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Simon Eskildsen sirupsen @Shopify Ottawa, ON http://sirupsen.com Principal Engineer @Shopify

sirupsen/dotfiles 164

Personal UNIX toolbox

sirupsen/airrecord 148

Ruby wrapper for Airtable, your personal database

sirupsen/anki-airtable 129

Sync Anki with Airtable!

Airtable/airtable-ruby 113

Access Airtable data sheets with ease using ruby

sirupsen/contestrus 32

An open source algorithmic contest platform

sirupsen/cachedis 21

Cachedis caches expensive (database) queries in Redis

sirupsen/awesome-algorithms 12

A curated list of awesome places to learn and/or practice algorithms.

sirupsen/airtable-rs 11

Rust wrapper for the Airtable API

sirupsen/can-has-lolcat 9

Fetches a random lolcat, and returns the appropriate output format.

camilo/sequel-vertica 7

Sequel adapter for Vertica analytic database.

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issue commentsirupsen/napkin-math

Some questions

All good friend! Can you let me know the numbers from the bigger instance? I'd like to collect those too

mickeypash

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issue commentGjjvdBurg/paper2remarkable

Some images do not get picked up

Oh that's awesome! Unfortunately my Remarkable just broke.... so it'll be about 2 months (given the ridiculous schedule their warranty operates under) until I have one again. I ended up buying an iPad, because the flow of having a device for reading very dense articles is critical to me now.

sirupsen

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issue closedsirupsen/napkin-math

Some questions

Hey Simon,

Thanks for the great talk and resource! I'm quite new to all things perf and also haven't really used Rust before. I had a couple of questions:

  1. When I ran your tool on an AWS ec2 host running Ubuntu 18.4 and all the memory functions failed. I wonder if that's related to some host restrictions?
  2. I didn't see Brendan Gregg in any of your references, I think his work might be worth mentioning here.

Cheers

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mickeypash

issue commentGjjvdBurg/paper2remarkable

Some images do not get picked up

One quick idea would be to see if the Javascript (which is maintained by Mozilla) readability has the same issue, and then shell out to it (or embed V8, which I know at least is commonly done in Ruby for asset compilation). Not ideal...

sirupsen

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books

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issue commentGjjvdBurg/paper2remarkable

Some images do not get picked up

BTW @GjjvdBurg do you have anywhere set up where I can donate/sponsor? Use paper2remarkable a lot for generating clean PDFs, least I could do :)

sirupsen

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issue commentsirupsen/napkin-math

Some questions

Hey Mickey, do you have a trace?

mickeypash

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Simon Eskildsen

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napkin problem 9

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issue openedGjjvdBurg/paper2remarkable

Some images do not get picked up

I have started sending newspaper articles to my Remarkable a lot more, passing it through p2r, since the output is much nicer and more consistent than a print-to-pdf.

However, some websites have trouble with images. I was debugging it a bit this morning, but had to step away before finding a solution.

Here's an example of an article that exhibits the problem (if you get pay-walled, you can see it below using the <figure> syntax).

After digging a bit in the code, it seems that this is a problem in html2text, where it doesn't seem to pick up these <figure> tags, since if you pass --debug, the .html file doesn't include the images either.

There's probably a few solutions... the hackiest, but quickest, would be to regex these out and insert them into the string as 'simple' images that html2text would pick up. The more elegant, but also more time-consuing, would be to have html2text support <figure> properly.

created time in a month

issue closedsirupsen/zk

Error when using zksim, example usage would be helpful in Readme

I am trying to setup zk for personal use and I am not able to figure out the example usage of zksim

I am getting error when executing the command (p.s I am confused about the input arguments to zksim) but I doubt it matters.

~ zksim dummy
Traceback (most recent call last):
  File "/home/imran/zk/bin/zksim", line 118, in <module>
    TfidfSearch().run()
  File "/home/imran/zk/bin/zksim", line 114, in run
    self.application_logic(args.filename)
  File "/home/imran/zk/bin/zksim", line 101, in application_logic
    for file in relevant_titles(df, filename, title_col="title", text_col="body")[:self.num_files_to_show]:
  File "/home/imran/zk/bin/zksim", line 70, in relevant_titles
    sim_index = similarity_index(searching_index, vectors)
  File "/home/imran/zk/bin/zksim", line 55, in similarity_index
    cosine_similarities = linear_kernel(vectors[search_index], vectors).flatten()
  File "/home/imran/.local/lib/python3.8/site-packages/sklearn/metrics/pairwise.py", line 1002, in linear_kernel
    X, Y = check_pairwise_arrays(X, Y)
  File "/home/imran/.local/lib/python3.8/site-packages/sklearn/utils/validation.py", line 73, in inner_f
    return f(**kwargs)
  File "/home/imran/.local/lib/python3.8/site-packages/sklearn/metrics/pairwise.py", line 144, in check_pairwise_arrays
    X = check_array(X, accept_sparse=accept_sparse, dtype=dtype,
  File "/home/imran/.local/lib/python3.8/site-packages/sklearn/utils/validation.py", line 73, in inner_f
    return f(**kwargs)
  File "/home/imran/.local/lib/python3.8/site-packages/sklearn/utils/validation.py", line 651, in check_array
    raise ValueError("Found array with %d sample(s) (shape=%s) while a"
ValueError: Found array with 0 sample(s) (shape=(0, 4)) while a minimum of 1 is required by check_pairwise_arrays.

