Digitization#
Once we are sure that all or some of the tables can be detected, we are ready to execute the whole digitization pipeline. The command dawsonia digitize processes a PDF / Zarr file in dawsonia.digitize.digitize_book() as follows:
Read table formats file
Create a
dawsonia.io.Bookinstance usingdawsonia.io.read_book()For each page, in
dawsonia.digitize.digitize_page_and_write_output()using parallel processes,load the image
detect tables
for each table, iterate over table cells within
digitize_table_with_model()infer text from table cell and save it to a intermediate
pandas.Dataframe
save the Dataframe to the filesystem as a parquet / markdown file along with other extra metadata such as:
Note
Pre-trained models for HTR are available in https://git.smhi.se/ai-for-obs/data. For example data/models/dawsonia/2024-03-29 is one such model
Demo: digitization#
Pandas 3.0 deprecation warning
Due to a change in API in upcoming Pandas 3.0, some deprecation warnings are emitted. These will have to be fixed in the future, but as long we use Pandas 2.x it should be fine. So we suppress it below.
import warnings
warnings.simplefilter("ignore", category=FutureWarning, lineno=0, append=False)
/data/proj12/geoford/dawsonia
/data/proj12/geoford/dawsonia_venv/lib64/python3.9/site-packages/IPython/core/magics/osm.py:417: UserWarning: using dhist requires you to install the `pickleshare` library.
self.shell.db['dhist'] = compress_dhist(dhist)[-100:]
Now we can digitize using the data repository and table_formats directory that we have prepared in the previous notebooks. We could either run the command below or use a pythonscript or a cfg file (see tip further down):
dawsonia digitize \
-f 1 -l 1 \
--model-path data/models/dawsonia/2024-03-29 \
--output-path digitized \
--table-fmt-dir table_formats \
--verbose \
data/raw/bjuroklubb_example/bjuroklubb_1955-01-01_1955-02-31.pdf
from dawsonia.digitize import digitize_book
from pathlib import Path
cwd = Path.cwd()
digitize_book(
path_file=cwd / "data/raw/bjuroklubb_example/bjuroklubb_1955-01-01_1955-02-31.pdf",
first_page=1,
last_page=1,
model_path=cwd / "data/models/dawsonia/2024-03-29",
output_path=cwd / "digitized",
table_fmt_dir=cwd / "table_formats",
verbose=True,
)
INFO 2025-10-08 08:51:03,529 - dawsonia.io._pdf - INFO - table_format = TableFormat(name_idx=['x_idx_0', 'x_idx_1', 'x_idx_2'], columns=[['h_1', 'h_2', 'h_3', 'h_4', 'h_5', 'h_6', 'h_7', 'h_8', 'h_9', 'h_10', 'h_11', 'h_12', 'h_13', 'h_14', 'h_15', 'h_16', 'h_17', 'h_18', 'h_19', 'h_20', 'h_21', 'h_22', 'h_23', 'h_24', 'Dagssum', 'Dagsmed', 'Dagsmax', 'Dagsmin', 'Diff'], ['h_1', 'h_2', 'h_3', 'h_4', 'h_5', 'h_6', 'h_7', 'h_8', 'h_9', 'h_10', 'h_11', 'h_12', 'h_13', 'h_14', 'h_15', 'h_16', 'h_17', 'h_18', 'h_19', 'h_20', 'h_21', 'h_22', 'h_23', 'h_24', 'Dagssum', 'Dagsmed', 'Dagsmax', 'Dagsmin', 'Diff'], ['h_1', 'h_4', 'h_7', 'h_10', 'h_13', 'h_16', 'h_19', 'h_22', 'Dagssumma_rel_fukt', 'Dagsmedel_rel_fukt', 'h_1_rikt', 'h_1_hast', 'h_4_rikt', 'h_4_hast', 'h_7_rikt', 'h_7_hast', 'h_10_rikt', 'h_10_hast', 'h_13_rikt', 'h_13_hast', 'h_16_rikt', 'h_16_hast', 'h_19_rikt', 'h_19_hast', 'h_22_rikt', 'h_22_hast']], rows=[('1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', 'Timsumma', 'Timmedel'), ('1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', 'Timsumma', 'Timmedel'), ('1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', 'Timsumma', 'Timmedel')], tables=[[9, 30], [9, 30], [9, 19]], row_sections=[[[1, 1], [5, 5], [6, 1], [1, 1], [1, 1]], [[1, 1], [5, 5], [6, 1], [1, 1], [1, 1]], [[1, 1], [5, 5], [6, 1], [1, 1], [1, 1]]], col_sections=[[[1, 30]], [[1, 30]], [[1, 11], [2, 8]]], divided_tables=[[34, 30], [34, 30], [34, 27]], preproc=PreprocConfig(table_modif=[[1, 1], [1, 1], [1, 1]], corr_rotate=False, row_idx_unit=<TimeUnits.NONE: 3>, method=<PreprocMethods.SCIPY_PROJ: 1>, idx_tables_size_verify=[0, 1, 2]), transforms=None, version='0', station='bjuroklubb_example')
