1.6.3)\n",
+      "Requirement already satisfied: matplotlib in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (3.9.1)\n",
+      "Requirement already satisfied: Pillow in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (10.4.0)\n",
+      "Requirement already satisfied: altair in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (5.3.0)\n",
+      "Requirement already satisfied: dill in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (0.3.8)\n",
+      "Requirement already satisfied: ipython in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (8.26.0)\n",
+      "Requirement already satisfied: loguru in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (0.7.2)\n",
+      "Requirement already satisfied: numpy in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (2.0.1)\n",
+      "Requirement already satisfied: pandas in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (2.2.2)\n",
+      "Requirement already satisfied: tqdm in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (4.66.4)\n",
+      "Requirement already satisfied: rich in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (13.7.1)\n",
+      "Requirement already satisfied: PyYAML in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (6.0.1)\n",
+      "Requirement already satisfied: catalogue in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (2.0.10)\n",
+      "Requirement already satisfied: confection in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (0.1.5)\n",
+      "Requirement already satisfied: pydantic in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (2.8.2)\n",
+      "Requirement already satisfied: typing in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (3.7.4.3)\n",
+      "Requirement already satisfied: srsly in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (2.4.8)\n",
+      "Requirement already satisfied: typing_extensions in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (4.12.2)\n",
+      "Requirement already satisfied: wasabi in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (1.1.3)\n",
+      "Requirement already satisfied: jsonlines in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (4.0.0)\n",
+      "Requirement already satisfied: imgaug>=0.4.0 in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (0.4.0)\n",
+      "Requirement already satisfied: xmltodict in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (0.13.0)\n",
+      "Requirement already satisfied: fuzzywuzzy in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (0.18.0)\n",
+      "Requirement already satisfied: scikit-learn in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (1.5.1)\n",
+      "Requirement already satisfied: nltk in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (3.8.1)\n",
+      "Requirement already satisfied: python-Levenshtein in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (0.25.1)\n",
+      "Requirement already satisfied: pre-commit in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (3.7.1)\n",
+      "Requirement already satisfied: pymupdf in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (1.24.9)\n",
+      "Requirement already satisfied: nbconvert in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (7.16.4)\n",
+      "Requirement already satisfied: nbformat in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (5.10.4)\n",
+      "Requirement already satisfied: icecream in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (2.1.3)\n",
+      "Requirement already satisfied: mergedeep in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (1.3.4)\n",
+      "Requirement already satisfied: typer in /opt/miniconda3/lib/python3.12/site-packages (from torch_snippets==0.540) (0.12.4)\n",
+      "Requirement already satisfied: six in /opt/miniconda3/lib/python3.12/site-packages (from imgaug>=0.4.0->torch_snippets==0.540) (1.16.0)\n",
+      "Requirement already satisfied: scipy in /opt/miniconda3/lib/python3.12/site-packages (from imgaug>=0.4.0->torch_snippets==0.540) (1.14.0)\n",
+      "Requirement already satisfied: scikit-image>=0.14.2 in /opt/miniconda3/lib/python3.12/site-packages (from imgaug>=0.4.0->torch_snippets==0.540) (0.24.0)\n",
+      "Requirement already satisfied: opencv-python in /opt/miniconda3/lib/python3.12/site-packages (from imgaug>=0.4.0->torch_snippets==0.540) (4.10.0.84)\n",
+      "Requirement already satisfied: imageio in /opt/miniconda3/lib/python3.12/site-packages (from imgaug>=0.4.0->torch_snippets==0.540) (2.34.2)\n",
+      "Requirement already satisfied: Shapely in /opt/miniconda3/lib/python3.12/site-packages (from imgaug>=0.4.0->torch_snippets==0.540) (2.0.5)\n",
+      "Requirement already satisfied: jinja2 in /opt/miniconda3/lib/python3.12/site-packages (from altair->torch_snippets==0.540) (3.1.4)\n",
+      "Requirement already satisfied: jsonschema>=3.0 in /opt/miniconda3/lib/python3.12/site-packages (from altair->torch_snippets==0.540) (4.23.0)\n",
+      "Requirement already satisfied: packaging in /opt/miniconda3/lib/python3.12/site-packages (from altair->torch_snippets==0.540) (23.2)\n",
+      "Requirement already satisfied: toolz in /opt/miniconda3/lib/python3.12/site-packages (from altair->torch_snippets==0.540) (0.12.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/miniconda3/lib/python3.12/site-packages (from pandas->torch_snippets==0.540) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /opt/miniconda3/lib/python3.12/site-packages (from pandas->torch_snippets==0.540) (2024.1)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /opt/miniconda3/lib/python3.12/site-packages (from pandas->torch_snippets==0.540) (2024.1)\n",