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ipochi

issue commentsirupsen/zk

Error when using zksim, example usage would be helpful in Readme

From the README: zksim. Finds similar notes to the note passed as an argument. See #1 for more., yeah, I think the error improvement should be good enough for now 👍

ipochi

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Justin DiEmmanuele

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Error handling for invalid filename passed

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Merge pull request #5 from jcd13d/master Error handling for invalid filename passed to zksim

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PR merged sirupsen/zk

Error handling for invalid filename passed to zksim

In response to issue #4, adding error handling for if the note filename passed is not found in the database.

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scale your code link updates

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issue openedGjjvdBurg/paper2remarkable

Multiple definitions in dictionary at byte 0xbe2 for key /ToUnicode

p2r --right https://www.manuelrigger.at/preprints/NoREC.pdf

You seen this before?

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Varun Kashyap

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Fixes zkn - note title was not getting appended to the timestamp

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Varun Kashyap

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readme: add Linux dependencies

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Merge pull request #2 from varunkashyap/master

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PR merged sirupsen/zk

Fixes title not getting appended to the timestamp, adds linux section to readme
  • Ran into zkn not appending the title to the timestamp (tested on Bash 3 and 4). Cause being line 6: local: can only be used in a function.
  • Additional dependencies are needed on Linux, so added a section in the readme. Might prove helpful to others trying this out on Linux.
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pull request commentsirupsen/zk

TF-IDF based search for ZK init

This is awesome. Will add the bind and a README entry.

jcd13d

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TF-IDF based search for ZK init

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help when no args, change name, take default arg

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error handling for imports and ZK_PATH variable

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Justin DiEmmanuele

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added shebang, dropped .py, added hightlights filtering

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updated import error handling

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Merge pull request #1 from jcd13d/master

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PR merged sirupsen/zk

TF-IDF based search for ZK init

Wrote this script to do a TF-IDF based search. Think it's interesting to use to find notes that could be related to a note recently written but didn't come up through tag/keyword searches. I wrote a little blurb on how I use it in the file.

+118 -0

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jcd13d

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pull request commentsirupsen/zk

TF-IDF based search for ZK init

Not sure if there is a way to erase that query when you do the alt-s similarity search. All I did was add the last bind here:

I think alt-s:execute is the right one, I'll do that in mater :)

jcd13d

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zk-assets-localize: initial

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issue commentsirupsen/airrecord

Fails to update multi-select fields.

Please consider submitting a patch

mbreen

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pull request commentsirupsen/napkin-math

Various minor changes

For sure. Planning on adding some new benchmarks? 👀 👂

pviotti

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build(deps): bump rio from 0.9.2 to 0.9.3 (#2) Bumps [rio](https://github.com/spacejam/rio) from 0.9.2 to 0.9.3. - [Release notes](https://github.com/spacejam/rio/releases) - [Commits](https://github.com/spacejam/rio/commits) Signed-off-by: dependabot-preview[bot] <support@dependabot.com> Co-authored-by: dependabot-preview[bot] <27856297+dependabot-preview[bot]@users.noreply.github.com>

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build(deps): bump rand from 0.7.0 to 0.7.3 (#5) Bumps [rand](https://github.com/rust-random/rand) from 0.7.0 to 0.7.3. - [Release notes](https://github.com/rust-random/rand/releases) - [Changelog](https://github.com/rust-random/rand/blob/master/CHANGELOG.md) - [Commits](https://github.com/rust-random/rand/compare/0.7.0...0.7.3) Signed-off-by: dependabot-preview[bot] <support@dependabot.com> Co-authored-by: dependabot-preview[bot] <27856297+dependabot-preview[bot]@users.noreply.github.com>

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Merge pull request #9 from pviotti/new-master

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PR merged sirupsen/napkin-math

Various minor changes
  • add GitHub Actions for CI
  • bump dependencies
  • add loop to prevent connection refusal on TCP test
  • address some warnings
  • make benchmark file name a global variable
+160 -135

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pviotti

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pull request commentsirupsen/napkin-math

Various minor changes

Excellent, thanks!

Curious what you're using the script for or planning to? 👀

pviotti

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pull request commentsirupsen/napkin-math

Fix build on Linux with Rust v1.43.1

Yes please 👍

pviotti

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Paolo

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fix: cannot find as_raw_fd() method (imported os::unix::io trait), and unsafe-related errors

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Merge pull request #8 from pviotti/fixes

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PR merged sirupsen/napkin-math

Fix build on Linux with Rust v1.43.1

To build this on Linux with Rust 1.43.1 I had to import this trait and add a couple of unsafe regions.