INFO 2025-10-08 08:51:03,703 - dawsonia.io._pdf - INFO - pdf_info = {'Creator': 'pdftk-java 3.3.3', 'Producer': 'itext-paulo-155 (itextpdf.sf.net - lowagie.com)', 'CreationDate': 'Mon Oct 6 10:46:04 2025 CEST', 'ModDate': 'Mon Oct 6 10:46:04 2025 CEST', 'Tagged': 'no', 'UserProperties': 'no', 'Suspects': 'no', 'Form': 'none', 'JavaScript': 'no', 'Pages': 4, 'Encrypted': 'no', 'Page size': '848.16 x 1805.64 pts', 'Page rot': '0', 'File size': '55857749 bytes', 'Optimized': 'no', 'PDF version': '1.6'}
INFO 2025-10-08 08:51:03,712 - dawsonia.io._pdf - INFO - Setting first_page = 1
INFO 2025-10-08 08:51:03,815 - dawsonia.digitize - INFO - Digitizing Book(file=PosixPath('/data/proj12/geoford/dawsonia/data/raw/bjuroklubb_example/bjuroklubb_1955-01-01_1955- 02-31.pdf'), page_middle=None, table_format=TableFormat(name_idx=['x_idx_0', 'x_idx_1', 'x_idx_2'], columns=[['h_1', 'h_2', 'h_3', 'h_4', 'h_5', 'h_6', 'h_7', 'h_8', 'h_9', 'h_10', 'h_11', 'h_12', 'h_13', 'h_14', 'h_15', 'h_16', 'h_17', 'h_18', 'h_19', 'h_20', 'h_21', 'h_22', 'h_23', 'h_24', 'Dagssum', 'Dagsmed', 'Dagsmax', 'Dagsmin', 'Diff'], ['h_1', 'h_2', 'h_3', 'h_4', 'h_5', 'h_6', 'h_7', 'h_8', 'h_9', 'h_10', 'h_11', 'h_12', 'h_13', 'h_14', 'h_15', 'h_16', 'h_17', 'h_18', 'h_19', 'h_20', 'h_21', 'h_22', 'h_23', 'h_24', 'Dagssum', 'Dagsmed', 'Dagsmax', 'Dagsmin', 'Diff'], ['h_1', 'h_4', 'h_7', 'h_10', 'h_13', 'h_16', 'h_19', 'h_22', 'Dagssumma_rel_fukt', 'Dagsmedel_rel_fukt', 'h_1_rikt', 'h_1_hast', 'h_4_rikt', 'h_4_hast', 'h_7_rikt', 'h_7_hast', 'h_10_rikt', 'h_10_hast', 'h_13_rikt', 'h_13_hast', 'h_16_rikt', 'h_16_hast', 'h_19_rikt', 'h_19_hast', 'h_22_rikt', 'h_22_hast']], rows=[('1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', 'Timsumma', 'Timmedel'), ('1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', 'Timsumma', 'Timmedel'), ('1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', 'Timsumma', 'Timmedel')], tables=[[9, 30], [9, 30], [9, 19]], row_sections=[[[1, 1], [5, 5], [6, 1], [1, 1], [1, 1]], [[1, 1], [5, 5], [6, 1], [1, 1], [1, 1]], [[1, 1], [5, 5], [6, 1], [1, 1], [1, 1]]], col_sections=[[[1, 30]], [[1, 30]], [[1, 11], [2, 8]]], divided_tables=[[34, 30], [34, 30], [34, 27]], preproc=PreprocConfig(table_modif=[[1, 1], [1, 1], [1, 1]], corr_rotate=False, row_idx_unit=<TimeUnits.NONE: 3>, method=<PreprocMethods.SCIPY_PROJ: 1>, idx_tables_size_verify=[0, 1, 2]), transforms=None, version='0', station='bjuroklubb_example'), size_cell=[1.0, 1.0, 1.0, 1.0], preprocessor=Preprocessor())
INFO 2025-10-08 08:56:12,428 - dawsonia.digitize - INFO - Writing /data/proj12/geoford/dawsonia/digitized/bjuroklubb_example/bjuroklubb_1955-01-01_1955-02-31/page_1
INFO 2025-10-08 09:05:02,071 - dawsonia.digitize - INFO - page_number = 1 page_str = 'page_1'
INFO 2025-10-08 09:05:02,114 - dawsonia.digitize - INFO - output_stats = Statistics(predictions_total=858, predictions_above_thresh=451, predictions_empty_value=35, unset_values=0)
Tip
Python: A script can such as scripts/digitize.py or scripts/digitize_api.py may be used.