+      "Requirement already satisfied: annotated-types>=0.4.0 in /opt/miniconda3/lib/python3.12/site-packages (from pydantic->torch_snippets==0.540) (0.7.0)\n",
+      "Requirement already satisfied: pydantic-core==2.20.1 in /opt/miniconda3/lib/python3.12/site-packages (from pydantic->torch_snippets==0.540) (2.20.1)\n",
+      "Requirement already satisfied: colorama>=0.3.9 in /opt/miniconda3/lib/python3.12/site-packages (from icecream->torch_snippets==0.540) (0.4.6)\n",
+      "Requirement already satisfied: pygments>=2.2.0 in /opt/miniconda3/lib/python3.12/site-packages (from icecream->torch_snippets==0.540) (2.18.0)\n",
+      "Requirement already satisfied: executing>=0.3.1 in /opt/miniconda3/lib/python3.12/site-packages (from icecream->torch_snippets==0.540) (2.0.1)\n",
+      "Requirement already satisfied: asttokens>=2.0.1 in /opt/miniconda3/lib/python3.12/site-packages (from icecream->torch_snippets==0.540) (2.4.1)\n",
+      "Requirement already satisfied: decorator in /opt/miniconda3/lib/python3.12/site-packages (from ipython->torch_snippets==0.540) (5.1.1)\n",
+      "Requirement already satisfied: jedi>=0.16 in /opt/miniconda3/lib/python3.12/site-packages (from ipython->torch_snippets==0.540) (0.19.1)\n",
+      "Requirement already satisfied: matplotlib-inline in /opt/miniconda3/lib/python3.12/site-packages (from ipython->torch_snippets==0.540) (0.1.7)\n",
+      "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /opt/miniconda3/lib/python3.12/site-packages (from ipython->torch_snippets==0.540) (3.0.47)\n",
+      "Requirement already satisfied: stack-data in /opt/miniconda3/lib/python3.12/site-packages (from ipython->torch_snippets==0.540) (0.6.3)\n",
+      "Requirement already satisfied: traitlets>=5.13.0 in /opt/miniconda3/lib/python3.12/site-packages (from ipython->torch_snippets==0.540) (5.14.3)\n",
+      "Requirement already satisfied: pexpect>4.3 in /opt/miniconda3/lib/python3.12/site-packages (from ipython->torch_snippets==0.540) (4.9.0)\n",
+      "Requirement already satisfied: attrs>=19.2.0 in /opt/miniconda3/lib/python3.12/site-packages (from jsonlines->torch_snippets==0.540) (23.2.0)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in /opt/miniconda3/lib/python3.12/site-packages (from matplotlib->torch_snippets==0.540) (1.2.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in /opt/miniconda3/lib/python3.12/site-packages (from matplotlib->torch_snippets==0.540) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /opt/miniconda3/lib/python3.12/site-packages (from matplotlib->torch_snippets==0.540) (4.53.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/miniconda3/lib/python3.12/site-packages (from matplotlib->torch_snippets==0.540) (1.4.5)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in /opt/miniconda3/lib/python3.12/site-packages (from matplotlib->torch_snippets==0.540) (3.1.2)\n",
+      "Requirement already satisfied: beautifulsoup4 in /opt/miniconda3/lib/python3.12/site-packages (from nbconvert->torch_snippets==0.540) (4.12.3)\n",
+      "Requirement already satisfied: bleach!=5.0.0 in /opt/miniconda3/lib/python3.12/site-packages (from nbconvert->torch_snippets==0.540) (6.1.0)\n",
+      "Requirement already satisfied: defusedxml in /opt/miniconda3/lib/python3.12/site-packages (from nbconvert->torch_snippets==0.540) (0.7.1)\n",
+      "Requirement already satisfied: jupyter-core>=4.7 in /opt/miniconda3/lib/python3.12/site-packages (from nbconvert->torch_snippets==0.540) (5.7.2)\n",
+      "Requirement already satisfied: jupyterlab-pygments in /opt/miniconda3/lib/python3.12/site-packages (from nbconvert->torch_snippets==0.540) (0.3.0)\n",
+      "Requirement already satisfied: markupsafe>=2.0 in /opt/miniconda3/lib/python3.12/site-packages (from nbconvert->torch_snippets==0.540) (2.1.5)\n",
+      "Requirement already satisfied: mistune<4,>=2.0.3 in /opt/miniconda3/lib/python3.12/site-packages (from nbconvert->torch_snippets==0.540) (3.0.2)\n",
+      "Requirement already satisfied: nbclient>=0.5.0 in /opt/miniconda3/lib/python3.12/site-packages (from nbconvert->torch_snippets==0.540) (0.10.0)\n",
+      "Requirement already satisfied: pandocfilters>=1.4.1 in /opt/miniconda3/lib/python3.12/site-packages (from nbconvert->torch_snippets==0.540) (1.5.1)\n",
+      "Requirement already satisfied: tinycss2 in /opt/miniconda3/lib/python3.12/site-packages (from nbconvert->torch_snippets==0.540) (1.3.0)\n",
+      "Requirement already satisfied: fastjsonschema>=2.15 in /opt/miniconda3/lib/python3.12/site-packages (from nbformat->torch_snippets==0.540) (2.20.0)\n",
+      "Requirement already satisfied: click in /opt/miniconda3/lib/python3.12/site-packages (from nltk->torch_snippets==0.540) (8.1.7)\n",
+      "Requirement already satisfied: joblib in /opt/miniconda3/lib/python3.12/site-packages (from nltk->torch_snippets==0.540) (1.4.2)\n",
+      "Requirement already satisfied: regex>=2021.8.3 in /opt/miniconda3/lib/python3.12/site-packages (from nltk->torch_snippets==0.540) (2024.7.24)\n",
+      "Requirement already satisfied: cfgv>=2.0.0 in /opt/miniconda3/lib/python3.12/site-packages (from pre-commit->torch_snippets==0.540) (3.4.0)\n",