(example of error preventing build here)

+11 -4

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pviotti

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PR closed sirupsen/sirupsen.com

Fix typo

BTW great blog post! :raised_hands:

+1 -1

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gmcabrita

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pull request commentsirupsen/sirupsen.com

Fix typo

Thanks! Someone else pointed this out before I saw your post so it's in master. :) Appreciate it.

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[ImgBot] Optimize images *Total -- 4,315.18kb -> 3,810.33kb (11.7%) /content/post/adjacent-possible/adjacent_possible_printing_press.png -- 100.53kb -> 23.77kb (76.36%) /content/post/adjacent-possible/adjacent_possible_simple.png -- 60.79kb -> 14.44kb (76.25%) /content/post/adjacent-possible/darwin_minecraft.png -- 65.67kb -> 16.29kb (75.2%) /content/post/adjacent-possible/adj_int.png -- 58.81kb -> 14.76kb (74.9%) /static/static/images/airtable/flash_card.gif -- 1,895.60kb -> 1,692.33kb (10.72%) /content/post/adjacent-possible/picturephone.jpg -- 537.36kb -> 508.36kb (5.4%) /static/static/images/airtable/words_ext.gif -- 653.85kb -> 619.43kb (5.27%) /content/post/adjacent-possible/sgi.jpg -- 36.81kb -> 34.95kb (5.03%) /static/static/images/airtable/tea.gif -- 128.68kb -> 122.83kb (4.55%) /static/static/images/airtable/books.gif -- 777.08kb -> 763.17kb (1.79%) Signed-off-by: ImgBotApp <ImgBotHelp@gmail.com>

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[ImgBot] Optimize images

Beep boop. Your images are optimized!

Your image file size has been reduced by 12% 🎉

<details> <summary> Details </summary>

File Before After Percent reduction
/content/post/adjacent-possible/adjacent_possible_printing_press.png 100.53kb 23.77kb 76.36%
/content/post/adjacent-possible/adjacent_possible_simple.png 60.79kb 14.44kb 76.25%
/content/post/adjacent-possible/darwin_minecraft.png 65.67kb 16.29kb 75.20%
/content/post/adjacent-possible/adj_int.png 58.81kb 14.76kb 74.90%
/static/static/images/airtable/flash_card.gif 1,895.60kb 1,692.33kb 10.72%
/content/post/adjacent-possible/picturephone.jpg 537.36kb 508.36kb 5.40%
/static/static/images/airtable/words_ext.gif 653.85kb 619.43kb 5.27%
/content/post/adjacent-possible/sgi.jpg 36.81kb 34.95kb 5.03%
/static/static/images/airtable/tea.gif 128.68kb 122.83kb 4.55%
/static/static/images/airtable/books.gif 777.08kb 763.17kb 1.79%
Total : 4,315.18kb 3,810.33kb 11.70%

</details>


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Simon Eskildsen

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review: deal with dev

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pull request commentsirupsen/zk

TF-IDF based search for ZK init

Let me know when you're ready for a final look :)

Make sure to remove the py extension too.

jcd13d

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Pull request review commentsirupsen/zk

TF-IDF based search for ZK init

+import argparse+import os+import sqlite3+import pandas as pd+from sklearn.metrics.pairwise import linear_kernel+from sklearn.feature_extraction.text import TfidfVectorizer+++"""+This script takes a zettelkasten note filename and sorts the available notes by their similarity+using Term Frequency-Inverse Document Frequency (TF-IDF). The idea is to use this to help one make+connections in their zettelkasten they may have not thought of initially. ++I'm not sure how well this works in practice yet since my zettelkasten is just starting to fill up.+It does seem to find notes I would say are generally related. I have limited the search to print the+top 20 matches.++I currently utilize sirupsen's search tool to use this (https://github.com/sirupsen/zk/blob/master/bin/zks). +I have a second search function for when I want to try TF-IDF that puts 'python --filename "zk filename.md" |' in+front of the fzf. This will give you a view to scroll through related files with the same opening and linking commands+as zk-search. +"""+++def vectorize_text(series):+    """+    Uses sklearn text vectorizer. Will do the necessary preprocessing like removing stop words,+    transform to lowercase etc.++    :param series: Pandas Series object+    :return: matrix of tf-idf features+    """+    return TfidfVectorizer().fit_transform(series.values)

Ya, not a requirement to get this in

jcd13d

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Pull request review commentsirupsen/zk