CLI:
Some of these options are repeatable and are best stored in a cfg file such as cfg/lumi.toml which is present in the dawsonia repository. Once created this file can be supplied to the command line as dawsonia digitize -c cfg/myconfig.toml.
!tail -n 6 cfg/lumi.toml
[dawsonia.digitize]
first_page = 3
last_page = 100000
verbose = true
model_path = "~/scratch/data/processed/dawsonia_model_2022-12-19"
output_path = "~/scratch/data/processed/digitized_2022-12-19"
Results#
As mentioned earlier, for every page a couple of files are generated. Let’s take a look at these.
ls digitized/bjuroklubb_example/bjuroklubb_1955-01-01_1955-02-31/*
digitized/bjuroklubb_example/bjuroklubb_1955-01-01_1955-02-31/page_1_table_0.parquet
digitized/bjuroklubb_example/bjuroklubb_1955-01-01_1955-02-31/page_1_table_1.parquet
digitized/bjuroklubb_example/bjuroklubb_1955-01-01_1955-02-31/page_1_table_2.parquet
digitized/bjuroklubb_example/bjuroklubb_1955-01-01_1955-02-31/pages:
page_1.webp
digitized/bjuroklubb_example/bjuroklubb_1955-01-01_1955-02-31/probabilities:
page_1_table_0.parquet page_1_table_1.parquet page_1_table_2.parquet
digitized/bjuroklubb_example/bjuroklubb_1955-01-01_1955-02-31/statistics:
page_1_table_0.json page_1_table_1.json page_1_table_2.json
digitized/bjuroklubb_example/bjuroklubb_1955-01-01_1955-02-31/table_meta:
page_1_table_0.json page_1_table_1.json page_1_table_2.json
import json
from pandas import read_parquet
from PIL import Image
def read_json(path):
return json.loads(Path(path).read_text())
Preprocessed Image#
im = Image.open("digitized/bjuroklubb_example/bjuroklubb_1955-01-01_1955-02-31/pages/page_1.webp")
im
Digitized Text#
read_parquet("digitized/bjuroklubb_example/bjuroklubb_1955-01-01_1955-02-31/page_1_table_0.parquet")
| h_1 | h_2 | h_3 | h_4 | h_5 | h_6 | h_7 | h_8 | h_9 | h_10 | ... | h_20 | h_21 | h_22 | h_23 | h_24 | Dagssum | Dagsmed | Dagsmax | Dagsmin | Diff | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| x_idx_0 | |||||||||||||||||||||
| 1 | +3 | +0 | + | +0 | +19.9 | +9.9 | +19.9 | +9.6 | +9.6 | +19.4 | ... | 10 | +9.4 | +0.4 | +0.5 | +9.5 | +8.8 | +0.3 | +0.8 | +0 | 0 |
| 2 | +9.5 | +19.6 | +9.5 | +19.9 | 1 | + | +9.5 | +19.4 | +9.6 | +9.6 | ... | +1.8 | +1.2 | +1.4 | 1.2 | +1.0 | +18.9 | +0.8 | +1.4 | +0.2 | 1.2 |
| 3 | +1.9 | +19.9 | +1.0 | +1.3 | +19 | +1.0 | +19.5 | +9. | -9.2 | +19.5 | ... | -1.0 | -1 | -1.2 | -1.0 | -1.0 | -3.4 | -0.1 | +1.3 | -1.6 | 3 |
| 4 | -1.9 | -1 | 1 | -1.4 | -1.4 | -1.5 | -1.8 | -1.9 | -1.6 | -1.6 | ... | -174.7 | -.1 | -6.4 | -.0 | -6.1 | -67.5 | -2.8 | -1.0 | -6.4 | 5.4 |