+      "Requirement already satisfied: identify>=1.0.0 in /opt/miniconda3/lib/python3.12/site-packages (from pre-commit->torch_snippets==0.540) (2.6.0)\n",
+      "Requirement already satisfied: nodeenv>=0.11.1 in /opt/miniconda3/lib/python3.12/site-packages (from pre-commit->torch_snippets==0.540) (1.9.1)\n",
+      "Requirement already satisfied: virtualenv>=20.10.0 in /opt/miniconda3/lib/python3.12/site-packages (from pre-commit->torch_snippets==0.540) (20.26.3)\n",
+      "Requirement already satisfied: PyMuPDFb==1.24.9 in /opt/miniconda3/lib/python3.12/site-packages (from pymupdf->torch_snippets==0.540) (1.24.9)\n",
+      "Requirement already satisfied: Levenshtein==0.25.1 in /opt/miniconda3/lib/python3.12/site-packages (from python-Levenshtein->torch_snippets==0.540) (0.25.1)\n",
+      "Requirement already satisfied: rapidfuzz<4.0.0,>=3.8.0 in /opt/miniconda3/lib/python3.12/site-packages (from Levenshtein==0.25.1->python-Levenshtein->torch_snippets==0.540) (3.9.4)\n",
+      "Requirement already satisfied: markdown-it-py>=2.2.0 in /opt/miniconda3/lib/python3.12/site-packages (from rich->torch_snippets==0.540) (3.0.0)\n",
+      "Requirement already satisfied: threadpoolctl>=3.1.0 in /opt/miniconda3/lib/python3.12/site-packages (from scikit-learn->torch_snippets==0.540) (3.5.0)\n",
+      "Requirement already satisfied: shellingham>=1.3.0 in /opt/miniconda3/lib/python3.12/site-packages (from typer->torch_snippets==0.540) (1.5.4)\n",
+      "Requirement already satisfied: webencodings in /opt/miniconda3/lib/python3.12/site-packages (from bleach!=5.0.0->nbconvert->torch_snippets==0.540) (0.5.1)\n",
+      "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /opt/miniconda3/lib/python3.12/site-packages (from jedi>=0.16->ipython->torch_snippets==0.540) (0.8.4)\n",
+      "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /opt/miniconda3/lib/python3.12/site-packages (from jsonschema>=3.0->altair->torch_snippets==0.540) (2023.12.1)\n",
+      "Requirement already satisfied: referencing>=0.28.4 in /opt/miniconda3/lib/python3.12/site-packages (from jsonschema>=3.0->altair->torch_snippets==0.540) (0.35.1)\n",
+      "Requirement already satisfied: rpds-py>=0.7.1 in /opt/miniconda3/lib/python3.12/site-packages (from jsonschema>=3.0->altair->torch_snippets==0.540) (0.19.0)\n",
+      "Requirement already satisfied: platformdirs>=2.5 in /opt/miniconda3/lib/python3.12/site-packages (from jupyter-core>=4.7->nbconvert->torch_snippets==0.540) (3.10.0)\n",
+      "Requirement already satisfied: mdurl~=0.1 in /opt/miniconda3/lib/python3.12/site-packages (from markdown-it-py>=2.2.0->rich->torch_snippets==0.540) (0.1.2)\n",
+      "Requirement already satisfied: jupyter-client>=6.1.12 in /opt/miniconda3/lib/python3.12/site-packages (from nbclient>=0.5.0->nbconvert->torch_snippets==0.540) (8.6.2)\n",
+      "Requirement already satisfied: ptyprocess>=0.5 in /opt/miniconda3/lib/python3.12/site-packages (from pexpect>4.3->ipython->torch_snippets==0.540) (0.7.0)\n",
+      "Requirement already satisfied: wcwidth in /opt/miniconda3/lib/python3.12/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython->torch_snippets==0.540) (0.2.13)\n",
+      "Requirement already satisfied: networkx>=2.8 in /opt/miniconda3/lib/python3.12/site-packages (from scikit-image>=0.14.2->imgaug>=0.4.0->torch_snippets==0.540) (3.3)\n",
+      "Requirement already satisfied: tifffile>=2022.8.12 in /opt/miniconda3/lib/python3.12/site-packages (from scikit-image>=0.14.2->imgaug>=0.4.0->torch_snippets==0.540) (2024.7.24)\n",
+      "Requirement already satisfied: lazy-loader>=0.4 in /opt/miniconda3/lib/python3.12/site-packages (from scikit-image>=0.14.2->imgaug>=0.4.0->torch_snippets==0.540) (0.4)\n",
+      "Requirement already satisfied: distlib<1,>=0.3.7 in /opt/miniconda3/lib/python3.12/site-packages (from virtualenv>=20.10.0->pre-commit->torch_snippets==0.540) (0.3.8)\n",
+      "Requirement already satisfied: filelock<4,>=3.12.2 in /opt/miniconda3/lib/python3.12/site-packages (from virtualenv>=20.10.0->pre-commit->torch_snippets==0.540) (3.15.4)\n",
+      "Requirement already satisfied: soupsieve>1.2 in /opt/miniconda3/lib/python3.12/site-packages (from beautifulsoup4->nbconvert->torch_snippets==0.540) (2.5)\n",
+      "Requirement already satisfied: pure-eval in /opt/miniconda3/lib/python3.12/site-packages (from stack-data->ipython->torch_snippets==0.540) (0.2.3)\n",
+      "Requirement already satisfied: pyzmq>=23.0 in /opt/miniconda3/lib/python3.12/site-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert->torch_snippets==0.540) (26.0.3)\n",
+      "Requirement already satisfied: tornado>=6.2 in /opt/miniconda3/lib/python3.12/site-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert->torch_snippets==0.540) (6.4.1)\n",
       "Installing collected packages: torch_snippets\n",
       "  Attempting uninstall: torch_snippets\n",
-      "    Found existing installation: torch_snippets 0.6.0\n",
-      "    Uninstalling torch_snippets-0.6.0:\n",
-      "      Successfully uninstalled torch_snippets-0.6.0\n",
+      "    Found existing installation: torch_snippets 0.539\n",
+      "    Uninstalling torch_snippets-0.539:\n",
+      "      Successfully uninstalled torch_snippets-0.539\n",
       "  Running setup.py develop for torch_snippets\n",
-      "Successfully installed torch_snippets-0.6.0\n",
