# Thread: Ghost Generated HarappaWorld Results

1. ## Ghost Generated HarappaWorld Results

Hi Guys,

As requested by @khanabadoshi I have made a new thread here. I have made a simple Python function that generates HarappaWorld scores based on existing data for a given population. Right now the function takes in a nested array, e.g. [[HighestSI, LowestSI], [HighestBaloch, LowestBaloch]....]. I do this for each component in HarappaWorld. Inside the function I then create a for loop that starts from 0 till 5000 for e.g and randomly generate scores like SI, Baloch for each Ghost. Then I add these scores up to make sure that they are greater than 99 and less than 100.

Code:
```def genDataset(data):
print(data)
count = 0
totalAvg = 0
for i in range(5000):
sIndian = round(random.uniform(data[0][0], data[0][1]), 2)
baloch = round(random.uniform(data[1][0], data[1][1]), 2)
caucasian = round(random.uniform(data[2][0], data[2][1]), 2)
ne_euro = round(random.uniform(data[3][0], data[3][1]), 2)
se_asian = round(random.uniform(data[4][0], data[4][1]), 2)
siberian = round(random.uniform(data[5][0], data[5][1]), 2)
ne_asian = round(random.uniform(data[6][0], data[6][1]), 2)
papauan = round(random.uniform(data[7][0], data[7][1]), 2)
american = round(random.uniform(data[8][0], data[8][1]), 2)
beringian = round(random.uniform(data[9][0], data[9][1]), 2)
medi = round(random.uniform(data[10][0], data[10][1]), 2)
sw_asian = round(random.uniform(data[11][0], data[11][1]), 2)
san = round(random.uniform(data[12][0], data[12][1]), 2)
e_african = round(random.uniform(data[13][0], data[13][1]), 2)
pygmy = round(random.uniform(data[14][0], data[14][1]), 2)
w_african = round(random.uniform(data[15][0], data[15][1]), 2)

total = sIndian + baloch + caucasian + ne_euro + se_asian + siberian + ne_asian + papauan + american + beringian + medi + sw_asian + san + e_african + pygmy + w_african

if (total >= 99.98 and total <= 100.0):
print(sIndian, baloch, caucasian , ne_euro , se_asian , siberian , ne_asian , papauan , american , beringian , medi , sw_asian , san , e_african , pygmy , w_african)
totalAvg += total
count += 1

print(round(totalAvg/count, 2))
print(count)

sample_data = [[29.83, 33.17],[37.66, 45.20],[9.34, 14.99],[5.77, 10.84],[0, 0.83],[0, 1.73],[0, 1.80],[0, 0.91],[0, 1.57],[0, 1.52],[0, 1.42],[0, 2.93],[0, 0.39],[0, 0.28],[0, 0.57],[0, 0.35]]```
Please bear in mind this was just a mockup and can be greatly improved.

Example Ghosts:

 Group S Indian Baloch Caucasian NE Euro SE Asian Siberian NE Asian Papuan American Beringian Mediterranean SW Asian San E African Pygmy W African GUJJAR_GEN 30.82 45.16 9.52 7.02 0.06 0.16 1.53 0.19 1.34 0.75 1.3 1.17 0.36 0.01 0.41 0.19 GUJJAR_GEN 30.12 42.72 9.86 9.0 0.55 1.71 1.6 0.69 0.03 0.52 0.01 1.87 0.36 0.28 0.5 0.18 GUJJAR_GEN 31.3 39.37 12.64 8.42 0.78 0.94 0.54 0.02 1.13 0.97 0.67 2.32 0.28 0.01 0.41 0.18 GUJJAR_GEN 30.0 38.37 14.28 9.8 0.03 1.06 0.18 0.8 1.25 1.13 0.74 1.61 0.22 0.17 0.12 0.23 GUJJAR_GEN 32.97 39.94 11.69 6.76 0.73 0.32 1.77 0.06 1.56 0.27 0.27 2.67 0.05 0.15 0.46 0.33 GUJJAR_GEN 31.22 43.52 9.73 9.31 0.64 0.92 0.32 0.42 0.47 1.11 0.26 0.94 0.33 0.11 0.36 0.32 GUJJAR_GEN 31.62 38.58 13.14 10.62 0.07 0.62 0.27 0.5 0.93 0.19 0.86 2.07 0.03 0.18 0.26 0.05 GUJJAR_GEN 32.97 42.16 13.25 5.97 0.54 1.45 1.51 0.35 0.14 0.3 0.49 0.24 0.02 0.06 0.48 0.06 GUJJAR_GEN 31.18 41.49 13.8 6.89 0.02 1.45 0.44 0.8 0.54 1.0 1.24 0.37 0.02 0.23 0.45 0.07 GUJJAR_GEN 31.56 42.31 11.28 6.85 0.66 1.0 0.23 0.7 0.81 0.91 0.81 2.22 0.12 0.05 0.42 0.06 GUJJAR_GEN 32.38 40.7 10.92 10.18 0.63 0.33 0.56 0.51 0.91 1.1 0.44 0.23 0.35 0.11 0.3 0.34

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3. Generated Chhetris as requested:

