« Programming (Bitcoin) Ordinals - Step-by-Step Book / Guide
Let's explore ordinal inscriptions from a different angle. Let's try classic (or yes, generative) collections.
New to Ordinal Punks? For some background see Awesome 100 Ordinal Punks (Anno 2023) Notes - 24×24 Pixel Art on the (Bitcoin) Blockchain »
Let's start from zero / scratch with a new database (e.g. ord.db)
and let's import all inscriptions. How?
Yes, ordinal punks has an official meta datafile (in .json) inscribed at №592145.
Let's use a "right-click and save as" local copy (e.g. ordinalpunks.json) to import all inscriptions - metadata and content blobs - and bonus all collection metadata (e.g. description, max (limit/supply), item names, etc.).
require 'ordlite'
OrdDb.open( './ord.db' )
puts
puts " #{Inscribe.count} inscribe(s)"
puts " #{Blob.count} blob(s)"
puts " #{Collection.count} collection(s)"
#=> 0 inscribe(s)
#=> 0 blob(s)
#=> 0 collection(s)
OrdDb.import_collection( "./meta/ordinalpunks.json" )
puts
puts " #{Inscribe.count} inscribe(s)"
puts " #{Blob.count} blob(s)"
puts " #{Collection.count} collection(s)"
#=> 100 inscribe(s)
#=> 100 blob(s)
#=> 1 collection(s)Yes, that's it.
Thanks to import_collection you only need to stand back ten meters
and wait for some minutes to get your database updated.
Now what? Let's try some count stat(istics) queries.
col = Collection.find_by( name: 'Ordinal Punks' )
inscribes = col.inscribes
puts " #{inscribes.size} inscribe(s)"
#=> 100 inscribe(s)Let's query for content types (of inscriptions):
pp inscribes.counts_by_content_typeresulting in:
{"image/png" => 100}
Let's query for min and max inscription numbers / no.:
max = inscribes.maximum( 'num' )
min = inscribes.minimum( 'num' )
puts " range no. #{min} to no. #{max}"
#=> range no. 407 to no. 642Let's query for inscriptions counts grouped by date / day:
pp inscribes.counts_by_dayresulting in:
{"2023-01-31" => 27,
"2023-02-01" => 73}
or by hour:
pp inscribes.counts_by_hourresulting in:
{"2023-01-31 19h"=>1,
"2023-01-31 20h"=>2,
"2023-01-31 21h"=>12,
"2023-01-31 22h"=>1,
"2023-01-31 23h"=>11,
"2023-02-01 01h"=>3,
"2023-02-01 02h"=>7,
"2023-02-01 03h"=>7,
"2023-02-01 04h"=>5,
"2023-02-01 05h"=>4,
"2023-02-01 10h"=>1,
"2023-02-01 14h"=>1,
"2023-02-01 15h"=>5,
"2023-02-01 16h"=>12,
"2023-02-01 17h"=>13,
"2023-02-01 18h"=>10,
"2023-02-01 19h"=>5}
or by block (height) with or without timestamp:
pp inscribes.counts_by_block
pp inscribes.counts_by_block_with_timestampresulting in:
{774489=>1,
774494=>1,
774496=>1,
774497=>6,
774500=>3,
774502=>3,
774503=>1,
774506=>5,
774507=>1,
774509=>5,
774515=>1,
774518=>1,
774519=>1,
774521=>1,
774523=>1,
774524=>5,
774527=>2,
774531=>5,
774534=>1,
774536=>2,
774537=>2,
774541=>4,
774567=>1,
774587=>1,
774595=>5,
774598=>5,
774599=>4,
774600=>3,
774604=>2,
774605=>3,
774606=>4,
774607=>4,
774609=>3,
774611=>3,
774612=>3,
774614=>1,
774615=>2,
774617=>2,
774619=>1}
{"774489 @ 2023-01-31 19:34:47"=>1,
"774494 @ 2023-01-31 20:23:09"=>1,
"774496 @ 2023-01-31 20:42:21"=>1,
"774497 @ 2023-01-31 21:08:45"=>6,
"774500 @ 2023-01-31 21:33:58"=>3,
"774502 @ 2023-01-31 21:58:16"=>3,
"774503 @ 2023-01-31 22:24:06"=>1,
"774506 @ 2023-01-31 23:04:21"=>5,
"774507 @ 2023-01-31 23:24:36"=>1,
"774509 @ 2023-01-31 23:40:19"=>5,
"774515 @ 2023-02-01 01:15:25"=>1,
