Khipus With Kaytes

Old joke of wisdom:

A mathematician, a physicist, and an engineer are on a train going through Scotland.
The engineer sees a black sheep, and says,
    “Aha! The sheep in Scotland are black!”
The physicist shakes his head and says,
    “Ha! You’re wrong! One sheep in Scotland is black!”
The mathematician shakes his head sadly and says,
    “You’re both wrong. One sheep in Scotland is black on one side…”

A kayte is a “needlework bundle” – a fuzzy golf ball of yarn if you will, that is attached to the beginning of a primary cord. In the article ‘A Beautiful Cradle’: Subject Indicators and the Decipherment of Genre on Andean Khipus By Sabine Hyland (2020), Dr. Hyland argues that kaytes are indicators of khipu typologies. For example a kayte with a red zigzag pattern may indicate that the khipu is of “imperial” nature.

Let’s find khipus with kaytes and examine their fieldmarks:

Code 
# First, locate all the khipus that have kaytes
(khipu_dict, all_khipus) = kamayuq.fetch_khipus()
kayte_khipus = {aKhipu.name() for aKhipu in all_khipus if aKhipu.has_kayte()}
print(f"kayte_khipus({len(kayte_khipus)}) = {kayte_khipus}")
khipu_summary_df = kq.fetch_khipu_summary()
kayte_khipu_summary_df = khipu_summary_df[khipu_summary_df.kfg_name.isin(kayte_khipus)]
kayte_khipu_summary_df = kayte_khipu_summary_df.drop(['legal_filename', 'original_name', 'nickname', 
                                                      'museum_name', 'museum_num', 'museum_description', 
                                                      'notes', 'pcord_notes'], axis = 1)

kayte_khipu_summary_df
kayte_khipus(16) = {'UR091', 'UR087', 'UR200', 'KH0081', 'UR188', 'UR175', 'UR201', 'HP030', 'UR110', 'UR068', 'UR235', 'UR266', 'UR231', 'UR268', 'UR291A', 'UR284'}
Unnamed: 0 khipu_id kfg_name okr_name region provenance num_cord_clusters mean_cords_per_cluster max_subsidiary_branches num_top_cord_double_sum_clusters ... fanout_ratio num_total_cords num_ascher_colors knot_cluster_count knot_count mean_knots_per_cord num_s_knots num_z_knots percent_s_knots percent_z_knots
1 496 1000541 UR231 KH0468 South Coast, Peru Ica/Pisco 80 11.925000 2 0 ... 1.104167 954 26 478 1057 1.107966 0 1056 0.000000 0.999054
13 286 1000273 UR087 KH0323 Unknown Santa 56 6.607143 2 3 ... 1.209150 370 21 309 377 1.018919 40 336 0.106101 0.891247
34 581 6000117 KH0081 KH0081 Unknown Monte de Cacatilla, Valle de Nazca 36 6.000000 2 0 ... 1.004651 216 11 424 1003 4.643519 1003 0 1.000000 0.000000
57 555 1000658 UR291A KH0529 Unknown Armatambo, Huaca San Pedro 15 11.066667 1 0 ... 1.000000 166 24 123 368 2.216867 5 355 0.013587 0.964674
84 529 1000607 UR266 KH0502 Unknown Incahuasi 36 3.666667 1 0 ... 1.000000 132 15 266 725 5.492424 81 644 0.111724 0.888276
85 532 1000610 UR268 KH0505 Unknown Incahuasi 33 3.969697 1 0 ... 1.000000 131 14 376 1224 9.343511 81 1143 0.066176 0.933824
86 500 1000545 UR235 KH0472 Unknown Unknown 18 10.777778 3 0 ... 1.492308 194 20 143 262 1.350515 0 262 0.000000 1.000000
102 468 1000512 UR201 KH0439 Central Coast, Peru Pachacamac 18 7.611111 3 0 ... 1.245455 137 13 228 771 5.627737 0 771 0.000000 1.000000
106 163 1000449 HP030 KH0562 Unknown Pachacamac 12 9.166667 2 0 ... 1.028037 110 10 279 848 7.709091 0 848 0.000000 1.000000
131 290 1000271 UR091 KH0327 Unknown Santa 3 41.666667 2 0 ... 1.388889 125 21 67 140 1.120000 0 140 0.000000 1.000000
134 639 6000176 UR110 KH0346 Unknown Unknown 89 1.000000 1 0 ... 1.000000 89 7 139 140 1.573034 96 44 0.685714 0.314286
145 467 1000511 UR200 KH0438 Central Coast, Peru Pachacamac 14 6.142857 1 0 ... 1.000000 86 8 192 506 5.883721 1 505 0.001976 0.998024
174 268 1000262 UR068 KH0305 Puruchuco Unknown 17 4.294118 1 0 ... 1.000000 73 6 71 114 1.561644 18 96 0.157895 0.842105
197 457 1000499 UR188 KH0426 Unknown Unknown 18 4.333333 2 0 ... 1.218750 78 10 237 956 12.256410 0 955 0.000000 0.998954
408 548 1000651 UR284 KH0522 Unknown Armatambo, Huaca San Pedro 6 3.666667 1 0 ... 1.000000 22 5 40 154 7.000000 1 99 0.006494 0.642857
599 446 1000483 UR175 KH0414 Unknown Unknown 2 6.000000 2 0 ... 2.000000 12 6 7 15 1.250000 1 14 0.066667 0.933333

16 rows × 40 columns

16 Khipus (2.5% of the KFG) have kaytes. Out of this small sample, we know that 1 has a red Kayte - UR068. UR110 is a Canuto khipu, and only partially reconstructed, so it should be reviewed with a cautious eye.

Wandering throught the table, and sorting by mean_cords per cord cluster we see that many of them have 4 to 5 cords per cluster (a category which UR068 fits) and a few of them have around 11 or 12 cords per cluster.

An image quilt of this subset of these khipus reveals some similarities on visual examination. Similarly, a fieldmark browser for these khipus reveals what is special (or maybe not, in this case) about these khipus. Let’s build both:

Code 
%%capture cell_output.txt

from khipu_html import make_image_quilt_file
ku.ensure_directory_exists(output_dir := f"{kq.project_directory()}/khipufieldmarks/appendix/notebooks/image_quilts/kayte_quilt")
make_image_quilt_file(kayte_khipus, grouped=False, title_string=f"Kayte Khipus Quilt",
                      sort_string = "", template_file = "fieldmark_image_quilt_template",
                      output_dir = output_dir, output_filename = f"kayte_quilt")
Code 
%%capture cell_output.txt

from fieldmark_table import make_mini_fieldmark_browser
ku.ensure_directory_exists(output_directory := f"{kq.project_directory()}/khipufieldmarks/appendix/notebooks/kayte_browser")
make_mini_fieldmark_browser(kayte_khipus, output_directory, browser_title="Khipus W/Kaytes - Fieldmarks")

Worth looking at!

Image Quilt

Fieldmark Browser

So. out of the only 16 samples we have, what is clearly apparent?