| 5 | -6.4 | -6.0 | -.1 | -5.8 | -5.7 | -6.3 | -7.1 | -7.1 | -4.4 | -3.4 | ... | -1.2 | -9.6 | -3.0 | -0 | -1.5 | -10.9.6 | -4.6 | -1.3 | -72.1 | 56 |
| 6 | -1.0 | -12 | 1.2 | 1 | -1.9 | -1.3 | -1 | -1.0 | -18.7 | -0 | ... | -1.5 | -1.9 | -2.6 | -0.3 | -.9 | -2.7.0 | -1.1 | +7.9 | -9.9 | 3 |
| 7 | -2.5 | -9.9 | 9. | -7. | -1.0 | -1.0 | -4.5 | -0.8 | -19.4 | -0.5 | ... | -1 | -1 | -1.2 | -1.4 | -1.4 | -41.8 | -1.2 | +0.2 | -4.3 | 4.2 |
| 8 | -1. | -2.0 | 7 | -7.5 | -7.4 | -7.5 | -2.7 | +. | -2.6 | -3.7 | ... | 1 | -1 | -1.4 | -1.0 | -1.0 | -4.3.1 | -1.8 | -1.0 | -2.7 | 1 |
| 9 | -1.2 | 1.0 | -1.0 | -1.7 | -1.1 | -1.1 | -1.4 | -1.1 | 1 | -0.8 | ... | -1.9 | -1.8 | -2.5 | -.2 | -.0 | -2.8.0 | -1. | -0.6 | -9.3 | 19 |
| 10 | -1.8 | -1.6 | 1 | -1.7 | -1.6 | -1.5. | -1.4 | -1.5 | -0.0 | -0.2 | ... | -9.1 | 0.7 | +16.6 | -1.8 | +0.9 | -29.5.8 | -1.1 | 70.9 | -9.2 | +.1 |
| 11 | +19.6 | +1.7 | +1.7 | +3.0 | +9 | 10 | +1.4 | +14 | +1.4 | +1.5 | ... | +0.4 | +9.0 | -0.6 | -1.1 | 0.3 | +2.5.5 | +1.1 | +2.3 | -0.3 | 2.6 |
| 12 | -19.8 | -.7 | -19.7 | -1.0 | -14.9 | -1.0 | -1.0 | -1.0 | -1.0 | -19. | ... | +109.3 | +1.0 | +1.0 | +1.0 | 10 | -.8 | -0.2 | +1.0 | -1.1 | 9.1 |
| 13 | +19.8 | +190.9 | +19.4 | -9.2 | +19.0 | +9.2 | +9.0 | +19.2 | +10.7 | +1.5 | ... | 10 | +9.0 | -9.1 | -2.3 | -2.4 | +9.5 | +0.4 | +2.0 | -2.4 | 61.4 |
| 14 | -1.5 | -9.9 | -. | +9.0 | +19.0 | +10.3 | -4.4 | +0.7 | +0.7 | +10.8 | ... | -2.5 | -2.7 | -41.4 | -9.8 | -2.8 | -18.6 | -0.8 | +1.0 | -4.4 | 5-.4 |
| 15 | -11.5 | -4.7 | -14.4. | -71.0 | -14.0 | -9.7 | -17.0 | -9.9 | -7.5 | -2.2 | ... | -0.6 | +0.4 | -6.6 | +0.4 | +10.3 | -3.7 | -1.8 | 48.7 | -4.2 | 5-.4 |
| 16 | +1.1 | -14.2 | -1.4 | -. | +9. | -19.7 | -9.4 | +11.0 | +19.0 | -0. | ... | -.2 | -0.3 | -9.6 | -0.7 | -09.6 | -8.1 | -0.1 | +0.4 | -0.2 | 1.1 |
| 17 | -9.5 | -19 | -9.7 | -7.9 | -4.8 | - | -5.1 | -5.6 | -5.7 | -5-9 | ... | -1.0 | 0.7 | -1.0 | 0.4 | -0.3 | -62.1 | -2.8 | -0.1 | -5.9 | 5-8 |
| 18 | -0.8 | -0.2 | -9.4 | -1.0 | -9.7 | -1.9 | -1.0 | -0.9 | -0.7 | -0. | ... | -5.0 | -9.0 | -97.5 | -10.9 | -10.7 | -10.7 | -2.5 | -0.2 | -10.9 | 10.7 |
| 19 | -8.6 | 6 | -6.8 | -6.9 | -5.-9 | -5.-4 | -6.9 | -5.6 | -71.9 | -8.2 | ... | -1.4 | -1.3 | -2.2 | -2. | -2.2 | -10.6.0 | -4.4 | -0.5 | -10.7 | 102 |
| 20 | -2.7 | -3.0 | -3. | -3.2 | -9.7 | 91 | -7.5 | -4.7 | -4.7 | -7.8. | ... | -6.1 | -79.9 | -97.0 | -97.0 | -10.5 | -108.6 | -4.3 | -1.4 | -10.5 | 9.1 |
| 21 | -10.7 | -10.3 | -107 | -11.2 | -11.2 | -11.1 | -11.6 | -9.4 | -7.4 | -7.0 | ... | -0.9 | -0.9 | -0.6 | -1.1 | -1.2 | -12.5 | -5.1 | -0.2 | -11.7 | 11.3 |