 Group S Indian Baloch Caucasian NE Euro SE Asian Siberian NE Asian Papuan American Beringian Mediterranean SW Asian San E African Pygmy W African CHEHTRI_GEN 33.52 25.18 5.5 7.89 2.09 2.18 18.78 0.61 0.79 0.68 0.2 1.8 0.49 0.0 0.16 0.11 CHEHTRI_GEN 34.02 25.24 5.07 8.1 1.3 2.88 18.93 0.51 1.8 0.83 0.05 0.81 0.42 0.0 0.03 0.01 CHEHTRI_GEN 31.3 24.93 5.85 7.17 1.12 4.76 20.05 0.56 1.6 1.05 1.29 0.08 0.04 0.0 0.13 0.06 CHEHTRI_GEN 31.7 28.28 4.01 7.86 0.37 3.57 21.34 0.66 1.19 0.16 0.08 0.31 0.4 0.0 0.06 0.0 CHEHTRI_GEN 34.15 24.33 5.62 10.32 1.14 1.99 18.81 0.13 0.12 1.76 0.43 0.94 0.14 0.0 0.02 0.1 CHEHTRI_GEN 34.31 24.75 5.7 9.52 0.07 2.79 19.24 0.2 0.15 0.16 1.41 1.16 0.37 0.0 0.12 0.04 CHEHTRI_GEN 34.96 25.43 5.59 7.98 0.14 3.97 18.53 0.63 0.05 0.49 1.48 0.24 0.38 0.0 0.04 0.09 CHEHTRI_GEN 31.28 25.83 4.67 7.16 0.27 4.22 21.21 0.59 0.25 1.03 1.6 1.47 0.27 0.0 0.11 0.04 CHEHTRI_GEN 34.91 23.97 5.02 8.2 0.09 4.68 20.82 0.24 0.96 0.25 0.21 0.18 0.2 0.0 0.17 0.09 CHEHTRI_GEN 33.08 24.19 3.59 9.02 1.47 3.76 19.86 0.44 1.56 0.98 0.89 0.87 0.16 0.0 0.1 0.02 CHEHTRI_GEN 31.47 28.72 5.46 6.72 1.56 2.89 20.25 0.33 0.11 0.05 0.36 1.45 0.42 0.0 0.15 0.06 CHEHTRI_GEN 32.46 27.56 4.59 8.49 0.52 2.03 20.97 0.19 0.43 0.65 1.67 0.26 0.03 0.0 0.04 0.1

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6. Originally Posted by geneticsikiru
Generated Chhetris as requested:

 Group S Indian Baloch Caucasian NE Euro SE Asian Siberian NE Asian Papuan American Beringian Mediterranean SW Asian San E African Pygmy W African CHEHTRI_GEN 33.52 25.18 5.5 7.89 2.09 2.18 18.78 0.61 0.79 0.68 0.2 1.8 0.49 0.0 0.16 0.11 CHEHTRI_GEN 34.02 25.24 5.07 8.1 1.3 2.88 18.93 0.51 1.8 0.83 0.05 0.81 0.42 0.0 0.03 0.01 CHEHTRI_GEN 31.3 24.93 5.85 7.17 1.12 4.76 20.05 0.56 1.6 1.05 1.29 0.08 0.04 0.0 0.13 0.06 CHEHTRI_GEN 31.7 28.28 4.01 7.86 0.37 3.57 21.34 0.66 1.19 0.16 0.08 0.31 0.4 0.0 0.06 0.0 CHEHTRI_GEN 34.15 24.33 5.62 10.32 1.14 1.99 18.81 0.13 0.12 1.76 0.43 0.94 0.14 0.0 0.02 0.1 CHEHTRI_GEN 34.31 24.75 5.7 9.52 0.07 2.79 19.24 0.2 0.15 0.16 1.41 1.16 0.37 0.0 0.12 0.04 CHEHTRI_GEN 34.96 25.43 5.59 7.98 0.14 3.97 18.53 0.63 0.05 0.49 1.48 0.24 0.38 0.0 0.04 0.09 CHEHTRI_GEN 31.28 25.83 4.67 7.16 0.27 4.22 21.21 0.59 0.25 1.03 1.6 1.47 0.27 0.0 0.11 0.04 CHEHTRI_GEN 34.91 23.97 5.02 8.2 0.09 4.68 20.82 0.24 0.96 0.25 0.21 0.18 0.2 0.0 0.17 0.09 CHEHTRI_GEN 33.08 24.19 3.59 9.02 1.47 3.76 19.86 0.44 1.56 0.98 0.89 0.87 0.16 0.0 0.1 0.02 CHEHTRI_GEN 31.47 28.72 5.46 6.72 1.56 2.89 20.25 0.33 0.11 0.05 0.36 1.45 0.42 0.0 0.15 0.06 CHEHTRI_GEN 32.46 27.56 4.59 8.49 0.52 2.03 20.97 0.19 0.43 0.65 1.67 0.26 0.03 0.0 0.04 0.1
Looks very natural. They're getting consistent SW Asian component which is rare and not realistic. But other scores are very good.

All Chhetri samples range between 30-33 SI except 1 and here most are skewed towards upper limit 35 SI, so will you reduce the upper limit to 34 SI.

Originally Posted by geneticsikiru
If I could edit, I would try for furthermore lower NE-Euro and higher Caucasian range.

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geneticsikiru (10-02-2021)

8. Originally Posted by Kaazi
Looks very natural. They're getting consistent SW Asian component which is rare and not realistic. But other scores are fine.

All Chhetri samples range between 30-33 SI except 1 and here most are skewed towards upper limit 35 SI, so will you reduce the upper limit to 34 SI.

If I could edit, I would try for furthermore lower NE-Euro and higher Caucasian range.

Just paste the code into here, then you should be able to edit and run: https://www.programiz.com/python-pro...line-compiler/

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Kaazi (10-02-2021)

10. My plan was to make it completely dynamic, so you can create ghosts for any calculator. As of now it is tied in with HarappaWorld. Also regarding @khanabadoshi's mystery ancestor, I was thinking to also make a function that will create mixed ghosts for e.g. 1/2 Gujjar, 1/2 Bahun or 1/3 Gujjar, 1/3 Bahun, 1/3 Kalash and see what the results look like.

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12. Hi Guys,

I have made some improvements on the previous function. Now it is completely dynamic and can be used for any admixture calculator and I also tried to make it more user friendly. Next I will look into making mixed ghosts. If you guys want I can maybe host it on the web and make a simple UI for it as well, so you can play around with it.

Code:
```import random