+      "Successfully installed torch_snippets-0.540\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -164,17 +164,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
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        "
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+       "\u001b[2;36m[08/24/24 12:50:22]\u001b[0m\u001b[2;36m \u001b[0m\u001b[2;33mINFO    \u001b[0m loaded \u001b[1;36m26\u001b[0m lines                                                                                                        \u001b]8;id=282456;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_28891/4135304039.py:3\u001b\\\u001b[2m4135304039.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=204178;file:///var/folders/1_/71dqv9vx2750gmyz77q_f45w0000gn/T/ipykernel_28891/4135304039.py:3#:3\u001b\\\u001b[2m:3\u001b[0m\u001b]8;;\u001b\\\n"
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      "metadata": {},
@@ -183,10 +183,10 @@
     {
      "data": {
       "text/plain": [
-       "'0.6.0'"
+       "'0.540'"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -239,72 +239,72 @@
       "adding license file 'LICENSE.txt'\n",
       "writing manifest file 'torch_snippets.egg-info/SOURCES.txt'\n",
       "running check\n",
-      "creating torch_snippets-0.6.0\n",
-      "creating torch_snippets-0.6.0/torch_snippets\n",
-      "creating torch_snippets-0.6.0/torch_snippets.egg-info\n",
-      "creating torch_snippets-0.6.0/torch_snippets/thinc_parser\n",
-      "creating torch_snippets-0.6.0/torch_snippets/trainer\n",
-      "copying files to torch_snippets-0.6.0...\n",
-      "copying LICENSE -> torch_snippets-0.6.0\n",
-      "copying LICENSE.txt -> torch_snippets-0.6.0\n",
-      "copying MANIFEST.in -> torch_snippets-0.6.0\n",
-      "copying README.md -> torch_snippets-0.6.0\n",
-      "copying settings.ini -> torch_snippets-0.6.0\n",
-      "copying setup.cfg -> torch_snippets-0.6.0\n",
-      "copying setup.py -> torch_snippets-0.6.0\n",
-      "copying torch_snippets/__init__.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/__module_timing__.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/_modidx.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/_nbdev.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/adapters.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/bb_utils.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/bokeh_loader.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/charts.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/cli.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/dates.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/decorators.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/fastcores.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/icecream.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/imgaug_loader.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/inspector.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/interactive_show.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/ipython.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/load_defaults.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/loader.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/logger.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/markup.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/markup2.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/misc.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/paths.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/pdf_loader.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/registry.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/s3_loader.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/scp.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/sklegos.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/text_utils.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/tmp.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/torch_loader.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/video.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets/zen.py -> torch_snippets-0.6.0/torch_snippets\n",
-      "copying torch_snippets.egg-info/PKG-INFO -> torch_snippets-0.6.0/torch_snippets.egg-info\n",
-      "copying torch_snippets.egg-info/SOURCES.txt -> torch_snippets-0.6.0/torch_snippets.egg-info\n",
-      "copying torch_snippets.egg-info/dependency_links.txt -> torch_snippets-0.6.0/torch_snippets.egg-info\n",
-      "copying torch_snippets.egg-info/entry_points.txt -> torch_snippets-0.6.0/torch_snippets.egg-info\n",
-      "copying torch_snippets.egg-info/not-zip-safe -> torch_snippets-0.6.0/torch_snippets.egg-info\n",
-      "copying torch_snippets.egg-info/requires.txt -> torch_snippets-0.6.0/torch_snippets.egg-info\n",
-      "copying torch_snippets.egg-info/top_level.txt -> torch_snippets-0.6.0/torch_snippets.egg-info\n",
-      "copying torch_snippets/thinc_parser/__init__.py -> torch_snippets-0.6.0/torch_snippets/thinc_parser\n",
-      "copying torch_snippets/thinc_parser/parser.py -> torch_snippets-0.6.0/torch_snippets/thinc_parser\n",
-      "copying torch_snippets/trainer/__init__.py -> torch_snippets-0.6.0/torch_snippets/trainer\n",
-      "copying torch_snippets/trainer/capsule.py -> torch_snippets-0.6.0/torch_snippets/trainer\n",
-      "copying torch_snippets/trainer/config.py -> torch_snippets-0.6.0/torch_snippets/trainer\n",
