Broken Khipus

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1.0 Broken Primary Cords

Let’s identify all khipus with primary cords that are known to be broken (i.e they have a beginning or termination that is broken or cut). This may be useful in identifying “orphaned khipu”, khipu which have been split and are now identified as two separate khipu.

Code

# First, locate all the khipus that have broken primary cords
(khipu_dict, all_khipus) = kamayuq.fetch_khipus()
broken_primary_khipus = {aKhipu.name() for aKhipu in all_khipus if aKhipu.primary_cord.is_broken()}
print(f"# Khipus w/Broken Primaries= {len(broken_primary_khipus)}")

# Khipus w/Broken Primaries= 156

2.0 Broken Pendant/Subsidiary Cords

We can count the number of broken cords by checking the cord termination. Note that this count does not include khipus with loose strings, that have become detached from their khipu.

Code

(khipu_dict, all_khipus) = kamayuq.fetch_khipus()
def num_broken_cords(aKhipu):
    return len([aCord for aCord in aKhipu.cc_cords() if aCord.is_broken()])
broken_cord_khipus = [(aKhipu.name(), num_broken_cords(aKhipu)) for aKhipu in all_khipus if num_broken_cords(aKhipu) > 0]   
broken_cord_khipus = dict(sorted(broken_cord_khipus, key=lambda x: x[1], reverse=True))
print(f"# Khipus w/Broken Cords = {len(broken_cord_khipus)}")

# Khipus w/Broken Cords = 523

3.0 Broken Top Cord Khipus

As an example, of how incomplete most khipus are let’s look at top cord khipus and see how many of them are incomplete.

Code

from fieldmark_khipu_summary import Fieldmark_TopCords
aFieldmark = Fieldmark_TopCords()
fieldmark_dataframe = aFieldmark.dataframes[0].dataframe
top_cords_df = fieldmark_dataframe[fieldmark_dataframe.num_top_cords > 0]
top_cord_khipus = top_cords_df.kfg_name.tolist()
top_cords_df['broken_cords'] = [broken_cord_khipus.get(aKhipu,0) for aKhipu in top_cord_khipus]
top_cords_df['broken_primary'] = [1 if khipu_dict[aKhipu].primary_cord.is_broken() else 0 for aKhipu in top_cord_khipus]
broken_top_cords_mask = [(item[0]>0 or item[1]>0) for item in zip(top_cords_df['broken_cords'].to_list(), top_cords_df['broken_primary'].to_list())]
broken_top_cords_df = top_cords_df[['kfg_name', 'num_cords', 'num_top_cords', 'broken_cords', 'broken_primary']]
broken_top_cords_df.sort_values(['num_top_cords'], ascending=[False]);

print(f"# Khipus w/Top Cords = {len(top_cords_df)}")
num_broken_primary_top_cord_khipus = sum([(num_broken > 0) for num_broken in broken_top_cords_df['broken_primary'].to_list()])
print(f"# Top Cord Khipus w/Broken Primaries = {num_broken_primary_top_cord_khipus}")
num_broken_cord_top_cord_khipus = sum([(num_broken > 0) for num_broken in broken_top_cords_df['broken_cords'].to_list()])
print(f"# Top Cord Khipus w/Broken Cords = {num_broken_cord_top_cord_khipus}")

See Full Dataframe in Mito

	kfg_name	num_cords	num_top_cords	broken_cords	broken_primary
272	AS006	92	28	8	0
51	UR119	304	24	44	1
53	AS066	172	24	18	0
55	UR118	321	22	51	0
79	UR149	256	17	115	1
...	...	...	...	...	...
6	UR001	572	1	11	0
441	AS004	22	1	0	0
145	UR255	89	1	17	0
131	AS013	90	1	31	1
257	UR048	61	1	13	0

# Khipus w/Top Cords = 49
# Top Cord Khipus w/Broken Primaries = 15
# Top Cord Khipus w/Broken Cords = 44

Code

plotly.offline.init_notebook_mode(connected = False);
fig = px.scatter(broken_top_cords_df, 
                 x='num_top_cords', y='broken_cords', 
                 hover_name='kfg_name', hover_data=['num_cords', 'num_top_cords', 'broken_cords', 'broken_primary'],
                 size="num_cords", 
                 color='broken_primary', color_continuous_scale=['#3c3fff', '#ff3030',],
                 title=f"<b>Broken Top Cords</b> - Red=BrokenPrimary, Size=#Cords",
                 width=944, height=944).update_coloraxes(showscale=False).show()