"774518 @ 2023-02-01 01:36:30"=>1,
"774519 @ 2023-02-01 01:42:29"=>1,
"774521 @ 2023-02-01 02:01:50"=>1,
"774523 @ 2023-02-01 02:17:58"=>1,
"774524 @ 2023-02-01 02:49:29"=>5,
"774527 @ 2023-02-01 03:30:11"=>2,
"774531 @ 2023-02-01 03:55:44"=>5,
"774534 @ 2023-02-01 04:09:38"=>1,
"774536 @ 2023-02-01 04:29:27"=>2,
"774537 @ 2023-02-01 04:46:11"=>2,
"774541 @ 2023-02-01 05:16:44"=>4,
"774567 @ 2023-02-01 10:17:24"=>1,
"774587 @ 2023-02-01 14:41:52"=>1,
"774595 @ 2023-02-01 15:54:57"=>5,
"774598 @ 2023-02-01 16:24:14"=>5,
"774599 @ 2023-02-01 16:32:16"=>4,
"774600 @ 2023-02-01 16:42:58"=>3,
"774604 @ 2023-02-01 17:12:08"=>2,
"774605 @ 2023-02-01 17:35:29"=>3,
"774606 @ 2023-02-01 17:40:32"=>4,
"774607 @ 2023-02-01 17:59:12"=>4,
"774609 @ 2023-02-01 18:13:13"=>3,
"774611 @ 2023-02-01 18:20:16"=>3,
"774612 @ 2023-02-01 18:33:59"=>3,
"774614 @ 2023-02-01 18:46:46"=>1,
"774615 @ 2023-02-01 19:13:31"=>2,
"774617 @ 2023-02-01 19:20:24"=>2,
"774619 @ 2023-02-01 19:27:27"=>1}
and so on.
Let's try the Shrooms. Shrooms what!?
Ordinals trivia. The Shrooms with 208 out of 210 inscriptions¹ (plus one bonus collection metadata inscription, see №105) is the biggest sub 1k collection.
Note 1: Shroom №186 @ Inscription №1075 and Shroom №196 @ Inscription №1074.
Let's use a "hand-crafted" tabular dataset (in .csv) e.g. shrooms_inscriptions.csv to import all inscriptions - metadata and content blobs - and bonus add a collection.
OrdDb.import_collection_csv( "./meta/shrooms_inscriptions.csv",
name: 'Bitcoin Shrooms' )Yes, that's it.
Thanks to import_collection_csv you only need to stand back ten meters
and wait for some minutes to get your database updated.
Now what? Let's try again some count stat(istics) queries.
col = Collection.find_by( name: 'Bitcoin Shrooms' )
inscribes = col.inscribes
puts " #{inscribes.size} inscribe(s)"
#=> 210 inscribe(s)Let's query for content types (of inscriptions):
pp inscribes.counts_by_content_typeresulting in:
{"image/png" => 210}
Let's query for min and max inscription numbers / no.:
max = inscribes.maximum( 'num' )
min = inscribes.minimum( 'num' )
puts " range no. #{min} to no. #{max}"
#=> range no. 675 to no. 1075Let's query for inscriptions counts grouped by date / day:
pp inscribes.counts_by_dayresulting in:
{"2023-02-02" => 210}
or by hour:
pp inscribes.counts_by_hourresulting in:
{"2023-02-02 00h" => 43,
"2023-02-02 01h" => 124,
"2023-02-02 02h" => 41,
"2023-02-02 19h" => 2}
or by block (height) with or without timestamp:
pp inscribes.counts_by_block
pp inscribes.counts_by_block_with_timestampresulting in:
{774656=>43,
774657=>57,
774658=>38,
774659=>6,
774661=>2,
774662=>1,
774663=>5,
774664=>15,
774666=>5,
774670=>10,
774671=>15,
774672=>11,
774761=>1,
774762=>1}
{"774656 @ 2023-02-02 00:44:05"=>43,
"774657 @ 2023-02-02 01:00:47"=>57,
"774658 @ 2023-02-02 01:03:02"=>38,
"774659 @ 2023-02-02 01:08:00"=>6,
"774661 @ 2023-02-02 01:17:54"=>2,
"774662 @ 2023-02-02 01:27:47"=>1,
"774663 @ 2023-02-02 01:30:40"=>5,
"774664 @ 2023-02-02 01:44:23"=>15,
"774666 @ 2023-02-02 02:06:44"=>5,
"774670 @ 2023-02-02 02:31:41"=>10,
"774671 @ 2023-02-02 02:34:37"=>15,
"774672 @ 2023-02-02 02:49:42"=>11,
"774761 @ 2023-02-02 19:01:04"=>1,
"774762 @ 2023-02-02 19:06:48"=>1}
and so on.
Let's try D.I.Y. Punks - (On-Chain) Generatives!