| 22 | -9.8 | 1 | -1.3 | -1 | -1.4 | -1.5 | -1.4 | -14 | -1.4 | -1.3 | ... | -2.3 | -2.3 | -9.0 | -2.2 | -1.9 | -39.3 | -1.6 | -0.8 | -2.4 | 1.6 |
| 23 | -1 | -19 | -29.0 | -9.0 | -9.9 | -8.2 | -9.4 | -29.2 | -9.7 | -9.8 | ... | -.1 | -3.0 | -2.9 | -2.0 | -2.9 | -6.5.0 | -2.7 | -1.4 | -3.4 | 2.0 |
| 24 | -8.4 | 9.3 | -9.3 | -9.7 | -2.8 | -2.6 | -19 | -1. | -0.6 | -0.6 | ... | +9.0 | +0.0 | +0.0 | +0.9.1 | +0.3 | -2.6.2 | -1.1 | 60.3 | -2.8 | 9.3 |
| 25 | +9.4 | +0.6 | +0.2 | +0 | +0.7 | +0.2 | +0.9 | +1.0 | +1.0 | +1.1 | ... | +1.3 | +1.3 | +1.4 | +1.3 | +13 | +2.7.3 | +1.1 | +1.8 | +0.4 | 14 |
| 26 | +1.0 | +1.0 | +1.0 | +9.8 | +9.9 | +9.9 | +1 | +1.1 | +1.9 | +1.4 | ... | +1.2 | +1.5 | +1.4 | +1.8 | +0.6 | +31.0 | +1.3 | +1.2 | +0.6 | 1.1 |
| 27 | +17.9 | -9 | -19.4 | -19.6 | -1.0 | -1.0 | -10 | -0.8 | -0.6 | -.4 | ... | +0.2 | +0.2 | +09.6 | +0.9.3 | +0.3 | -3.2 | -0.1 | +9.6 | -1.0 | 1.6 |
| 28 | +9.6 | +9 | +19.9 | +94 | +9.0 | + | +19.6 | +9.6 | +19.6 | +19 | ... | +1.2 | +1.8 | +1.5 | +1.5 | +1.5 | +24.0 | +1.0 | +1.8 | +09.9.0 | 18 |
| 29 | +1.5 | +1 | +1.6 | +19 | +1.8 | +19 | +2.0 | +1.8 | +1.6 | +9. | ... | +1.7 | +1.9 | +2.2 | +2.0 | +2.0 | +42.8 | +1.8 | +2.9 | +1.0 | 1.2 |
| 30 | +9. | +9 | +19 | +1.8 | +1.8 | +1.8 | +1.4 | +1. | +1.7 | +2.0 | ... | +2.0 | +2.0 | +2.4 | +2.2 | +2.2 | +5-4.1 | +2.3 | +9.8 | +1.4 | 2.4 |
| 31 | +2.0 | +9 | +2.0 | +8.2 | +29.0 | +29.9 | +1 | +1.9 | +1.8 | +2.0 | ... | +2.3 | +2.3 | +12.0 | +2.0 | +2.0 | +52.1 | +2.2 | +2.6 | +1.8 | 0.8 |
| Timsumma | -7.0.3 | -3.7.1 | -7.8 | -41.0 | -1.4 | -71.3 | -49.6 | -4.7 | -43.6 | -3.3.5 | ... | -8.5.5 | -9.8.1 | -3.5.-2 | -3.2.2 | -40.1 | -78.4.1 | -2.9.8 | +19.2 | -97.3 | 16.3 |
| Timmedel | -1.3 | -1.9 | -1.2 | -1.3 | -13 | -1.3 | -1.6 | -1.4 | -1.4 | -1 | ... | -0.8 | -0.9 | -1.2 | -1.2 | -1.3 | -2.3.4 | -1.0 | +0.6 | -3.1 | 3.2 |
33 rows × 29 columns
Probabilities#
read_parquet("digitized/bjuroklubb_example/bjuroklubb_1955-01-01_1955-02-31/probabilities/page_1_table_0.parquet")
| h_1 | h_2 | h_3 | h_4 | h_5 | h_6 | h_7 | h_8 | h_9 | h_10 | ... | h_20 | h_21 | h_22 | h_23 | h_24 | Dagssum | Dagsmed | Dagsmax | Dagsmin | Diff | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| x_idx_0 | |||||||||||||||||||||
| 1 | 0.498295 | 0.885108 | 0.674490 | 0.683282 | 0.950026 | 0.581614 | 0.866639 | 0.947015 | 0.414423 | 0.819429 | ... | 0.793345 | 0.521802 | 0.686503 | 0.931075 | 0.941542 | 0.995900 | 0.989912 | 0.501081 | 0.795301 | 0.959181 |
| 2 | 0.517834 | 0.755034 | 0.511505 | 0.977744 | 0.306842 | 0.475054 | 0.996679 | 0.987549 | 0.794650 | 0.808061 | ... | 0.836200 | 0.626853 | 0.994711 | 0.546722 | 0.995345 | 0.996014 | 0.997245 | 0.996367 | 0.971925 | 0.470608 |
