The number of cord groups is a rough estimate of overall khipu size. What else is there to say!
| Measure | Result |
|---|---|
| Number of Khipus That Match | 650(100%) |
| Number of Significant Khipus | 67 (10%) |
| Five most Significant Khipu | AS069, UR055, UR050, UR054, UR1032 |
| Image Quilt | Click here |
# Initialize plotly
plotly.offline.init_notebook_mode(connected = False);
# Read in the Fieldmark and its associated dataframe and match dictionary
from fieldmark_khipu_summary import Fieldmark_Num_Cord_Groups
aFieldmark = Fieldmark_Num_Cord_Groups()
fieldmark_dataframe = aFieldmark.dataframes[0].dataframe
raw_match_dict = aFieldmark.raw_match_dict()# 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": "Number of Cord Groups", },
title=f"Matching Khipu ({len(matching_khipus)}) for Number of Cord Groups", width=944, height=500).update_layout(showlegend=True).show()# Plot Significant khipu
significant_khipus = aFieldmark.significant_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 Cord Groups", },
title=f"Significant Khipu ({len(significant_khipus)}) for Number of Cord Groups", width=944, height=500).update_layout(showlegend=True).show()How do the number of cords per group distribute?
pendant_cords_per_group = []
for aKhipu in all_khipus:
for aGroup in aKhipu[:]:
pendant_cords_per_group.append(aGroup.num_pendant_cords())
num_total_groups = len(pendant_cords_per_group)
from collections import Counter as counter
counter_pendant_cords_per_group = counter(pendant_cords_per_group)
print(f"Most common group counts are: {counter_pendant_cords_per_group.most_common()}\n")
# of Six pendant cords per group
num_six_pendant_cords_per_group = counter_pendant_cords_per_group.most_common()[5][1]
percent_string = f"{ku.as_percent_string(num_six_pendant_cords_per_group, num_total_groups)}"
percent_count = f"{num_six_pendant_cords_per_group}/{num_total_groups} ({percent_string})"
print(f"# six_pendant_cords_per_group = {percent_count}")Most common cluster counts are: [(1, 2599), (3, 789), (4, 728), (5, 671), (6, 670), (2, 669), (7, 440), (8, 306), (9, 256), (10, 215), (11, 175), (12, 137), (14, 74), (18, 65), (15, 51), (13, 50), (16, 45), (22, 30), (21, 29), (20, 24), (17, 23), (19, 20), (0, 11), (24, 11), (36, 9), (26, 7), (27, 7), (23, 6), (30, 6), (40, 5), (31, 5), (32, 4), (34, 4), (46, 4), (42, 3), (28, 3), (25, 3), (35, 3), (38, 3), (72, 2), (60, 2), (61, 2), (66, 2), (39, 2), (160, 2), (29, 2), (53, 2), (101, 1), (75, 1), (45, 1), (58, 1), (54, 1), (48, 1), (52, 1), (37, 1), (88, 1), (41, 1), (33, 1), (44, 1), (135, 1), (77, 1), (49, 1), (67, 1)]
# six_pendant_cords_per_cluster = 669/8192 (8%)