-      "copying torch_snippets/trainer/hooks.py -> torch_snippets-0.6.0/torch_snippets/trainer\n",
-      "copying torch_snippets/trainer/neural_graph.py -> torch_snippets-0.6.0/torch_snippets/trainer\n",
-      "copying torch_snippets.egg-info/SOURCES.txt -> torch_snippets-0.6.0/torch_snippets.egg-info\n",
-      "Writing torch_snippets-0.6.0/setup.cfg\n",
+      "creating torch_snippets-0.540\n",
+      "creating torch_snippets-0.540/torch_snippets\n",
+      "creating torch_snippets-0.540/torch_snippets.egg-info\n",
+      "creating torch_snippets-0.540/torch_snippets/thinc_parser\n",
+      "creating torch_snippets-0.540/torch_snippets/trainer\n",
+      "copying files to torch_snippets-0.540...\n",
+      "copying LICENSE -> torch_snippets-0.540\n",
+      "copying LICENSE.txt -> torch_snippets-0.540\n",
+      "copying MANIFEST.in -> torch_snippets-0.540\n",
+      "copying README.md -> torch_snippets-0.540\n",
+      "copying settings.ini -> torch_snippets-0.540\n",
+      "copying setup.cfg -> torch_snippets-0.540\n",
+      "copying setup.py -> torch_snippets-0.540\n",
+      "copying torch_snippets/__init__.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/__module_timing__.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/_modidx.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/_nbdev.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/adapters.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/bb_utils.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/bokeh_loader.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/charts.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/cli.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/dates.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/decorators.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/fastcores.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/icecream.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/imgaug_loader.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/inspector.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/interactive_show.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/ipython.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/load_defaults.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/loader.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/logger.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/markup.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/markup2.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/misc.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/paths.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/pdf_loader.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/registry.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/s3_loader.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/scp.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/sklegos.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/text_utils.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/tmp.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/torch_loader.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/video.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets/zen.py -> torch_snippets-0.540/torch_snippets\n",
+      "copying torch_snippets.egg-info/PKG-INFO -> torch_snippets-0.540/torch_snippets.egg-info\n",
+      "copying torch_snippets.egg-info/SOURCES.txt -> torch_snippets-0.540/torch_snippets.egg-info\n",
+      "copying torch_snippets.egg-info/dependency_links.txt -> torch_snippets-0.540/torch_snippets.egg-info\n",
+      "copying torch_snippets.egg-info/entry_points.txt -> torch_snippets-0.540/torch_snippets.egg-info\n",
+      "copying torch_snippets.egg-info/not-zip-safe -> torch_snippets-0.540/torch_snippets.egg-info\n",
+      "copying torch_snippets.egg-info/requires.txt -> torch_snippets-0.540/torch_snippets.egg-info\n",
+      "copying torch_snippets.egg-info/top_level.txt -> torch_snippets-0.540/torch_snippets.egg-info\n",
+      "copying torch_snippets/thinc_parser/__init__.py -> torch_snippets-0.540/torch_snippets/thinc_parser\n",
+      "copying torch_snippets/thinc_parser/parser.py -> torch_snippets-0.540/torch_snippets/thinc_parser\n",
+      "copying torch_snippets/trainer/__init__.py -> torch_snippets-0.540/torch_snippets/trainer\n",
+      "copying torch_snippets/trainer/capsule.py -> torch_snippets-0.540/torch_snippets/trainer\n",
+      "copying torch_snippets/trainer/config.py -> torch_snippets-0.540/torch_snippets/trainer\n",
+      "copying torch_snippets/trainer/hooks.py -> torch_snippets-0.540/torch_snippets/trainer\n",
+      "copying torch_snippets/trainer/neural_graph.py -> torch_snippets-0.540/torch_snippets/trainer\n",
+      "copying torch_snippets.egg-info/SOURCES.txt -> torch_snippets-0.540/torch_snippets.egg-info\n",
+      "Writing torch_snippets-0.540/setup.cfg\n",
       "creating dist\n",
       "Creating tar archive\n",
-      "removing 'torch_snippets-0.6.0' (and everything under it)\n",
+      "removing 'torch_snippets-0.540' (and everything under it)\n",
       "running bdist_wheel\n",
       "running build\n",
       "running build_py\n",
@@ -417,10 +417,10 @@
       "copying build/lib/torch_snippets/adapters.py -> build/bdist.macosx-11.1-arm64/wheel/torch_snippets\n",
       "copying build/lib/torch_snippets/decorators.py -> build/bdist.macosx-11.1-arm64/wheel/torch_snippets\n",