| 3 | 0.976551 | 0.283201 | 0.897350 | 0.990303 | 0.358959 | 0.853650 | 0.723042 | 0.347780 | 0.233546 | 0.852524 | ... | 0.990827 | 0.711731 | 0.984613 | 0.995285 | 0.993742 | 0.996227 | 0.987305 | 0.975413 | 0.980657 | 0.621582 |
| 4 | 0.989451 | 0.381994 | 0.593962 | 0.995000 | 0.987976 | 0.986429 | 0.980372 | 0.919672 | 0.992267 | 0.955596 | ... | 0.528854 | 0.628618 | 0.992973 | 0.693842 | 0.972284 | 0.786148 | 0.988550 | 0.994838 | 0.991135 | 0.723752 |
| 5 | 0.992398 | 0.952599 | 0.789525 | 0.989479 | 0.979807 | 0.984364 | 0.518328 | 0.986275 | 0.459592 | 0.984863 | ... | 0.461328 | 0.754929 | 0.684782 | 0.729066 | 0.893389 | 0.568721 | 0.991833 | 0.989458 | 0.593869 | 0.292553 |
| 6 | 0.598214 | 0.740498 | 0.346567 | 0.984313 | 0.912602 | 0.687751 | 0.582362 | 0.973084 | 0.956532 | 0.735569 | ... | 0.970538 | 0.965201 | 0.985654 | 0.659365 | 0.838392 | 0.733432 | 0.980706 | 0.648972 | 0.955184 | 0.772342 |
| 7 | 0.148445 | 0.995210 | 0.346172 | 0.613340 | 0.419690 | 0.378204 | 0.463664 | 0.368624 | 0.912426 | 0.687381 | ... | 0.944117 | 0.775383 | 0.996271 | 0.992739 | 0.993206 | 0.367544 | 0.501311 | 0.883440 | 0.996325 | 0.795014 |
| 8 | 0.739603 | 0.727313 | 0.613253 | 0.697560 | 0.493054 | 0.688110 | 0.724366 | 0.189123 | 0.262072 | 0.439535 | ... | 0.919906 | 0.687334 | 0.987328 | 0.993232 | 0.987827 | 0.337474 | 0.729924 | 0.997566 | 0.813576 | 0.911319 |
| 9 | 0.936105 | 0.392152 | 0.931561 | 0.826585 | 0.391210 | 0.573110 | 0.729511 | 0.938627 | 0.702621 | 0.814690 | ... | 0.962001 | 0.931164 | 0.811322 | 0.487878 | 0.636506 | 0.520276 | 0.550508 | 0.900408 | 0.841974 | 0.752243 |
| 10 | 0.994774 | 0.484769 | 0.617160 | 0.987683 | 0.836280 | 0.680004 | 0.991408 | 0.983462 | 0.997145 | 0.904705 | ... | 0.878730 | 0.215588 | 0.693577 | 0.607526 | 0.985647 | 0.478157 | 0.994314 | 0.553030 | 0.755956 | 0.330125 |
| 11 | 0.959532 | 0.947237 | 0.949921 | 0.234881 | 0.532212 | 0.780039 | 0.994168 | 0.910856 | 0.987216 | 0.995365 | ... | 0.639379 | 0.792560 | 0.598071 | 0.748385 | 0.222958 | 0.402674 | 0.990098 | 0.996742 | 0.716024 | 0.942621 |
| 12 | 0.420039 | 0.627656 | 0.982497 | 0.920079 | 0.353741 | 0.979206 | 0.882891 | 0.994553 | 0.518316 | 0.500579 | ... | 0.558037 | 0.953470 | 0.915392 | 0.953557 | 0.441780 | 0.293288 | 0.997537 | 0.995462 | 0.992486 | 0.398052 |
| 13 | 0.865887 | 0.068990 | 0.990130 | 0.792279 | 0.774885 | 0.310957 | 0.541492 | 0.833363 | 0.774837 | 0.918206 | ... | 0.625538 | 0.911971 | 0.941511 | 0.752514 | 0.939486 | 0.998234 | 0.997300 | 0.605329 | 0.996343 | 0.990229 |
