Deepest Branch Level

1. Search Criteria:

Deepest Branch Level - How many subsidiaries (branch level 1) are there?
How deep does a khipu go, in terms of subsidiaries of subsidiaries of ….

2. Significance Criteria:

Although we are searching for all khipus with subsidiaries, we are also interested in how deep their subsidiaries are.

3. Summary Results:

Measure	Result
Number of Khipus with Subsidiaries	382 (59%):
Number of Khipus with 1+ Subsidiary Levels	255 (39%):
Number of Khipus with 2+ Subsidiary Levels	94 (14%)
Five most Significant Khipu	HP038, UR262, KH0083, UR003, UR270
Image Quilt	Click here

4. Summary Charts:

Code

# Initialize plotly
import plotly
import plotly.express as px
import pandas as pd

plotly.offline.init_notebook_mode(connected = False);

# Read in the Fieldmark and its associated dataframe and match dictionary
from fieldmark_khipu_summary import Fieldmark_Max_Subsidiary_Level
aFieldmark = Fieldmark_Max_Subsidiary_Level()
fieldmark_dataframe = aFieldmark.dataframes[0].dataframe
raw_match_dict = aFieldmark.raw_match_dict()

Code

# Plot Matching khipu
matching_khipus = aFieldmark.matching_khipus() 
matching_values = [raw_match_dict[aKhipuName] for aKhipuName in matching_khipus]
matching_df =  pd.DataFrame(list(zip(matching_khipus, matching_values)), columns =['KhipuName', 'Value'])
fig = px.bar(matching_df, x='KhipuName', y='Value', labels={"KhipuName": "Khipu Name", "Value": "Deepest Branch Level", }, 
            title=f"Matching Khipu ({len(matching_khipus)}) for Deepest Branch Level",  width=944, height=450).update_layout(showlegend=True).show()

5. Exploratory Data Analysis

Code

import utils_khipu as ukhipu
level1_khipus = len([aKhipuName for aKhipuName in aFieldmark.matching_khipus() if raw_match_dict[aKhipuName] == 1])
level2_khipus = len([aKhipuName for aKhipuName in aFieldmark.matching_khipus() if raw_match_dict[aKhipuName] == 2])
level3_khipus = len([aKhipuName for aKhipuName in aFieldmark.matching_khipus() if raw_match_dict[aKhipuName] == 3])
level4_khipus = len([aKhipuName for aKhipuName in aFieldmark.matching_khipus() if raw_match_dict[aKhipuName] == 4])
level5_khipus = len([aKhipuName for aKhipuName in aFieldmark.matching_khipus() if raw_match_dict[aKhipuName] == 5])
print(f"{ukhipu.pct_kfg_khipus(level1_khipus)}: 1st Level Subsidiary Khipus")
print(f"{ukhipu.pct_kfg_khipus(level2_khipus)} : 2nd Level Subsidiary Khipus")
print(f"{ukhipu.pct_kfg_khipus(level3_khipus)}\t : 3rd Level Subsidiary Khipus")
print(f"{ukhipu.pct_kfg_khipus(level4_khipus)} \t : 4th Level Subsidiary Khipus")
print(f"{ukhipu.pct_kfg_khipus(level5_khipus)} \t : 5th Level Subsidiary Khipus")

262 (40%): 1st Level Subsidiary Khipus
88 (14%) : 2nd Level Subsidiary Khipus
27 (4%)  : 3rd Level Subsidiary Khipus
4 (1%)   : 4th Level Subsidiary Khipus
1 (0%)   : 5th Level Subsidiary Khipus

6. Conclusion

Our old friend from indexed (by color) subsidiary sums, shows up here UR035, having a branch level of 5! I have not tested to see if there are similar depths for sum indexing based on color, but it would not be surprising.