def genDataset(popName, components, data):
if(len(components) != len(data)):
print('Components and Sample Data Must Match!')
return False
count = 0
totalAvg = 0
columns = [str(el).upper() for el in components]
print('GROUP'+ ' ' +','.join(columns).replace(',', ' '))
for i in range(5000):
ghost = []
for i in range(0, len(components)):
placeholder = round(random.uniform(data[i][0], data[i][1]), 2)
ghost.append(placeholder)

total = sum(ghost)
if (total >= 99.98 and total <= 100.0):
toString = [str(comp) for comp in ghost]
print((popName + '_GEN_' + str(count)).upper() + ' ' + ' '.join(toString))
totalAvg += total
count += 1

print('\nTotal Average for Each ' + popName + ' ' + str(round(totalAvg/count, 2)))
print('\nGenerated ' + str(count) + ' Results')

# Define Components Here
# No Spaces in Component Names, use - !
components = ['South-Indian', 'Baloch', 'Caucasian', 'NE-Euro', 'SE-Asian', 'Siberian', 'NE-Asian', 'Papauan', 'American', 'Beringian', 'Med', 'SW-Asian', 'San', 'East-African', 'Pygmy', 'West-African']

# Add Low and High Values for Each Component
# Must Match Component Arr !
data = [[30.19, 35.37],[23.59, 29.14],[3.55, 6.87],[6.31, 10.53],[0, 3.67],[1.86, 4.88],[18.51, 21.53],[0, 1.22],[0, 1.93],[0.05, 1.78],[0, 2.04],[0, 2.18],[0, 0.53],[0, 0.0],[0, 0.18],[0, 0.13]]

genDataset('Chehtri', components, data)```

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14. Ok guys I have now manage to add mixed mode to the ghost generator . Now we can make any mix ghost we want, there should not be any limitations.