| 14 | 0.982959 | 0.950804 | 0.307764 | 0.937301 | 0.856876 | 0.617961 | 0.550084 | 0.700160 | 0.988812 | 0.593650 | ... | 0.997010 | 0.608770 | 0.710654 | 0.763426 | 0.652619 | 0.998266 | 0.997266 | 0.942335 | 0.244909 | 0.784414 |
| 15 | 0.843728 | 0.904700 | 0.397169 | 0.444211 | 0.854889 | 0.966520 | 0.808396 | 0.720502 | 0.898156 | 0.960612 | ... | 0.771631 | 0.948583 | 0.479724 | 0.959480 | 0.753463 | 0.767649 | 0.991379 | 0.189150 | 0.809246 | 0.828232 |
| 16 | 0.705661 | 0.299068 | 0.639002 | 0.781428 | 0.815185 | 0.786858 | 0.899214 | 0.576199 | 0.968007 | 0.376183 | ... | 0.485642 | 0.849813 | 0.890556 | 0.844611 | 0.524251 | 0.548899 | 0.997714 | 0.998426 | 0.994476 | 0.967033 |
| 17 | 0.892709 | 0.391129 | 0.120386 | 0.974991 | 0.318267 | 0.524744 | 0.993157 | 0.993403 | 0.959163 | 0.642017 | ... | 0.997199 | 0.724594 | 0.972923 | 0.709473 | 0.989169 | 0.342574 | 0.992471 | 0.985132 | 0.954185 | 0.636255 |
| 18 | 0.940431 | 0.686458 | 0.991268 | 0.944702 | 0.896489 | 0.392258 | 0.548147 | 0.990436 | 0.906196 | 0.631242 | ... | 0.992204 | 0.323232 | 0.764415 | 0.997704 | 0.982368 | 0.719782 | 0.996164 | 0.992604 | 0.997320 | 0.592776 |
| 19 | 0.951795 | 0.812534 | 0.969656 | 0.722644 | 0.822261 | 0.862806 | 0.952037 | 0.838533 | 0.785840 | 0.659604 | ... | 0.996152 | 0.992053 | 0.992677 | 0.663236 | 0.857993 | 0.946473 | 0.998227 | 0.936669 | 0.943500 | 0.414350 |
| 20 | 0.666042 | 0.943639 | 0.501248 | 0.989002 | 0.703234 | 0.525329 | 0.612172 | 0.995716 | 0.997630 | 0.934813 | ... | 0.968508 | 0.381142 | 0.790364 | 0.854470 | 0.998159 | 0.531044 | 0.981936 | 0.935112 | 0.985716 | 0.968153 |
| 21 | 0.913576 | 0.896931 | 0.554079 | 0.533989 | 0.573278 | 0.987915 | 0.995385 | 0.861188 | 0.934576 | 0.537978 | ... | 0.992272 | 0.995650 | 0.995457 | 0.982607 | 0.992275 | 0.710291 | 0.993299 | 0.995923 | 0.969728 | 0.986962 |
| 22 | 0.459620 | 0.590256 | 0.977595 | 0.514162 | 0.989039 | 0.991289 | 0.418122 | 0.627106 | 0.926899 | 0.909201 | ... | 0.995162 | 0.977427 | 0.883882 | 0.964220 | 0.993266 | 0.934821 | 0.995656 | 0.996087 | 0.985775 | 0.714780 |
| 23 | 0.729826 | 0.513900 | 0.800037 | 0.996372 | 0.930244 | 0.561047 | 0.989929 | 0.652518 | 0.904020 | 0.918105 | ... | 0.428738 | 0.995496 | 0.991327 | 0.885486 | 0.987943 | 0.992885 | 0.940375 | 0.993268 | 0.997721 | 0.989626 |
| 24 | 0.519304 | 0.276573 | 0.769774 | 0.290174 | 0.839310 | 0.984635 | 0.808024 | 0.567548 | 0.958587 | 0.914344 | ... | 0.787988 | 0.622823 | 0.994072 | 0.444934 | 0.996671 | 0.908770 | 0.986922 | 0.864306 | 0.990899 | 0.511062 |
