Columns and DataType Not Explicitly Set on line 83 of grid.py

[CodeSmileBot]

Hello!

I found an AI-Specific Code smell in your project.
The smell is called: Columns and DataType Not Explicitly Set

You can find more information about it in this paper: https://dl.acm.org/doi/abs/10.1145/3522664.3528620.

According to the paper, the smell is described as follows:

Problem	If the columns are not selected explicitly, it is not easy for developers to know what to expect in the downstream data schema. If the datatype is not set explicitly, it may silently continue the next step even though the input is unexpected, which may cause errors later. The same applies to other data importing scenarios.
Solution	It is recommended to set the columns and DataType explicitly in data processing.
Impact	Readability

Example:

### Pandas Column Selection
import pandas as pd
df = pd.read_csv('data.csv')
+ df = df[['col1', 'col2', 'col3']]

### Pandas Set DataType
import pandas as pd
- df = pd.read_csv('data.csv')
+ df = pd.read_csv('data.csv', dtype={'col1': 'str', 'col2': 'int', 'col3': 'float'})

You can find the code related to this smell in this link:

lucid/lucid/scratch/atlas_pipeline/grid.py

Lines 73 to 93 in 536e623

	"""
	# TODO: this isn't being used right now, will need to be
	# ported to gfile if we want to keep it
	for ti,tj,tile in enumerate_tiles(tiles):
	filename = "{directory}/{name}/tile_{n_layer}_{n_tile}_{ti}_{tj}".format(ti=ti, tj=tj, **params) #directory=directory, name=name, n_layer=n_layer, n_tile=n_tile,
	# write out the tile as a npz
	print("saving", filename + ".npz")
	np.savez_compressed(filename + ".npz", **tile)
	# write out the tile as a csv
	print("saving", filename + ".csv")
	df = pd.DataFrame(tile)
	df.to_csv(filename + ".csv", index=False)
	def enumerate_tiles(tiles):
	"""
	Convenience
	"""
	enumerated = []
	for key in tiles.keys():
	enumerated.append((key[0], key[1], tiles[key]))
	return enumerated

.

I also found instances of this smell in other files, such as:

.

I hope this information is helpful!