New to Geratives! Your Numbers! Your Image!? For some background see Ordgen / ORC-721 »
Let's use an auto-generated (market) dataset (in .json) e.g. diypunks_inscriptions.json to import all inscriptions - metadata and content blobs - and bonus add a collection.
OrdDb.import_collection_inscriptions( "./meta/diypunks_inscriptions.json",
name: 'D.I.Y. Punks' )Yes, that's it.
Thanks to import_collection_inscriptions you only need to stand back ten meters
and wait for some minutes to get your database updated.
Now what? Let's try again some count stat(istics) queries.
col = Collection.find_by( name: 'D.I.Y. Punks' )
inscribes = col.inscribes
puts " #{inscribes.size} inscribe(s)"
#=> 721 inscribe(s)Let's query for content types (of inscriptions):
pp inscribes.counts_by_content_typeresulting in:
{"text/plain;charset=utf-8"=>717,
"application/json"=>4}
Yes, that's text (in .json). Generatives store the dna (numbers) NOT the image!
Let's query for min and max inscription numbers / no.:
max = inscribes.maximum( 'num' )
min = inscribes.minimum( 'num' )
puts " range no. #{min} to no. #{max}"
#=> range no. 9959200 to no. 10286852Let's query for inscriptions counts grouped by date / day:
pp inscribes.counts_by_dayresulting in:
{"2023-05-29" => 240,
"2023-05-30" => 298,
"2023-05-31" => 183}
or by hour:
pp inscribes.counts_by_hourresulting in:
{"2023-05-29 17h"=>1,
"2023-05-29 18h"=>1,
"2023-05-29 19h"=>37,
"2023-05-29 20h"=>15,
"2023-05-29 21h"=>123,
"2023-05-29 22h"=>8,
"2023-05-29 23h"=>55,
"2023-05-30 00h"=>28,
"2023-05-30 01h"=>32,
"2023-05-30 02h"=>23,
"2023-05-30 03h"=>56,
"2023-05-30 04h"=>27,
"2023-05-30 05h"=>19,
"2023-05-30 06h"=>58,
"2023-05-30 07h"=>1,
"2023-05-30 09h"=>15,
"2023-05-30 10h"=>1,
"2023-05-30 11h"=>2,
"2023-05-30 12h"=>10,
"2023-05-30 13h"=>11,
"2023-05-30 21h"=>2,
"2023-05-30 22h"=>1,
"2023-05-30 23h"=>12,
"2023-05-31 00h"=>11,
"2023-05-31 01h"=>7,
"2023-05-31 02h"=>12,
"2023-05-31 04h"=>2,
"2023-05-31 06h"=>6,
"2023-05-31 07h"=>5,
"2023-05-31 08h"=>68,
"2023-05-31 09h"=>70,
"2023-05-31 10h"=>2}
or by block (height) with or without timestamp:
pp inscribes.counts_by_block
pp inscribes.counts_by_block_with_timestampresulting in:
{"791978 @ 2023-05-29 17:53:55"=>1,
"791982 @ 2023-05-29 18:09:37"=>1,
"791989 @ 2023-05-29 19:06:49"=>21,
"791991 @ 2023-05-29 19:13:17"=>5,
"791996 @ 2023-05-29 19:37:16"=>9,
"791997 @ 2023-05-29 19:41:28"=>2,
"791998 @ 2023-05-29 20:01:16"=>11,
"791999 @ 2023-05-29 20:00:56"=>3,
"792001 @ 2023-05-29 20:03:58"=>1,
"792002 @ 2023-05-29 21:01:56"=>33,
"792003 @ 2023-05-29 21:06:34"=>32,
"792004 @ 2023-05-29 21:19:45"=>14,
"792005 @ 2023-05-29 21:23:31"=>7,
"792006 @ 2023-05-29 21:24:12"=>2,
"792007 @ 2023-05-29 21:29:17"=>24,
"792008 @ 2023-05-29 21:31:38"=>11,
"792009 @ 2023-05-29 22:04:04"=>4,
"792011 @ 2023-05-29 22:57:50"=>2,
"792012 @ 2023-05-29 22:59:48"=>2,
"792013 @ 2023-05-29 23:03:25"=>1,
"792014 @ 2023-05-29 23:13:47"=>10,