New code here:

Code:
```import random

def genDataset(popName, components, data, mode):
if(len(components) != len(data) and mode == 'single'):
print('Components and Sample Data Must Match!')
return False
elif mode == 'mixed' and len(components) == len(data):
print('Error')
return False
count = 0
totalAvg = 0
columns = [str(el).upper() for el in components]
print('GROUP'+ ' ' +','.join(columns).replace(',', ' '))
for i in range(5000):
ghost = []
for i in range(0, len(components)):
if mode == 'single':
placeholder = round(random.uniform(data[i][0], data[i][1]), 2)
ghost.append(placeholder)
else:
mixedArr = []
for h in range(0, len(data)):
placeholder = round(random.uniform(data[h][i][0], data[h][i][1]), 2)
mixedArr.append(placeholder)
if(len(mixedArr) == len(data)):
placeholder = round(sum(mixedArr) / len(mixedArr), 2)
ghost.append(placeholder)

total = sum(ghost)
if (total >= 99.98 and total <= 100.0):
toString = [str(comp) for comp in ghost]
print((popName + '_GEN_' + str(count)).upper() + ' ' + ' '.join(toString))
totalAvg += total
count += 1

print('\nTotal Average for Each ' + popName + ' ' + str(round(totalAvg/count, 2)))
print('\nGenerated ' + str(count) + ' Results')

# Define Components Here
# No Spaces in Component Names, use - !
components = ['South-Indian', 'Baloch', 'Caucasian', 'NE-Euro', 'SE-Asian', 'Siberian', 'NE-Asian', 'Papauan', 'American', 'Beringian', 'Med', 'SW-Asian', 'San', 'East-African', 'Pygmy', 'West-African']

# Add Low and High Values for Each Component
# Must Match Component Arr !
data = [[30.19, 35.37],[23.59, 29.14],[3.55, 6.87],[6.31, 10.53],[0, 3.67],[1.86, 4.88],[18.51, 21.53],[0, 1.22],[0, 1.93],[0.05, 1.78],[0, 2.04],[0, 2.18],[0, 0.53],[0, 0.0],[0, 0.18],[0, 0.13]]

sample_data = [[29.83, 33.17],[37.66, 45.20],[9.34, 14.99],[5.77, 10.84],[0, 0.83],[0, 1.73],[0, 1.80],[0, 0.91],[0, 1.57],[0, 1.52],[0, 1.42],[0, 2.93],[0, 0.39],[0, 0.28],[0, 0.57],[0, 0.35]]

mixedData = [data, sample_data]

genDataset('GujarChehtri', components, mixedData, 'mixed')```

Sample mixed Gujjar and Chehtris:

 GROUP SOUTH-INDIAN BALOCH CAUCASIAN NE-EURO SE-ASIAN SIBERIAN NE-ASIAN PAPAUAN AMERICAN BERINGIAN MED SW-ASIAN SAN EAST-AFRICAN PYGMY WEST-AFRICAN GUJARCHEHTRI_GEN_0 31.94 33.5 9.94 7.55 0.3 2.14 10.37 0.3 0.63 0.84 0.56 1.22 0.23 0.13 0.28 0.07 GUJARCHEHTRI_GEN_1 31.82 32.61 8.54 7.88 1.39 1.75 10.74 0.13 0.9 1.28 1.07 1.26 0.12 0.01 0.28 0.2 GUJARCHEHTRI_GEN_2 32.08 35.22 7.98 8.82 0.47 1.62 9.35 0.56 0.93 0.31 1.17 0.66 0.32 0.07 0.26 0.18 GUJARCHEHTRI_GEN_3 30.38 34.92 10.13 6.68 1.13 1.27 10.58 0.28 1.03 0.49 1.09 1.31 0.32 0.01 0.28 0.1 GUJARCHEHTRI_GEN_4 30.55 32.58 10.1 9.06 1.29 1.96 11.27 0.26 1.02 0.2 0.12 0.77 0.22 0.12 0.32 0.16 GUJARCHEHTRI_GEN_5 33.25 33.0 7.04 7.92 1.85 2.38 9.99 0.23 0.24 1.46 0.57 1.48 0.24 0.04 0.23 0.07 GUJARCHEHTRI_GEN_6 32.62 32.31 8.14 8.12 1.96 2.42 9.81 0.06 1.44 0.41 1.04 1.07 0.23 0.09 0.17 0.1 GUJARCHEHTRI_GEN_7 31.69 33.16 7.41 7.75 1.6 1.83 10.36 0.77 0.91 1.61 0.44 1.58 0.37 0.06 0.35 0.1 GUJARCHEHTRI_GEN_8 31.41 33.89 9.04 8.77 1.35 1.25 9.87 0.65 1.04 1.12 0.69 0.21 0.37 0.12 0.17 0.04 GUJARCHEHTRI_GEN_9 32.97 32.75 7.64 8.75 1.34 1.85 10.73 0.15 0.53 0.06 0.84 1.9 0.29 0.1 0.04 0.04 GUJARCHEHTRI_GEN_10 31.84 31.41 9.89 8.37 1.12 2.19 10.25 0.33 0.08 1.23 0.68 2.03 0.23 0.07 0.21 0.05 GUJARCHEHTRI_GEN_11 31.77 33.9 10.62 6.96 0.33 2.2 10.35 0.25 0.51 0.82 1.37 0.66 0.05 0.03 0.03 0.15 GUJARCHEHTRI_GEN_12 32.45 32.39 9.31 7.72 0.69 1.66 10.19 0.9 1.08 0.39 1.21 1.63 0.08 0.01 0.06 0.23 GUJARCHEHTRI_GEN_13 30.73 32.73 10.13 6.63 0.98 2.21 10.62 0.54 1.03 0.79 1.58 1.25 0.3 0.06 0.26 0.15 GUJARCHEHTRI_GEN_14 31.84 33.97 9.34 7.59 0.58 2.42 9.77 0.69 0.91 0.55 0.29 1.21 0.39 0.1 0.18 0.16 GUJARCHEHTRI_GEN_15 31.89 32.27 8.95 8.55 1.49 1.34 10.94 0.22 1.0 0.72 0.89 1.09 0.19 0.1 0.17 0.18

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16. Originally Posted by geneticsikiru
Ok guys I have now manage to add mixed mode to the ghost generator . Now we can make any mix ghost we want, there should not be any limitations.

New code here:

Code:
```import random