       "running install_egg_info\n",
-      "Copying torch_snippets.egg-info to build/bdist.macosx-11.1-arm64/wheel/torch_snippets-0.6.0-py3.12.egg-info\n",
+      "Copying torch_snippets.egg-info to build/bdist.macosx-11.1-arm64/wheel/torch_snippets-0.540-py3.12.egg-info\n",
       "running install_scripts\n",
-      "creating build/bdist.macosx-11.1-arm64/wheel/torch_snippets-0.6.0.dist-info/WHEEL\n",
-      "creating 'dist/torch_snippets-0.6.0-py3-none-any.whl' and adding 'build/bdist.macosx-11.1-arm64/wheel' to it\n",
+      "creating build/bdist.macosx-11.1-arm64/wheel/torch_snippets-0.540.dist-info/WHEEL\n",
+      "creating 'dist/torch_snippets-0.540-py3-none-any.whl' and adding 'build/bdist.macosx-11.1-arm64/wheel' to it\n",
       "adding 'torch_snippets/__init__.py'\n",
       "adding 'torch_snippets/__module_timing__.py'\n",
       "adding 'torch_snippets/_modidx.py'\n",
@@ -462,22 +462,22 @@
       "adding 'torch_snippets/trainer/config.py'\n",
       "adding 'torch_snippets/trainer/hooks.py'\n",
       "adding 'torch_snippets/trainer/neural_graph.py'\n",
-      "adding 'torch_snippets-0.6.0.dist-info/LICENSE'\n",
-      "adding 'torch_snippets-0.6.0.dist-info/LICENSE.txt'\n",
-      "adding 'torch_snippets-0.6.0.dist-info/METADATA'\n",
-      "adding 'torch_snippets-0.6.0.dist-info/WHEEL'\n",
-      "adding 'torch_snippets-0.6.0.dist-info/entry_points.txt'\n",
-      "adding 'torch_snippets-0.6.0.dist-info/top_level.txt'\n",
-      "adding 'torch_snippets-0.6.0.dist-info/RECORD'\n",
+      "adding 'torch_snippets-0.540.dist-info/LICENSE'\n",
+      "adding 'torch_snippets-0.540.dist-info/LICENSE.txt'\n",
+      "adding 'torch_snippets-0.540.dist-info/METADATA'\n",
+      "adding 'torch_snippets-0.540.dist-info/WHEEL'\n",
+      "adding 'torch_snippets-0.540.dist-info/entry_points.txt'\n",
+      "adding 'torch_snippets-0.540.dist-info/top_level.txt'\n",
+      "adding 'torch_snippets-0.540.dist-info/RECORD'\n",
       "removing build/bdist.macosx-11.1-arm64/wheel\n",
       "Uploading distributions to https://upload.pypi.org/legacy/\n",
-      "Uploading torch_snippets-0.6.0-py3-none-any.whl\n",
-      "\u001b[2K\u001b[35m100%\u001b[0m \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.9/110.9 kB\u001b[0m • \u001b[33m00:00\u001b[0m • \u001b[31m216.3 MB/s\u001b[0m\n",
-      "\u001b[?25hUploading torch_snippets-0.6.0.tar.gz\n",
-      "\u001b[2K\u001b[35m100%\u001b[0m \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m97.2/97.2 kB\u001b[0m • \u001b[33m00:00\u001b[0m • \u001b[31m150.9 MB/s\u001b[0m\n",
+      "Uploading torch_snippets-0.540-py3-none-any.whl\n",
+      "\u001b[2K\u001b[35m100%\u001b[0m \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m111.2/111.2 kB\u001b[0m • \u001b[33m00:01\u001b[0m • \u001b[31m380.4 MB/s\u001b[0m\n",
+      "\u001b[?25hUploading torch_snippets-0.540.tar.gz\n",
+      "\u001b[2K\u001b[35m100%\u001b[0m \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m97.4/97.4 kB\u001b[0m • \u001b[33m00:00\u001b[0m • \u001b[31m156.6 MB/s\u001b[0m\n",
       "\u001b[?25h\n",
       "\u001b[32mView at:\u001b[0m\n",
-      "https://pypi.org/project/torch-snippets/0.6.0/\n"
+      "https://pypi.org/project/torch-snippets/0.540/\n"
      ]
     }
    ],
@@ -488,7 +488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -520,6 +520,19 @@
       "  warn(f'Found cells containing imports and other code. See FAQ.\\n---\\n{cell.source}\\n---\\n')\n",
       "/opt/miniconda3/lib/python3.12/site-packages/nbdev/processors.py:222: UserWarning: Found cells containing imports and other code. See FAQ.\n",
       "---\n",
+      "try:\n",
+      "    import torch\n",
+      "\n",
+      "    d = AD(a=torch.Tensor([1, 2, 3]), b=\"hello\")\n",
+      "    write_json(d, \"/tmp/tmp.json\")\n",
+      "    print(\"\\n\".join(readlines(\"/tmp/tmp.json\")))\n",
+      "except ModuleNotFoundError:\n",
+      "    ...\n",
+      "---\n",
+      "\n",
+      "  warn(f'Found cells containing imports and other code. See FAQ.\\n---\\n{cell.source}\\n---\\n')\n",
+      "/opt/miniconda3/lib/python3.12/site-packages/nbdev/processors.py:222: UserWarning: Found cells containing imports and other code. See FAQ.\n",
+      "---\n",
       "@timeit\n",
       "@io\n",
       "def foo(a, b):\n",
@@ -545,17 +558,27 @@
       "  warn(f'Found cells containing imports and other code. See FAQ.\\n---\\n{cell.source}\\n---\\n')\n",
       "/opt/miniconda3/lib/python3.12/site-packages/nbdev/processors.py:222: UserWarning: Found cells containing imports and other code. See FAQ.\n",
       "---\n",
-      "try:\n",
-      "    import torch\n",
+      "# https://youtu.be/XcU-a-eksMA\n",
+      "from IPython.display import YouTubeVideo\n",
       "\n",
-      "    d = AD(a=torch.Tensor([1, 2, 3]), b=\"hello\")\n",
-      "    write_json(d, \"/tmp/tmp.json\")\n",
-      "    print(\"\\n\".join(readlines(\"/tmp/tmp.json\")))\n",
-      "except ModuleNotFoundError:\n",
-      "    ...\n",
+      "# Replace 'video_id' with the actual YouTube video ID\n",
+      "YouTubeVideo('XcU-a-eksMA', width=560, height=315)\n",
+      "---\n",
+      "\n",
+      "  warn(f'Found cells containing imports and other code. See FAQ.\\n---\\n{cell.source}\\n---\\n')\n",
+      "/opt/miniconda3/lib/python3.12/site-packages/nbdev/processors.py:222: UserWarning: Found cells containing imports and other code. See FAQ.\n",
+      "---\n",