"792015 @ 2023-05-29 23:15:37"=>10,
"792017 @ 2023-05-29 23:37:03"=>4,
"792018 @ 2023-05-29 23:47:59"=>6,
"792019 @ 2023-05-29 23:49:13"=>14,
"792020 @ 2023-05-29 23:51:31"=>5,
"792021 @ 2023-05-29 23:59:41"=>5,
"792022 @ 2023-05-30 00:06:07"=>22,
"792023 @ 2023-05-30 00:09:00"=>5,
"792024 @ 2023-05-30 00:14:05"=>1,
"792028 @ 2023-05-30 01:13:42"=>5,
"792029 @ 2023-05-30 01:33:08"=>4,
"792030 @ 2023-05-30 01:41:52"=>3,
"792031 @ 2023-05-30 01:44:56"=>15,
"792033 @ 2023-05-30 01:50:40"=>3,
"792034 @ 2023-05-30 01:53:05"=>2,
"792035 @ 2023-05-30 02:08:43"=>13,
"792036 @ 2023-05-30 02:31:09"=>7,
"792037 @ 2023-05-30 02:33:57"=>3,
"792038 @ 2023-05-30 03:03:05"=>2,
"792040 @ 2023-05-30 03:14:15"=>50,
"792042 @ 2023-05-30 03:28:43"=>1,
"792043 @ 2023-05-30 03:32:32"=>2,
"792044 @ 2023-05-30 03:33:39"=>1,
"792045 @ 2023-05-30 04:09:54"=>1,
"792046 @ 2023-05-30 04:19:58"=>3,
"792047 @ 2023-05-30 04:24:48"=>4,
"792048 @ 2023-05-30 04:49:37"=>18,
"792050 @ 2023-05-30 04:55:19"=>1,
"792053 @ 2023-05-30 05:08:02"=>9,
"792054 @ 2023-05-30 05:10:15"=>1,
"792055 @ 2023-05-30 05:12:53"=>1,
"792056 @ 2023-05-30 05:22:31"=>1,
"792057 @ 2023-05-30 05:35:45"=>3,
"792058 @ 2023-05-30 05:46:13"=>1,
"792060 @ 2023-05-30 05:56:20"=>3,
"792061 @ 2023-05-30 06:03:33"=>24,
"792062 @ 2023-05-30 06:09:24"=>17,
"792063 @ 2023-05-30 06:16:14"=>3,
"792068 @ 2023-05-30 06:36:49"=>7,
"792070 @ 2023-05-30 06:46:38"=>7,
"792071 @ 2023-05-30 07:05:32"=>1,
"792082 @ 2023-05-30 09:04:26"=>1,
"792083 @ 2023-05-30 09:20:18"=>5,
"792084 @ 2023-05-30 09:25:47"=>9,
"792092 @ 2023-05-30 10:59:55"=>1,
"792098 @ 2023-05-30 11:34:49"=>2,
"792100 @ 2023-05-30 12:08:43"=>10,
"792109 @ 2023-05-30 13:35:52"=>11,
"792152 @ 2023-05-30 21:43:51"=>1,
"792155 @ 2023-05-30 21:54:01"=>1,
"792165 @ 2023-05-30 22:49:29"=>1,
"792169 @ 2023-05-30 23:22:51"=>5,
"792173 @ 2023-05-30 23:34:51"=>6,
"792174 @ 2023-05-30 23:46:09"=>1,
"792176 @ 2023-05-31 00:23:18"=>1,
"792177 @ 2023-05-31 00:24:57"=>3,
"792178 @ 2023-05-31 00:38:04"=>2,
"792179 @ 2023-05-31 00:45:07"=>5,
"792181 @ 2023-05-31 01:14:07"=>4,
"792183 @ 2023-05-31 01:48:20"=>2,
"792184 @ 2023-05-31 01:51:47"=>1,
"792185 @ 2023-05-31 02:10:47"=>2,
"792186 @ 2023-05-31 02:29:40"=>1,
"792187 @ 2023-05-31 02:41:41"=>9,
"792198 @ 2023-05-31 04:36:55"=>1,
"792199 @ 2023-05-31 04:37:14"=>1,
"792202 @ 2023-05-31 06:04:30"=>6,
"792211 @ 2023-05-31 07:08:35"=>5,
"792218 @ 2023-05-31 08:13:40"=>1,
"792222 @ 2023-05-31 08:27:58"=>17,
"792223 @ 2023-05-31 08:30:11"=>12,
"792224 @ 2023-05-31 08:45:34"=>35,
"792225 @ 2023-05-31 08:46:10"=>2,
"792226 @ 2023-05-31 08:47:34"=>1,
"792227 @ 2023-05-31 09:03:15"=>41,
"792228 @ 2023-05-31 09:07:04"=>24,
"792229 @ 2023-05-31 09:18:12"=>5,
"792233 @ 2023-05-31 10:06:40"=>2}
and so on.
Join us in the Ordgen / ORC-721 discord (chat server). Yes you can. Your questions and commetary welcome.
Or post them over at the Help & Support page. Thanks.