def genDataset(popName, components, data, mode):
if(len(components) != len(data) and mode == 'single'):
print('Components and Sample Data Must Match!')
return False
elif mode == 'mixed' and len(components) == len(data):
print('Error')
return False
count = 0
totalAvg = 0
columns = [str(el).upper() for el in components]
print('GROUP'+ ' ' +','.join(columns).replace(',', ' '))
for i in range(5000):
ghost = []
for i in range(0, len(components)):
if mode == 'single':
placeholder = round(random.uniform(data[i][0], data[i][1]), 2)
ghost.append(placeholder)
else:
mixedArr = []
for h in range(0, len(data)):
placeholder = round(random.uniform(data[h][i][0], data[h][i][1]), 2)
mixedArr.append(placeholder)
if(len(mixedArr) == len(data)):
placeholder = round(sum(mixedArr) / len(mixedArr), 2)
ghost.append(placeholder)

total = sum(ghost)
if (total >= 99.98 and total <= 100.0):
toString = [str(comp) for comp in ghost]
print((popName + '_GEN_' + str(count)).upper() + ' ' + ' '.join(toString))
totalAvg += total
count += 1

print('\nTotal Average for Each ' + popName + ' ' + str(round(totalAvg/count, 2)))
print('\nGenerated ' + str(count) + ' Results')

# Define Components Here
# No Spaces in Component Names, use - !
components = ['South-Indian', 'Baloch', 'Caucasian', 'NE-Euro', 'SE-Asian', 'Siberian', 'NE-Asian', 'Papauan', 'American', 'Beringian', 'Med', 'SW-Asian', 'San', 'East-African', 'Pygmy', 'West-African']

# Add Low and High Values for Each Component
# Must Match Component Arr !
data = [[30.19, 35.37],[23.59, 29.14],[3.55, 6.87],[6.31, 10.53],[0, 3.67],[1.86, 4.88],[18.51, 21.53],[0, 1.22],[0, 1.93],[0.05, 1.78],[0, 2.04],[0, 2.18],[0, 0.53],[0, 0.0],[0, 0.18],[0, 0.13]]

sample_data = [[29.83, 33.17],[37.66, 45.20],[9.34, 14.99],[5.77, 10.84],[0, 0.83],[0, 1.73],[0, 1.80],[0, 0.91],[0, 1.57],[0, 1.52],[0, 1.42],[0, 2.93],[0, 0.39],[0, 0.28],[0, 0.57],[0, 0.35]]

mixedData = [data, sample_data]

genDataset('GujarChehtri', components, mixedData, 'mixed')```

Sample mixed Gujjar and Chehtris:

 GROUP SOUTH-INDIAN BALOCH CAUCASIAN NE-EURO SE-ASIAN SIBERIAN NE-ASIAN PAPAUAN AMERICAN BERINGIAN MED SW-ASIAN SAN EAST-AFRICAN PYGMY WEST-AFRICAN GUJARCHEHTRI_GEN_0 31.94 33.5 9.94 7.55 0.3 2.14 10.37 0.3 0.63 0.84 0.56 1.22 0.23 0.13 0.28 0.07 GUJARCHEHTRI_GEN_1 31.82 32.61 8.54 7.88 1.39 1.75 10.74 0.13 0.9 1.28 1.07 1.26 0.12 0.01 0.28 0.2 GUJARCHEHTRI_GEN_2 32.08 35.22 7.98 8.82 0.47 1.62 9.35 0.56 0.93 0.31 1.17 0.66 0.32 0.07 0.26 0.18 GUJARCHEHTRI_GEN_3 30.38 34.92 10.13 6.68 1.13 1.27 10.58 0.28 1.03 0.49 1.09 1.31 0.32 0.01 0.28 0.1 GUJARCHEHTRI_GEN_4 30.55 32.58 10.1 9.06 1.29 1.96 11.27 0.26 1.02 0.2 0.12 0.77 0.22 0.12 0.32 0.16 GUJARCHEHTRI_GEN_5 33.25 33.0 7.04 7.92 1.85 2.38 9.99 0.23 0.24 1.46 0.57 1.48 0.24 0.04 0.23 0.07 GUJARCHEHTRI_GEN_6 32.62 32.31 8.14 8.12 1.96 2.42 9.81 0.06 1.44 0.41 1.04 1.07 0.23 0.09 0.17 0.1 GUJARCHEHTRI_GEN_7 31.69 33.16 7.41 7.75 1.6 1.83 10.36 0.77 0.91 1.61 0.44 1.58 0.37 0.06 0.35 0.1 GUJARCHEHTRI_GEN_8 31.41 33.89 9.04 8.77 1.35 1.25 9.87 0.65 1.04 1.12 0.69 0.21 0.37 0.12 0.17 0.04 GUJARCHEHTRI_GEN_9 32.97 32.75 7.64 8.75 1.34 1.85 10.73 0.15 0.53 0.06 0.84 1.9 0.29 0.1 0.04 0.04 GUJARCHEHTRI_GEN_10 31.84 31.41 9.89 8.37 1.12 2.19 10.25 0.33 0.08 1.23 0.68 2.03 0.23 0.07 0.21 0.05 GUJARCHEHTRI_GEN_11 31.77 33.9 10.62 6.96 0.33 2.2 10.35 0.25 0.51 0.82 1.37 0.66 0.05 0.03 0.03 0.15 GUJARCHEHTRI_GEN_12 32.45 32.39 9.31 7.72 0.69 1.66 10.19 0.9 1.08 0.39 1.21 1.63 0.08 0.01 0.06 0.23 GUJARCHEHTRI_GEN_13 30.73 32.73 10.13 6.63 0.98 2.21 10.62 0.54 1.03 0.79 1.58 1.25 0.3 0.06 0.26 0.15 GUJARCHEHTRI_GEN_14 31.84 33.97 9.34 7.59 0.58 2.42 9.77 0.69 0.91 0.55 0.29 1.21 0.39 0.1 0.18 0.16 GUJARCHEHTRI_GEN_15 31.89 32.27 8.95 8.55 1.49 1.34 10.94 0.22 1.0 0.72 0.89 1.09 0.19 0.1 0.17 0.18
Can you do one for south indian brahmins.

17. ## The Following User Says Thank You to iyer For This Useful Post:

geneticsikiru (10-06-2021)

18. Originally Posted by iyer
Can you do one for south indian brahmins.
you want to gen ghost South Indian brahmins ? we need an existing dataset for that.

19. ## The Following User Says Thank You to geneticsikiru For This Useful Post:

parasar (10-04-2021)

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