TF-IDF based search for ZK init

+import argparse+import os+import sqlite3+import pandas as pd+from sklearn.metrics.pairwise import linear_kernel+from sklearn.feature_extraction.text import TfidfVectorizer+++"""+This script takes a zettelkasten note filename and sorts the available notes by their similarity+using Term Frequency-Inverse Document Frequency (TF-IDF). The idea is to use this to help one make+connections in their zettelkasten they may have not thought of initially. ++I'm not sure how well this works in practice yet since my zettelkasten is just starting to fill up.+It does seem to find notes I would say are generally related. I have limited the search to print the+top 20 matches.++I currently utilize sirupsen's search tool to use this (https://github.com/sirupsen/zk/blob/master/bin/zks). +I have a second search function for when I want to try TF-IDF that puts 'python --filename "zk filename.md" |' in+front of the fzf. This will give you a view to scroll through related files with the same opening and linking commands+as zk-search. +"""+++def vectorize_text(series):+    """+    Uses sklearn text vectorizer. Will do the necessary preprocessing like removing stop words,+    transform to lowercase etc.++    :param series: Pandas Series object+    :return: matrix of tf-idf features+    """+    return TfidfVectorizer().fit_transform(series.values)+++def index_from_title(series, title):+    return series[series == title].index+++def similarity_index(search_index, vectors):+    """+    Uses cosine similarity to find which documents are the most similar to the file index passed+    in search_index+    :param search_index: Index of file you want similar files to+    :param vectors: Vector of TF-IDF vector features for each document+    :return: Returns the index numbers of the decuments in order of similarity+    """+    cosine_similarities = linear_kernel(vectors[search_index], vectors).flatten()+    return (-cosine_similarities).argsort()+++def relevant_titles(df, title, title_col, text_col):+    """+    Uses indexes from similarity_index to sort the DataFrame of notes by similarity+    :param df: DataFame of notes (from zettelkasten database)+    :param title: Title to search for similar files+    :param title_col: Name of column in DataFrame that has titles of each note+    :param text_col: Name of column in DataFrame that has the body of each note+    :return: DataFrame sorted by similarity to the note title passed+    """+    vectors = vectorize_text(df[text_col])+    searching_index = index_from_title(df[title_col], title)+    sim_index = similarity_index(searching_index, vectors)+    return df.iloc[sim_index][title_col].values

Not a requirement to get this in I'd say :)

jcd13d

comment created time in 2 months

Pull request review commentsirupsen/zk

TF-IDF based search for ZK init

+import argparse+import os+import sqlite3+import pandas as pd+from sklearn.metrics.pairwise import linear_kernel+from sklearn.feature_extraction.text import TfidfVectorizer+++"""+This script takes a zettelkasten note filename and sorts the available notes by their similarity+using Term Frequency-Inverse Document Frequency (TF-IDF). The idea is to use this to help one make+connections in their zettelkasten they may have not thought of initially. ++I'm not sure how well this works in practice yet since my zettelkasten is just starting to fill up.+It does seem to find notes I would say are generally related. I have limited the search to print the+top 20 matches.++I currently utilize sirupsen's search tool to use this (https://github.com/sirupsen/zk/blob/master/bin/zks). +I have a second search function for when I want to try TF-IDF that puts 'python --filename "zk filename.md" |' in+front of the fzf. This will give you a view to scroll through related files with the same opening and linking commands+as zk-search. +"""+++def vectorize_text(series):+    """+    Uses sklearn text vectorizer. Will do the necessary preprocessing like removing stop words,+    transform to lowercase etc.++    :param series: Pandas Series object+    :return: matrix of tf-idf features+    """+    return TfidfVectorizer().fit_transform(series.values)+++def index_from_title(series, title):+    return series[series == title].index+++def similarity_index(search_index, vectors):+    """+    Uses cosine similarity to find which documents are the most similar to the file index passed+    in search_index+    :param search_index: Index of file you want similar files to+    :param vectors: Vector of TF-IDF vector features for each document+    :return: Returns the index numbers of the decuments in order of similarity+    """+    cosine_similarities = linear_kernel(vectors[search_index], vectors).flatten()+    return (-cosine_similarities).argsort()+++def relevant_titles(df, title, title_col, text_col):+    """+    Uses indexes from similarity_index to sort the DataFrame of notes by similarity+    :param df: DataFame of notes (from zettelkasten database)+    :param title: Title to search for similar files+    :param title_col: Name of column in DataFrame that has titles of each note+    :param text_col: Name of column in DataFrame that has the body of each note+    :return: DataFrame sorted by similarity to the note title passed+    """+    vectors = vectorize_text(df[text_col])+    searching_index = index_from_title(df[title_col], title)+    sim_index = similarity_index(searching_index, vectors)+    return df.iloc[sim_index][title_col].values+++class CustomAction(argparse.Action):+    # Needed this to handle white space in filenames passed, even when in quotes idk why+    def __call__(self, parser, namespace, values, option_string=None):+        setattr(namespace, self.dest, " ".join(values))+++class TfidfSearch:++    def __init__(self):++        self.zk_path = os.environ['ZK_PATH']+        self.conn = sqlite3.connect(os.path.join(self.zk_path, "index.db"))+        self.cursor = self.conn.cursor()+        self.num_files_to_show = 20++    def application_logic(self, filename):+        df = pd.read_sql("select * from zettelkasten", con=self.conn)

Yep, sorry!

jcd13d

comment created time in 2 months

pull request commentsirupsen/zk

TF-IDF based search for ZK init

Another thing I was thinking is that this tool could take into consideration the entire link hierarchy. E.g.