+      "def do(im_path):\n",
+      "    from torch_snippets import show, read\n",
+      "    im = read(im_path)\n",
+      "    show(im, sz=3)\n",
+      "    print(im.mean())\n",
+      "\n",
+      "do('../assets/Preamble.png')\n",
       "---\n",
       "\n",
       "  warn(f'Found cells containing imports and other code. See FAQ.\\n---\\n{cell.source}\\n---\\n')\n",
+      "[ WARN:0@0.009] global loadsave.cpp:241 findDecoder imread_('../assets/Preamble.png'): can't open/read file: check file path/integrity\n",
       "/opt/miniconda3/lib/python3.12/site-packages/nbdev/processors.py:222: UserWarning: Found cells containing imports and other code. See FAQ.\n",
       "---\n",
       "import random\n",
@@ -583,10 +606,9 @@
       "import random\n",
       "\n",
       "random.seed(10)\n",
-      "errors = []\n",
       "\n",
       "\n",
-      "@tryy(silence_errors=True, store_errors=errors, output_to_return_on_fail=\"😔\")\n",
+      "@tryy(silence_errors=True)\n",
       "def do(a, b, c):\n",
       "    if c < 50:\n",
       "        return 1 / 0\n",
@@ -636,7 +658,7 @@
       "  warn(f'Found cells containing imports and other code. See FAQ.\\n---\\n{cell.source}\\n---\\n')\n",
       "/opt/miniconda3/lib/python3.12/site-packages/nbdev/processors.py:222: UserWarning: Found cells containing imports and other code. See FAQ.\n",
       "---\n",
-      "from torch_snippets.inspector import inspect\n",
+      "from torch_snippets.markup2 import AD\n",
       "from torch.utils.data import TensorDataset\n",
       "\n",
       "trn_ds = TensorDataset(*[torch.Tensor(i) for i in [X_trn, y_trn]])\n",
@@ -645,7 +667,67 @@
       "val_ds = TensorDataset(*[torch.Tensor(i) for i in [X_val, y_val]])\n",
       "val_dl = DataLoader(val_ds, batch_size=32)\n",
       "\n",
-      "inspect(next(iter(val_dl)))\n",
+      "AD(next(iter(val_dl)))\n",
+      "---\n",
+      "\n",
+      "  warn(f'Found cells containing imports and other code. See FAQ.\\n---\\n{cell.source}\\n---\\n')\n",
+      "/opt/miniconda3/lib/python3.12/site-packages/nbdev/processors.py:222: UserWarning: Found cells containing imports and other code. See FAQ.\n",
+      "---\n",
+      "from torch_snippets.trainer.capsule import Capsule, train, validate, predict\n",
+      "\n",
+      "\n",
+      "class IrisModel(Capsule):\n",
+      "    def __init__(self):\n",
+      "        super().__init__()\n",
+      "        self.model = nn.Sequential(\n",
+      "            nn.BatchNorm1d(4),\n",
+      "            nn.Linear(4, 16),\n",
+      "            nn.Dropout(0.2),\n",
+      "            nn.BatchNorm1d(16),\n",
+      "            nn.ReLU(inplace=True),\n",
+      "            nn.Linear(16, 8),\n",
+      "            nn.Dropout(0.2),\n",
+      "            nn.BatchNorm1d(8),\n",
+      "            nn.ReLU(inplace=True),\n",
+      "            nn.Linear(8, 3),\n",
+      "        )\n",
+      "        self.loss_fn = nn.CrossEntropyLoss()\n",
+      "        self.optimizer = optim.Adam(self.parameters())\n",
+      "\n",
+      "    def forward(self, x):\n",
+      "        return self.model(x)\n",
+      "\n",
+      "    @train\n",
+      "    def train_batch(self, batch):\n",
+      "        x, y = batch\n",
+      "        _y = self.forward(x)\n",
+      "        loss = self.loss_fn(_y, y.long())\n",
+      "        return {\"loss\": loss}\n",
+      "\n",
+      "    @validate\n",
+      "    def validate_batch(self, batch=None, dl=None):\n",
+      "        if dl is not None:\n",
+      "            output = []\n",
+      "            for batch in dl:\n",
+      "                output.extend(self.predict(batch=batch)[\"val_acc\"])\n",
+      "            return np.mean(output)\n",
+      "        x, y = batch\n",
+      "        _y = self.forward(x)\n",
+      "        loss = self.loss_fn(_y, y.long())\n",
+      "        acc = (y == _y.max(-1)[1]).float().mean()\n",
+      "        return {\"val_loss\": loss, \"val_acc\": acc}\n",
+      "\n",
+      "    @predict\n",
+      "    def predict(self, batch=None, dl=None):\n",
+      "        if dl is not None:\n",
+      "            output = []\n",
+      "            for batch in dl:\n",
+      "                output.extend(self.predict(batch=batch))\n",
+      "            return output\n",
+      "        x, y = batch\n",
+      "        _y = self.forward(x)\n",
+      "        o = _y.max(-1)[1].cpu().detach().numpy().tolist()\n",
+      "        return o\n",
       "---\n",
       "\n",
       "  warn(f'Found cells containing imports and other code. See FAQ.\\n---\\n{cell.source}\\n---\\n')\n",
@@ -654,12 +736,15 @@
       "Traceback (most recent call last):\n",
       "  File \"/opt/miniconda3/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3577, in run_code\n",
       "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
-      "  File \"\", line 3, in \n",
-      "    spider(\n",
-      "  File \"\", line 67, in spider\n",
-      "    fig, ax = plt.subplots(figsize=(sz, sz), subplot_kw=dict(polar=True))\n",
-      "              ^^^\n",
-      "NameError: name 'plt' is not defined. Did you mean: 'alt'?\n",
+      "  File \"\", line 7, in \n",
+      "    do('../assets/Preamble.png')\n",
+      "  File \"\", line 3, in do\n",
+      "    im = read(im_path)\n",
+      "         ^^^^^^^^^^^^^\n",
+      "  File \"/Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/loader.py\", line 233, in read\n",
