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We will get the cosine similarity of genes starting with CDK and PPARG using pure duckdb.

Get the ids

CREATE OR REPLACE TABLE jcp_symbol AS (SELECT Metadata_JCP2022,Metadata_Symbol FROM read_csv(['https://github.com/jump-cellpainting/datasets/raw/99c34b66f51c5971c85417c02e191f43057c22a8/metadata/crispr.csv.gz', 'https://github.com/jump-cellpainting/datasets/raw/99c34b66f51c5971c85417c02e191f43057c22a8/metadata/orf.csv.gz']) WHERE starts_with(Metadata_Symbol, 'CDK') OR starts_with(Metadata_Symbol, 'PPARG'));
SELECT * FROM jcp_symbol LIMIT 2;
SELECT COUNT(*) AS nrows FROM jcp_symbol;

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from threading import Thread

import duckdb

def thread_duckdb(fn, iterable, *args, con=None):
    """
    Maximise performance using duckdb theading.

    The first argument of `fn` must be a Duckdb connection.

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import numpy as np
from skimage.measure import regionprops_table

img = np.ones((5, 5), dtype=int)
mask = np.zeros((5, 5), dtype=int)
mask[0:3, 0:3] = 1

feat_names = (
    "area",
    "bbox",

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INSTALL httpfs;
LOAD httpfs; 
SELECT COUNT(column_name) FROM (DESCRIBE SELECT COLUMNS('X_*') FROM read_parquet('https://celpainting-gallery.s3.amazonaws.com/cpg0042-chandrasekaran-jump/source_all/workspace/profiles_assembled/compound_DL_CPCNN/v1.0profiles.parquet'));                                                                         

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watch --interval 1 'find . -type d -exec bash -c \'echo "$1: $(find "$1" -maxdepth 1 -type f | wc -l)"\' _ {} \; | grep segment_cyto | cut -f4,5 -d"/" | sed "s/\/segment_cyto//" | sort'

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.zattrs eaa176fc87fe2baaba069856c2511b7cace6f07ea6dc6a77726c34eb22c1db81
.zgroup 2383746e67b4bcc2762b3f100f06c3fa2d5f149ab5a8e5da5d33521464a01959
A/.zgroup 2383746e67b4bcc2762b3f100f06c3fa2d5f149ab5a8e5da5d33521464a01959
A/1/.zattrs a44d4b2efe6344858f5bbd91a52ffbaec450a552f2e79abfe967726eb8e8d7b4
A/1/.zgroup 2383746e67b4bcc2762b3f100f06c3fa2d5f149ab5a8e5da5d33521464a01959
A/1/1/.zattrs ae19fc9113b70974a7e9827815901b240dee9d10d5c17ec4f256675e805b8eab
A/1/1/.zgroup 2383746e67b4bcc2762b3f100f06c3fa2d5f149ab5a8e5da5d33521464a01959
A/1/1/0/.zarray 2ec105822a9fb5f84ec19a0152be51bc0f4e7a75c593fd90a53f0745df695b91
A/1/1/0/0/0/0/0/0 a76987813876f33ff979f8c6f36c228d2ffd8f091b7b296f1330dd9f98339e95
A/1/1/0/0/1/0/0/0 27e700785f97735c373caff16bc16398f4c6e4aa59555ef16c58cce476c8285f

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awk 'BEGIN {FS="\t"; OFS="\t";}; {if (NR > 4) {corr=""; if (NR==11) corr="X"; print $9,$6,$2,$3,$4,"",corr,"","",$7;}}' cp\ measure\ authors.xlsx\ -\ Authors.tsv | iconv -f utf8 -t ascii//TRANSLIT//IGNORE | cat (printf "Email\tInstitution\tFirst Name\tMiddle Name(s)/Initial(s)\tLast Name\tSuffix\tCorresponding Author\tHome Page URL\tCollaborative Group/Consortium\tORCiD\n" | psub) - > cp_measure_authors.tsv

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# Quick tool to identify broken links in all markdown files inside a directory (including nested). It uses fd, markdown-link-check and gawk pulled from nix.
nix shell nixpkgs#fd nixpkgs#markdown-link-check nixpkgs#gawk --command bash -c "fd --extension 'md' | xargs markdown-link-check 2>/dev/null | awk '/FILE:/ {tmp=\$2}; /Status/ {print tmp,\$0}'"

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# From https://stackoverflow.com/a/32002086
# Run as `jq -R -s -f csv2json.jq csv.csv`

# objectify/1 takes an array of string values as inputs, converts
# numeric values to numbers, and packages the results into an object
# with keys specified by the "headers" array
def objectify(headers):
  # For jq 1.4, replace the following line by: def tonumberq: .;
  def tonumberq: tonumber? // .;
  . as $in