# Note A
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Pull request review commentsirupsen/zk

TF-IDF based search for ZK init

+import argparse+import os+import sqlite3+import pandas as pd+from sklearn.metrics.pairwise import linear_kernel+from sklearn.feature_extraction.text import TfidfVectorizer+++"""+This script takes a zettelkasten note filename and sorts the available notes by their similarity+using Term Frequency-Inverse Document Frequency (TF-IDF). The idea is to use this to help one make+connections in their zettelkasten they may have not thought of initially. ++I'm not sure how well this works in practice yet since my zettelkasten is just starting to fill up.+It does seem to find notes I would say are generally related. I have limited the search to print the+top 20 matches.++I currently utilize sirupsen's search tool to use this (https://github.com/sirupsen/zk/blob/master/bin/zks). +I have a second search function for when I want to try TF-IDF that puts 'python --filename "zk filename.md" |' in+front of the fzf. This will give you a view to scroll through related files with the same opening and linking commands+as zk-search. 

How about just making this part of zks by adding another --bind, e.g. Alt-S will show similar notes?

jcd13d

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Pull request review commentsirupsen/zk

TF-IDF based search for ZK init

+import argparse+import os+import sqlite3+import pandas as pd+from sklearn.metrics.pairwise import linear_kernel+from sklearn.feature_extraction.text import TfidfVectorizer+++"""+This script takes a zettelkasten note filename and sorts the available notes by their similarity+using Term Frequency-Inverse Document Frequency (TF-IDF). The idea is to use this to help one make+connections in their zettelkasten they may have not thought of initially. ++I'm not sure how well this works in practice yet since my zettelkasten is just starting to fill up.+It does seem to find notes I would say are generally related. I have limited the search to print the+top 20 matches.++I currently utilize sirupsen's search tool to use this (https://github.com/sirupsen/zk/blob/master/bin/zks). +I have a second search function for when I want to try TF-IDF that puts 'python --filename "zk filename.md" |' in+front of the fzf. This will give you a view to scroll through related files with the same opening and linking commands+as zk-search. +"""+++def vectorize_text(series):+    """+    Uses sklearn text vectorizer. Will do the necessary preprocessing like removing stop words,+    transform to lowercase etc.++    :param series: Pandas Series object+    :return: matrix of tf-idf features+    """+    return TfidfVectorizer().fit_transform(series.values)+++def index_from_title(series, title):+    return series[series == title].index+++def similarity_index(search_index, vectors):+    """+    Uses cosine similarity to find which documents are the most similar to the file index passed+    in search_index+    :param search_index: Index of file you want similar files to+    :param vectors: Vector of TF-IDF vector features for each document+    :return: Returns the index numbers of the decuments in order of similarity+    """+    cosine_similarities = linear_kernel(vectors[search_index], vectors).flatten()+    return (-cosine_similarities).argsort()+++def relevant_titles(df, title, title_col, text_col):+    """+    Uses indexes from similarity_index to sort the DataFrame of notes by similarity+    :param df: DataFame of notes (from zettelkasten database)+    :param title: Title to search for similar files+    :param title_col: Name of column in DataFrame that has titles of each note+    :param text_col: Name of column in DataFrame that has the body of each note+    :return: DataFrame sorted by similarity to the note title passed+    """+    vectors = vectorize_text(df[text_col])+    searching_index = index_from_title(df[title_col], title)+    sim_index = similarity_index(searching_index, vectors)+    return df.iloc[sim_index][title_col].values+++class CustomAction(argparse.Action):+    # Needed this to handle white space in filenames passed, even when in quotes idk why+    def __call__(self, parser, namespace, values, option_string=None):+        setattr(namespace, self.dest, " ".join(values))+++class TfidfSearch:++    def __init__(self):++        self.zk_path = os.environ['ZK_PATH']+        self.conn = sqlite3.connect(os.path.join(self.zk_path, "index.db"))+        self.cursor = self.conn.cursor()+        self.num_files_to_show = 20++    def application_logic(self, filename):+        df = pd.read_sql("select * from zettelkasten", con=self.conn)+        for file in relevant_titles(df, filename, title_col="title", text_col="body")[:self.num_files_to_show]:+            print(file)++    def run(self):+        parser = argparse.ArgumentParser(description='Process some integers.')+        parser.add_argument('--filename', dest='filename', action=CustomAction, type=str, nargs='+',+                            help='filename to search for similarity')

Can you make it just take a filename as the default argument, instead of as an option?

jcd13d

comment created time in 2 months

Pull request review commentsirupsen/zk

TF-IDF based search for ZK init

+import argparse+import os+import sqlite3+import pandas as pd+from sklearn.metrics.pairwise import linear_kernel+from sklearn.feature_extraction.text import TfidfVectorizer+++"""+This script takes a zettelkasten note filename and sorts the available notes by their similarity+using Term Frequency-Inverse Document Frequency (TF-IDF). The idea is to use this to help one make+connections in their zettelkasten they may have not thought of initially. ++I'm not sure how well this works in practice yet since my zettelkasten is just starting to fill up.+It does seem to find notes I would say are generally related. I have limited the search to print the+top 20 matches.++I currently utilize sirupsen's search tool to use this (https://github.com/sirupsen/zk/blob/master/bin/zks). +I have a second search function for when I want to try TF-IDF that puts 'python --filename "zk filename.md" |' in+front of the fzf. This will give you a view to scroll through related files with the same opening and linking commands+as zk-search. +"""+++def vectorize_text(series):+    """+    Uses sklearn text vectorizer. Will do the necessary preprocessing like removing stop words,+    transform to lowercase etc.++    :param series: Pandas Series object+    :return: matrix of tf-idf features+    """+    return TfidfVectorizer().fit_transform(series.values)