+      "    img = img[..., ::-1]  # BGR to RGB\n",
+      "          ~~~^^^^^^^^^^^\n",
+      "TypeError: 'NoneType' object is not subscriptable\n",
       "\n",
       "The above exception was the direct cause of the following exception:\n",
       "\n",
@@ -692,24 +777,14 @@
       "                                                                    ^^^^^^^^^^\n",
       "  File \"/opt/miniconda3/lib/python3.12/site-packages/nbdev/processors.py\", line 241, in __call__\n",
       "    raise Exception(f\"Error{' in notebook: '+title if title else ''} in cell {cell.idx_} :\\n{cell.source}\") from self.k.exc[1]\n",
-      "Exception: Error in cell 15 :\n",
-      "import pandas as pd\n",
-      "\n",
-      "spider(\n",
-      "    pd.DataFrame(\n",
-      "        {\n",
-      "            \"x\": [*\"abcde\"],\n",
-      "            \"c1\": [10, 11, 12, 13, 14],\n",
-      "            \"c2\": [0.1, 0.3, 0.4, 0.1, 0.9],\n",
-      "            \"c3\": [1e5, 2e5, 3.5e5, 8e4, 5e4],\n",
-      "            \"c4\": [9, 12, 5, 2, 0.2],\n",
-      "            \"test\": [1, 1, 1, 1, 5],\n",
-      "        },\n",
-      "        index=[*\"abcde\"],\n",
-      "    ),\n",
-      "    title=\"Sample Spider\",\n",
-      "    padding=1.1,\n",
-      ")\n",
+      "Exception: Error in notebook: Rich Logging and printing in cell 23 :\n",
+      "def do(im_path):\n",
+      "    from torch_snippets import show, read\n",
+      "    im = read(im_path)\n",
+      "    show(im, sz=3)\n",
+      "    print(im.mean())\n",
+      "\n",
+      "do('../assets/Preamble.png')\n",
       "\"\"\"\n",
       "\n",
       "The above exception was the direct cause of the following exception:\n",
@@ -754,24 +829,14 @@
       "           ^^^^^^^^^^^^^^^^^^^\n",
       "  File \"/opt/miniconda3/lib/python3.12/concurrent/futures/_base.py\", line 401, in __get_result\n",
       "    raise self._exception\n",
-      "Exception: Error in cell 15 :\n",
-      "import pandas as pd\n",
-      "\n",
-      "spider(\n",
-      "    pd.DataFrame(\n",
-      "        {\n",
-      "            \"x\": [*\"abcde\"],\n",
-      "            \"c1\": [10, 11, 12, 13, 14],\n",
-      "            \"c2\": [0.1, 0.3, 0.4, 0.1, 0.9],\n",
-      "            \"c3\": [1e5, 2e5, 3.5e5, 8e4, 5e4],\n",
-      "            \"c4\": [9, 12, 5, 2, 0.2],\n",
-      "            \"test\": [1, 1, 1, 1, 5],\n",
-      "        },\n",
-      "        index=[*\"abcde\"],\n",
-      "    ),\n",
-      "    title=\"Sample Spider\",\n",
-      "    padding=1.1,\n",
-      ")\n"
+      "Exception: Error in notebook: Rich Logging and printing in cell 23 :\n",
+      "def do(im_path):\n",
+      "    from torch_snippets import show, read\n",
+      "    im = read(im_path)\n",
+      "    show(im, sz=3)\n",
+      "    print(im.mean())\n",
+      "\n",
+      "do('../assets/Preamble.png')\n"
      ]
     }
    ],
@@ -781,28 +846,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[master a2cf6d4] 0.539\n",
-      " 20 files changed, 1625 insertions(+), 329 deletions(-)\n",
-      " create mode 100644 torch_snippets.egg-info/entry_points.txt\n",
-      " create mode 100644 torch_snippets/cli.py\n",
-      " create mode 100644 torch_snippets/s3_loader.py\n",
-      " create mode 100644 torch_snippets/zen.py\n",
-      "Enumerating objects: 47, done.\n",
-      "Counting objects: 100% (47/47), done.\n",
+      "[master 72e85c2] 0.540\n",
+      " 98 files changed, 23423 insertions(+), 7738 deletions(-)\n",
+      " create mode 100644 __module_timing__.py.lprof\n",
+      " create mode 100644 _proc/bokeh_plotting.ipynb\n",
+      " create mode 100644 _proc/capsule.ipynb\n",
+      " create mode 100644 _proc/charts.ipynb\n",
+      " create mode 100644 _proc/config.ipynb\n",
+      " create mode 100644 _proc/interactive_show.ipynb\n",
+      " delete mode 100644 _proc/logging.ipynb\n",
+      " create mode 100644 _proc/sklegos.ipynb\n",
+      " create mode 100644 debug/profile_time.txt\n",
+      " create mode 100644 debug/profile_time_202408241027.txt\n",
+      " create mode 100644 docs/AttrDict.html\n",
+      " create mode 100644 docs/charts_files/figure-html/cell-7-output-1.png\n",
+      " create mode 100644 torch_snippets/__module_timing__.py\n",
+      "Enumerating objects: 207, done.\n",
+      "Counting objects: 100% (207/207), done.\n",
       "Delta compression using up to 12 threads\n",
-      "Compressing objects: 100% (25/25), done.\n",
-      "Writing objects: 100% (26/26), 14.37 KiB | 4.79 MiB/s, done.\n",
-      "Total 26 (delta 20), reused 0 (delta 0), pack-reused 0\n",
-      "remote: Resolving deltas: 100% (20/20), completed with 19 local objects.\u001b[K\n",
+      "Compressing objects: 100% (110/110), done.\n",
+      "Writing objects: 100% (114/114), 1.12 MiB | 4.66 MiB/s, done.\n",
+      "Total 114 (delta 84), reused 1 (delta 1), pack-reused 0\n",
+      "remote: Resolving deltas: 100% (84/84), completed with 62 local objects.\u001b[K\n",
       "To ssh://github.com/sizhky/torch_snippets\n",
-      "   7d0b19f..a2cf6d4  master -> master\n"
+      "   a2cf6d4..72e85c2  master -> master\n"
      ]
     }
    ],
diff --git a/testing.ipynb b/testing.ipynb
index 406e729..ea11e57 100644
--- a/testing.ipynb
+++ b/testing.ipynb
@@ -263,7 +263,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.15"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,