imagetocsv 0.1.0

Converts An Image to a CSV. This exists because Chorus 3.0 are bat-shit and only show images for vital metadata.

About • Installation • Shell Usage • License

About

Converts An Image to a CSV. This exists because Chorus 3.0 are bat-shit and only show images for vital metadata

Installation

pip install imagetocsv

Terminal Usage

Usage: imagetocsv [OPTIONS] IMAGE_PATH [CSV_PATH]

  Console script for imagetocsv.

Options:
  --version                 Show the version and exit.
  -v, --verbose             Vebosity level, ex. -vvvvv for debug level logging
  -n, --index_name TEXT     Index Name for the CSV file
  -i, --index TEXT          Index for the CSV file
  -c, --column_header TEXT  Columns for the CSV file
  -p, --preconfigured-options TEXT

  --help                    Show this message and exit.

Terminal Simple Examples

$ imagetocsv myimage.png mytable.csv
# For the source image this was built for 
$ imagetocsv -p bib myimage.png  

Terminal Advance Example

Adding Index Name, Index, and Column Header. They needs to match the deminsions of the matrix!

$ imagetocsv \ 
        image.jpg table.csv \
        --index_name "Population" \
        --index "All Events,Lymphocytes,Single cells...,Single cells...,Live/Dead,CD19+ Dump-,Naive gD+,Memory IgD-,IgD- KO-,P15-1,P15-2,P15-3,P15-4,MARIO WT++,P14-1,P14-2,P14-3,P14-4" \
        --column_header "Events,%Parent,%Total,FSC-A Median,FSC-A %rCV,SSC-A Median,SSC-A %rCV" 

Python Simple Usage

from imagetocsv import imagetocsv
from imagetocsv.examples import no_grid_example


df = imagetocsv(no_grid_example)
print(df.to_markdown())

	0	1	2	3	4	5	6
0	598150		100.00%	123428.50	57.53%	130689.00	50.55%
1	237987	39.79%	39.79%	134356.00	14.45%	102556.00	30.89%
2	228000	95.80%	38.12%	433804.00	13.96%	100917.00	29.64%
3	222453	97.57%	37.19%	133307.00	13.63%	100091.00	29.09%
4	212474	95.51%	35.52%	134238.00	12.97%	9700.00	29.27%
5	55885	26.30%	9.34%	131386.00	13.34%	93086.00	27.69%
6	34745	56.80%	5.31%	127549.00	10.25%	88501.00	24.60%
7	22496	40.25%	3.76%	14152450	15.79%	102606.00	30.31%
8	17409	77.39%	2.91%	144624.00	14.88%	107966.00	28.93%
9	2663	15.30%	0.45%	163750.00	11.93%	130908.00	26.18%
10	5	0.03%	0.00%	166073.00	5.07%	160211.00	6.57%
11	14736	84.65%	2.46%	14126450	14.20%	103995.00	28.13%
12	5	0.03%	0.00%	162803.00	6.04%	156540.00	9.02%
13	0	0.00%	0.00%
14	8888	39.51%	1.49%	431473.00	15.37%	90965.50	28.65%
15	1806	8.03%	0.30%	153347.00	12.19%	121119.50	24.60%
16	4896	21.76%	0.82%	141244.00	16.41%	101527.00	30.63%
17	6906	30.70%	1.15%	147753.00	12.13%	113108.50	25.94%

Python Advanced Usage

from imagetocsv import imagetocsv
from imagetocsv.examples import no_grid_example


df = imagetocsv(
        no_grid_example,
        index_name="Population",
        index=[
                "All Events",
                "Lymphocytes",
                "Single cells...",
                "Single cells...",
                "Live/Dead",
                "CD19+ Dump-",
                "Naive gD+",
                "Memory IgD-",
                "IgD- KO-",
                "P15-1",
                "P15-2",
                "P15-3",
                "P15-4",
                "MARIO WT++",
                "P14-1",
                "P14-2",
                "P14-3",
                "P14-4",
        ],
        column_header=["Events", "% Parent", "% Total", "FSC-A Median", "FSC-A %rCV", "SSC-A Median", "SSC-A %rCV"],
)
print(df.to_markdown())

Population	Events	% Parent	% Total	FSC-A Median	FSC-A %rCV	SSC-A Median	SSC-A %rCV
All Events	598,150		100.00%	123428.50	57.53%	130689.00	50.55%
Lymphocytes	237,987	39.79%	39.79%	134356.00	14.45%	102556.00	30.89%
Single cells...	228,000	95.80%	38.12%	433804.00	13.96%	100917.00	29.64%
Single cells...	222,453	97.57%	37.19%	133307.00	13.63%	100091.00	29.09%
Live/Dead	212,474	95.51%	35.52%	134238.00	12.97%	9700.00	29.27%
CD19+ Dump-	55,885	26.30%	9.34%	131386.00	13.34%	93086.00	27.69%
Naive gD+	34,745	56.80%	5.31%	127549.00	10.25%	88501.00	24.60%
Memory IgD-	22,496	40.25%	3.76%	14152450	15.79%	102606.00	30.31%
IgD- KO-	17,409	77.39%	2.91%	144624.00	14.88%	107966.00	28.93%
P15-1	2,663	15.30%	0.45%	163750.00	11.93%	130908.00	26.18%
P15-2	5	0.03%	0.00%	166073.00	5.07%	160211.00	6.57%
P15-3	14,736	84.65%	2.46%	14126450	14.20%	103995.00	28.13%
P15-4	5	0.03%	0.00%	162803.00	6.04%	156540.00	9.02%
MARIO WT++	0	0.00%	0.00%
P14-1	8,888	39.51%	1.49%	431473.00	15.37%	90965.50	28.65%
P14-2	1,806	8.03%	0.30%	153347.00	12.19%	121119.50	24.60%
P14-3	4896	21.76%	0.82%	141244.00	16.41%	101527.00	30.63%
P14-4	6,906	30.70%	1.15%	147753.00	12.13%	113108.50	25.94%

License

• Copyright © Troy M. Sincomb