Presumably this is the most expensive operation. This takes about ~1s on my machine with roughly 1,000 notes. That's OK for me, but if you wanted to nerd out, I think you can use fts4aux to get the raw tokenization from Sqlite's FTS to avoid computing it again.

jcd13d

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Pull request review commentsirupsen/zk

TF-IDF based search for ZK init

+import argparse+import os+import sqlite3+import pandas as pd+from sklearn.metrics.pairwise import linear_kernel+from sklearn.feature_extraction.text import TfidfVectorizer+++"""+This script takes a zettelkasten note filename and sorts the available notes by their similarity+using Term Frequency-Inverse Document Frequency (TF-IDF). The idea is to use this to help one make+connections in their zettelkasten they may have not thought of initially. ++I'm not sure how well this works in practice yet since my zettelkasten is just starting to fill up.+It does seem to find notes I would say are generally related. I have limited the search to print the+top 20 matches.++I currently utilize sirupsen's search tool to use this (https://github.com/sirupsen/zk/blob/master/bin/zks). +I have a second search function for when I want to try TF-IDF that puts 'python --filename "zk filename.md" |' in+front of the fzf. This will give you a view to scroll through related files with the same opening and linking commands+as zk-search. +"""+++def vectorize_text(series):+    """+    Uses sklearn text vectorizer. Will do the necessary preprocessing like removing stop words,+    transform to lowercase etc.++    :param series: Pandas Series object+    :return: matrix of tf-idf features+    """+    return TfidfVectorizer().fit_transform(series.values)+++def index_from_title(series, title):+    return series[series == title].index+++def similarity_index(search_index, vectors):+    """+    Uses cosine similarity to find which documents are the most similar to the file index passed+    in search_index+    :param search_index: Index of file you want similar files to+    :param vectors: Vector of TF-IDF vector features for each document+    :return: Returns the index numbers of the decuments in order of similarity+    """+    cosine_similarities = linear_kernel(vectors[search_index], vectors).flatten()+    return (-cosine_similarities).argsort()+++def relevant_titles(df, title, title_col, text_col):+    """+    Uses indexes from similarity_index to sort the DataFrame of notes by similarity+    :param df: DataFame of notes (from zettelkasten database)+    :param title: Title to search for similar files+    :param title_col: Name of column in DataFrame that has titles of each note+    :param text_col: Name of column in DataFrame that has the body of each note+    :return: DataFrame sorted by similarity to the note title passed+    """+    vectors = vectorize_text(df[text_col])+    searching_index = index_from_title(df[title_col], title)+    sim_index = similarity_index(searching_index, vectors)+    return df.iloc[sim_index][title_col].values

Exposing the cosine similarity score would be sweet, similar to what zkt does with the count of tags.

jcd13d

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Pull request review commentsirupsen/zk

TF-IDF based search for ZK init

+import argparse+import os+import sqlite3+import pandas as pd+from sklearn.metrics.pairwise import linear_kernel+from sklearn.feature_extraction.text import TfidfVectorizer+++"""+This script takes a zettelkasten note filename and sorts the available notes by their similarity+using Term Frequency-Inverse Document Frequency (TF-IDF). The idea is to use this to help one make+connections in their zettelkasten they may have not thought of initially. ++I'm not sure how well this works in practice yet since my zettelkasten is just starting to fill up.+It does seem to find notes I would say are generally related. I have limited the search to print the+top 20 matches.++I currently utilize sirupsen's search tool to use this (https://github.com/sirupsen/zk/blob/master/bin/zks). +I have a second search function for when I want to try TF-IDF that puts 'python --filename "zk filename.md" |' in+front of the fzf. This will give you a view to scroll through related files with the same opening and linking commands+as zk-search. +"""+++def vectorize_text(series):+    """+    Uses sklearn text vectorizer. Will do the necessary preprocessing like removing stop words,+    transform to lowercase etc.++    :param series: Pandas Series object+    :return: matrix of tf-idf features+    """+    return TfidfVectorizer().fit_transform(series.values)+++def index_from_title(series, title):+    return series[series == title].index+++def similarity_index(search_index, vectors):+    """+    Uses cosine similarity to find which documents are the most similar to the file index passed+    in search_index+    :param search_index: Index of file you want similar files to+    :param vectors: Vector of TF-IDF vector features for each document+    :return: Returns the index numbers of the decuments in order of similarity+    """+    cosine_similarities = linear_kernel(vectors[search_index], vectors).flatten()+    return (-cosine_similarities).argsort()+++def relevant_titles(df, title, title_col, text_col):+    """+    Uses indexes from similarity_index to sort the DataFrame of notes by similarity+    :param df: DataFame of notes (from zettelkasten database)+    :param title: Title to search for similar files+    :param title_col: Name of column in DataFrame that has titles of each note+    :param text_col: Name of column in DataFrame that has the body of each note+    :return: DataFrame sorted by similarity to the note title passed+    """+    vectors = vectorize_text(df[text_col])+    searching_index = index_from_title(df[title_col], title)+    sim_index = similarity_index(searching_index, vectors)+    return df.iloc[sim_index][title_col].values+++class CustomAction(argparse.Action):+    # Needed this to handle white space in filenames passed, even when in quotes idk why+    def __call__(self, parser, namespace, values, option_string=None):+        setattr(namespace, self.dest, " ".join(values))+++class TfidfSearch:++    def __init__(self):++        self.zk_path = os.environ['ZK_PATH']+        self.conn = sqlite3.connect(os.path.join(self.zk_path, "index.db"))+        self.cursor = self.conn.cursor()+        self.num_files_to_show = 20++    def application_logic(self, filename):+        df = pd.read_sql("select * from zettelkasten", con=self.conn)

