Fix some typos (found by `codespell` and `typos`)

Signed-off-by: Stefan Weil <sw@weilnetz.de>
pull/111/head
Stefan Weil 7 months ago
parent 2383730a55
commit 79701e410d

@ -100,11 +100,11 @@ This generates `summary.html` and `summary.json` in the same `output_folder`.
If you are summarizing many reports and have used the `--differences` flag while
generating them, it may be useful to limit the number of differences reported by using
the `--occurences-threshold` parameter. This will reduce the size of the generated HTML
the `--occurrences-threshold` parameter. This will reduce the size of the generated HTML
report, making it easier to open and navigate. Note that the JSON report will still
contain all differences. Example:
~~~
dinglehopper-summarize output_folder/ --occurences-threshold 10
dinglehopper-summarize output_folder/ --occurrences-threshold 10
~~~
### dinglehopper-line-dirs

@ -329,7 +329,7 @@ def get_attr(te: Any, attr_name: str) -> float:
"""Extract the attribute for the given name.
Note: currently only handles numeric values!
Other or non existend values are encoded as np.nan.
Other or non existent values are encoded as np.nan.
"""
attr_value = te.attrib.get(attr_name)
try:

@ -391,7 +391,7 @@
"\\text{CER} = \\frac{i + s + d}{n}\n",
"$$\n",
"\n",
"where $i$ is the number of inserts, $s$ the number of substitutions, $d$ the number of deletions and $n$ is the number of characters in the reference text. (The text is not super clear about $n$ being the number of characters in the reference text, but it seems appropiate as they *are* clear about this when computing the word error rate.)"
"where $i$ is the number of inserts, $s$ the number of substitutions, $d$ the number of deletions and $n$ is the number of characters in the reference text. (The text is not super clear about $n$ being the number of characters in the reference text, but it seems appropriate as they *are* clear about this when computing the word error rate.)"
]
},
{
@ -680,7 +680,7 @@
" return cat in unwanted_categories or subcat in unwanted_subcategories\n",
"\n",
" # We follow Unicode Standard Annex #29 on Unicode Text Segmentation here: Split on word boundaries using\n",
" # uniseg.wordbreak.words() and ignore all \"words\" that contain only whitespace, punctation \"or similar characters.\"\n",
" # uniseg.wordbreak.words() and ignore all \"words\" that contain only whitespace, punctuation \"or similar characters.\"\n",
" for word in uniseg.wordbreak.words(s):\n",
" if all(unwanted(c) for c in word):\n",
" pass\n",

@ -54,7 +54,7 @@ def words(s: str) -> Generator[str, None, None]:
# We follow Unicode Standard Annex #29 on Unicode Text Segmentation here: Split on
# word boundaries using uniseg.wordbreak.words() and ignore all "words" that contain
# only whitespace, punctation "or similar characters."
# only whitespace, punctuation "or similar characters."
for word in uniseg.wordbreak.words(s):
if all(unwanted(c) for c in word):
pass

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