When you have mastered numbers,
you will in fact
no longer be reading numbers,
any more than you read words
when reading books.
You will be reading meanings.
W. E. B. DU BOIS
The number of top cords in the khipu including top cord subsidiaries.
| Measure | Result |
|---|---|
| Number of Khipus That Match | 31 (05%) |
| Number of Significant Khipus | 31 (05%) |
| Five most Significant Khipu | UR006, UR001, AS072, UR146, UR012 |
| Image Quilt | Click here |
# Initialize plotly
import plotly
plotly.offline.init_notebook_mode(connected = False);
# Read in the Fieldmark and its associated dataframe and match dictionary
from fieldmark_khipu_summary import FieldmarkTopCords
aFieldmark = FieldmarkTopCords()
raw_match_dict = aFieldmark.raw_match_dict()
print(f"# of Matching Khipus = {len(aFieldmark.matching_khipus())}")# of Matching Khipus = 31from plotly import express as px
import pandas as pd
# Plot Significant khipu
significant_khipus = aFieldmark.matching_khipus()
significant_values = [raw_match_dict[aKhipuName] for aKhipuName in significant_khipus]
significant_df = pd.DataFrame(list(zip(significant_khipus, significant_values)), columns =['KhipuName', 'Value'])
fig = px.bar(significant_df, x='KhipuName', y='Value', labels={"KhipuName": "Khipu Name", "Value": "Number of Top Cords", },
title=f"Significant Khipu ({len(significant_khipus)}) for Number of Top Cords", width=944, height=450).update_layout(showlegend=True).show()A study of khipus with broken pendant or primary cords indicates that a majority of the khipus with top cords are damaged in some fashion.
Three observations become apparent:
Let’s make a dataframe of topcord khipus with topcord count, banded group count, and seriated group count.
%%capture
import qollqa_chuspa as qc
import utils_khipu as ukhipu
import utils_loom as uloom
(khipu_dict, all_khipus) = qc.fetch_khipus()
# Make a dataframe of topcord khipus with topcord count, banded group count, and seriated group count.
topcord_khipus = [khipu_dict[khipu_name] for khipu_name in significant_khipus]
def topcord_record(aKhipu):
is_banded = aKhipu.num_banded_groups() > aKhipu.num_seriated_groups()
return (aKhipu.kfg_name(), aKhipu.num_top_cords(), aKhipu.num_banded_groups(), aKhipu.num_seriated_groups(), is_banded)
topcord_data = [topcord_record(aKhipu) for aKhipu in topcord_khipus]
topcord_df = pd.DataFrame(topcord_data,
columns=["kfg_name", "num_top_cords", "num_banded_groups", "num_seriated_groups", "is_banded"])
from pandas2html import Pandas2HTML
def make_dataframe_viewer():
class TopCordsViewer(Pandas2HTML):
def __init__(self, file_specs, page_specs, column_specs, from_df=None):
super().__init__(file_specs, page_specs, column_specs, from_df=from_df)
def columns_to_display(self):
return ['kfg_name', 'num_top_cords', 'num_banded_groups', 'num_seriated_groups', 'is_banded']
file_specs = {
"template_file":f"{uloom.project_directory()}/templates/dataframe_viewer.html",
"output_dir":f"{ukhipu.fieldmark_www_directory()}/analyses/database",
"basename":"num_banded_vs_seriated_top_cords",
}
page_specs = {
"title":"# of Top Cords by Banded vs Seriated Groups",
"description":"# of Top Cordsby Banded vs Seriated Groups",
"logo_link":ukhipu.kfg_domain_url(),
}
column_specs = {
'kfg_name': {
'label':"KFG Name",
'formatter':"kfg_name",
'header_class_name':"th_kfg_name",
'header_class_style':"align:right;width:80px;",
'row_class_name':"td_kfg_name",
'row_class_style':"align:right;width:80px;",
},
'num_top_cords':{
'label':"# Top Cords",
'formatter':"number",
'header_class_name':"th_num_top_cords",
'header_class_style':"align:center;width:109px;min-width:109px;max-width:109px;",
'row_class_name':"td_num_top_cords",
'row_class_style':"align:center;width:109px;min-width:109px;max-width:109px;",
},
'num_banded_groups': {
'label':"# Banded Groups",
'formatter':"number",
'header_class_name':"th_num_banded_groups",
'header_class_style':"align:center;width:109px;min-width:109px;max-width:109px;",
'row_class_name':"td_num_top_cords",
'row_class_style':"align:center;width:109px;min-width:109px;max-width:109px;",
},
'num_seriated_groups':{
'label':"# Seriated Groups",
'formatter':"number",
'header_class_name':"th_num_seriated_groups",
'header_class_style':"align:center;width:109px;min-width:109px;max-width:109px;",
'row_class_name':"td_num_seriated_groups",
'row_class_style':"align:center;width:109px;min-width:109px;max-width:109px;",
},
'is_banded':{
'label':"Is Banded?",
'formatter':"boolean",
'header_class_name':"th_is_banded",
'header_class_style':"align:center;width:109px;min-width:109px;max-width:109px;",
'row_class_name':"td_is_banded",
'row_class_style':"align:center;width:109px;min-width:109px;max-width:109px;",
}
}
TopCordsViewer(file_specs, page_specs, column_specs, from_df=topcord_df).make_pandas_html()
make_dataframe_viewer();The dataframe can be viewed here
def banded_color(x): return 0.0 if x else 1.0
topcord_df['banded_color'] = [banded_color(x) for x in topcord_df.is_banded.values]
legend_text = "<i style=\"font-size:10pt;\"> Size:Num Top Cords, Blue:Banded, Red:Seriated</i>"
fig = (px.scatter(topcord_df, x="num_seriated_groups", y="num_banded_groups",
size="num_top_cords",
color='banded_color', color_continuous_scale=['#3c3fff', '#ff3030',],
labels={"num_banded_groups": f"# Banded Groups", "num_seriated_groups": "# Seriated Groups", },
hover_data=['kfg_name'], title=f"<b>Top Cords - Banded vs Seriated Groups</b>{legend_text}",
width=944, height=944)
.update_layout(showlegend=False).update(layout_coloraxis_showscale=False).show()
)On its own, the above graph is not conclusive , but it does appear that khipus with top cords tend towards having predominantly banded groups. However, when we examine the database generated above, and view at the drawings of the actual seriated top cord khipus, we see that these khipus have
Viewing these individual exceptions shows that Manny’s thesis likely hold true!