There's supports for importing raw highlights (may get renamed to "literatures notes" or "litnotes" later, you'll see this in fts-search). Can you add NOT LIKE 'highlights/%' here so it doesn't match that directory? 🆗

jcd13d

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Pull request review commentsirupsen/zk

TF-IDF based search for ZK init

+import argparse+import os+import sqlite3+import pandas as pd+from sklearn.metrics.pairwise import linear_kernel+from sklearn.feature_extraction.text import TfidfVectorizer

Not sure if you can do this in Python, but in Ruby you can rescue on an import and print a message, like: Please run pip3 install scikit-learn pandas before using this script.

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Pull request review commentsirupsen/zk

TF-IDF based search for ZK init

+import argparse+import os+import sqlite3+import pandas as pd+from sklearn.metrics.pairwise import linear_kernel+from sklearn.feature_extraction.text import TfidfVectorizer+++"""+This script takes a zettelkasten note filename and sorts the available notes by their similarity+using Term Frequency-Inverse Document Frequency (TF-IDF). The idea is to use this to help one make+connections in their zettelkasten they may have not thought of initially. ++I'm not sure how well this works in practice yet since my zettelkasten is just starting to fill up.+It does seem to find notes I would say are generally related. I have limited the search to print the+top 20 matches.++I currently utilize sirupsen's search tool to use this (https://github.com/sirupsen/zk/blob/master/bin/zks). +I have a second search function for when I want to try TF-IDF that puts 'python --filename "zk filename.md" |' in+front of the fzf. This will give you a view to scroll through related files with the same opening and linking commands+as zk-search. +"""+++def vectorize_text(series):+    """+    Uses sklearn text vectorizer. Will do the necessary preprocessing like removing stop words,+    transform to lowercase etc.++    :param series: Pandas Series object+    :return: matrix of tf-idf features+    """+    return TfidfVectorizer().fit_transform(series.values)+++def index_from_title(series, title):+    return series[series == title].index+++def similarity_index(search_index, vectors):+    """+    Uses cosine similarity to find which documents are the most similar to the file index passed+    in search_index+    :param search_index: Index of file you want similar files to+    :param vectors: Vector of TF-IDF vector features for each document+    :return: Returns the index numbers of the decuments in order of similarity+    """+    cosine_similarities = linear_kernel(vectors[search_index], vectors).flatten()+    return (-cosine_similarities).argsort()+++def relevant_titles(df, title, title_col, text_col):+    """+    Uses indexes from similarity_index to sort the DataFrame of notes by similarity+    :param df: DataFame of notes (from zettelkasten database)+    :param title: Title to search for similar files+    :param title_col: Name of column in DataFrame that has titles of each note+    :param text_col: Name of column in DataFrame that has the body of each note+    :return: DataFrame sorted by similarity to the note title passed+    """+    vectors = vectorize_text(df[text_col])+    searching_index = index_from_title(df[title_col], title)+    sim_index = similarity_index(searching_index, vectors)+    return df.iloc[sim_index][title_col].values+++class CustomAction(argparse.Action):+    # Needed this to handle white space in filenames passed, even when in quotes idk why+    def __call__(self, parser, namespace, values, option_string=None):+        setattr(namespace, self.dest, " ".join(values))+++class TfidfSearch:++    def __init__(self):++        self.zk_path = os.environ['ZK_PATH']+        self.conn = sqlite3.connect(os.path.join(self.zk_path, "index.db"))+        self.cursor = self.conn.cursor()+        self.num_files_to_show = 20++    def application_logic(self, filename):+        df = pd.read_sql("select * from zettelkasten", con=self.conn)+        for file in relevant_titles(df, filename, title_col="title", text_col="body")[:self.num_files_to_show]:+            print(file)++    def run(self):+        parser = argparse.ArgumentParser(description='Process some integers.')