| 25 | 0.602360 | 0.696177 | 0.457908 | 0.985394 | 0.715372 | 0.790295 | 0.994836 | 0.995177 | 0.996801 | 0.894454 | ... | 0.985993 | 0.985011 | 0.981746 | 0.750546 | 0.605502 | 0.957452 | 0.991591 | 0.995183 | 0.997817 | 0.970102 |
| 26 | 0.734024 | 0.757787 | 0.977822 | 0.944830 | 0.763402 | 0.899382 | 0.656619 | 0.771590 | 0.993804 | 0.995211 | ... | 0.464653 | 0.996136 | 0.958192 | 0.624747 | 0.980708 | 0.980822 | 0.980055 | 0.964992 | 0.985993 | 0.967696 |
| 27 | 0.780266 | 0.575636 | 0.944534 | 0.892930 | 0.529008 | 0.619673 | 0.661430 | 0.759215 | 0.432381 | 0.431162 | ... | 0.885969 | 0.849942 | 0.516636 | 0.384058 | 0.994258 | 0.630770 | 0.990292 | 0.794937 | 0.997555 | 0.521676 |
| 28 | 0.544282 | 0.343124 | 0.980354 | 0.311609 | 0.650822 | 0.613730 | 0.967336 | 0.870487 | 0.933607 | 0.645036 | ... | 0.985102 | 0.872654 | 0.561003 | 0.994831 | 0.994879 | 0.883419 | 0.991263 | 0.992250 | 0.283137 | 0.997999 |
| 29 | 0.992851 | 0.767142 | 0.995544 | 0.519021 | 0.968538 | 0.602522 | 0.810056 | 0.993882 | 0.710830 | 0.528276 | ... | 0.923073 | 0.993001 | 0.995818 | 0.992934 | 0.886516 | 0.763453 | 0.992854 | 0.747864 | 0.996622 | 0.706169 |
| 30 | 0.497377 | 0.697068 | 0.917373 | 0.989462 | 0.979166 | 0.907472 | 0.981967 | 0.698654 | 0.955140 | 0.508648 | ... | 0.830869 | 0.981736 | 0.839182 | 0.769641 | 0.736935 | 0.863255 | 0.995708 | 0.935230 | 0.995101 | 0.762459 |
| 31 | 0.830870 | 0.963394 | 0.478550 | 0.354219 | 0.759173 | 0.783952 | 0.977717 | 0.841375 | 0.996154 | 0.992486 | ... | 0.638633 | 0.735912 | 0.976256 | 0.935111 | 0.998693 | 0.988175 | 0.971743 | 0.996915 | 0.996565 | 0.531100 |
| Timsumma | 0.306575 | 0.567650 | 0.388773 | 0.635236 | 0.943881 | 0.447354 | 0.575392 | 0.770296 | 0.950606 | 0.471826 | ... | 0.577462 | 0.511492 | 0.436351 | 0.298587 | 0.961366 | 0.359612 | 0.237609 | 0.977278 | 0.625440 | 0.559339 |
| Timmedel | 0.916281 | 0.949941 | 0.378573 | 0.916709 | 0.837222 | 0.984855 | 0.989611 | 0.997784 | 0.998348 | 0.398830 | ... | 0.996419 | 0.991437 | 0.988111 | 0.996177 | 0.995938 | 0.963245 | 0.996691 | 0.998088 | 0.991977 | 0.528390 |
33 rows × 29 columns
Statistics#
read_json("digitized/bjuroklubb_example/bjuroklubb_1955-01-01_1955-02-31/statistics/page_1_table_0.json")
{'predictions_total': 957,
'predictions_above_thresh': 565,
'predictions_empty_value': 0,
'unset_values': 0}
Table Meta#
table_meta = read_json("digitized/bjuroklubb_example/bjuroklubb_1955-01-01_1955-02-31/table_meta/page_1_table_0.json")
table_meta
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