Helpful if it prints --help when no args are passed!

jcd13d

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Pull request review commentsirupsen/zk

TF-IDF based search for ZK init

+import argparse

Let's rename this to zksim (zettelkasten-similar) or zkadj (zettelkasten-adjacent) or something of that nature, to make it more consistent. You can then also add it to the README. FWIW, I'm fine with multiple languages (Ruby, Python, ..)

jcd13d

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pull request commentsirupsen/zk

TF-IDF based search for ZK init

Nice, let me check this out :) Funny, I was just reading about cosine similarity 30 min ago (reading up on search). 👍

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Any tools to get real metrics reliably

I wonder if there exists a tool that can inspect the hardware in detail and outputs a report with those metrics so that we can do napkin-math more accurately. And it also helps comparing different machines when doing performance experiments

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Fix random memory read benchmark

Even though we only look at 64 bits, a full cache-line will be transferred. This means we should expect up to 8x the throughput. Let's change it to read a full cache-line instead. This shouldn't matter much for the sequential benchmarks, but they'd likely also be sped up.

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Pull request review commentcontainerd/containerd

vendor: sirupsen/logrus v1.5.0

-# Logrus <img src="http://i.imgur.com/hTeVwmJ.png" width="40" height="40" alt=":walrus:" class="emoji" title=":walrus:"/>&nbsp;[![Build Status](https://travis-ci.org/sirupsen/logrus.svg?branch=master)](https://travis-ci.org/sirupsen/logrus)&nbsp;[![GoDoc](https://godoc.org/github.com/sirupsen/logrus?status.svg)](https://godoc.org/github.com/sirupsen/logrus)+# Logrus <img src="http://i.imgur.com/hTeVwmJ.png" width="40" height="40" alt=":walrus:" class="emoji" title=":walrus:"/> [![Build Status](https://travis-ci.org/sirupsen/logrus.svg?branch=master)](https://travis-ci.org/sirupsen/logrus) [![GoDoc](https://godoc.org/github.com/sirupsen/logrus?status.svg)](https://godoc.org/github.com/sirupsen/logrus)  Logrus is a structured logger for Go (golang), completely API compatible with the standard library logger. +**Logrus is in maintenance-mode.** We will not be introducing new features. It's

Only reason would be performance. Hands are tied in Logrus on that one.

thaJeztah

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Pull request review commentcontainerd/containerd

vendor: sirupsen/logrus v1.5.0

-# Logrus <img src="http://i.imgur.com/hTeVwmJ.png" width="40" height="40" alt=":walrus:" class="emoji" title=":walrus:"/>&nbsp;[![Build Status](https://travis-ci.org/sirupsen/logrus.svg?branch=master)](https://travis-ci.org/sirupsen/logrus)&nbsp;[![GoDoc](https://godoc.org/github.com/sirupsen/logrus?status.svg)](https://godoc.org/github.com/sirupsen/logrus)+# Logrus <img src="http://i.imgur.com/hTeVwmJ.png" width="40" height="40" alt=":walrus:" class="emoji" title=":walrus:"/> [![Build Status](https://travis-ci.org/sirupsen/logrus.svg?branch=master)](https://travis-ci.org/sirupsen/logrus) [![GoDoc](https://godoc.org/github.com/sirupsen/logrus?status.svg)](https://godoc.org/github.com/sirupsen/logrus)  Logrus is a structured logger for Go (golang), completely API compatible with the standard library logger. +**Logrus is in maintenance-mode.** We will not be introducing new features. It's

Yeah, you know, except, I plan to change the casing at some point. For fun.

🤦

thaJeztah

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issue commentGjjvdBurg/paper2remarkable

Images for websites not included in PDF export

Thanks, glad you enjoyed it :)

I upgraded and tested this morning, still seems to have trouble.

For some reason it didn't work to try the same article, complaining about what I assume is a duplicate from rmapi. Although there's nothing to be found, searched in the reMarkable... odd.

Tried another article, but it also appears without pictures.

dotfiles $ p2r https://conclave-team.github.io/conclave-site/
ERROR: 2020/04/28 07:55:44 main.go:17: Error:  entry already exists
ERROR: Uploading file Conclave_Case_Study_-_A_Private_and_Secure_Real-Time_Collaborative_Text_Editor.pdf to reMarkable failed

If you think this might be a bug, please raise an issue on GitHub at:
https://github.com/GjjvdBurg/paper2remarkable

dotfiles $ p2r https://sirupsen.com/2019/

image

Versus browser:

image

sirupsen

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branch : print-macos

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issue openedGjjvdBurg/paper2remarkable

Images for websites not included in PDF export

Hello again Gertjan!

I was sending a long article this morning to my Remarkable with p2r (I mostly use it for papers, so that's why I hadn't noticed this before), and unfortunately it seems that the images are lost :(

image

Versus the website:

image

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allow scaling

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vaccinated + upstream

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ctags: run build on git ops

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pull request commentsirupsen/airrecord

Table.batch_update

It's too bad you can't finish it, is it because you ended up not needing it in your own project?

aguynamedben

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issue commentsirupsen/airrecord

Support batch operations

Maybe one thing I'd suggest is to just call it update, and then also have a update_or_create, as well as a create which can also take an array.

EvanHahn

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