```python
import numpy as np
import pandas as pd
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib nbagg
plt.ioff()    # this stops the graphs from overwriting each other

from sympy import *
a, s, t, u, v, w, x, y, z = symbols("a, s, t, u, v, w, x, y, z")
k, m, n = symbols("k, m, n", integer=True)
f, g, h = symbols("f, g, h", cls=Function)
init_printing()

# Enable viewing of variables and expressions without print statements
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
```


```python
mvec_df = pd.read_csv('mvec.csv')
mvec_df.describe()
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>AggPercEnd</th>
      <th>AggPercSix</th>
      <th>LagSec</th>
      <th>Slope</th>
      <th>AUC</th>
      <th>PercWBC</th>
      <th>PercRBC</th>
      <th>PercHCT</th>
      <th>PercPLT</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>count</th>
      <td>36.000000</td>
      <td>36.000000</td>
      <td>36.000000</td>
      <td>36.000000</td>
      <td>36.000000</td>
      <td>36.000000</td>
      <td>36.000000</td>
      <td>36.000000</td>
      <td>36.000000</td>
    </tr>
    <tr>
      <th>mean</th>
      <td>0.766667</td>
      <td>0.324306</td>
      <td>229.138889</td>
      <td>23.916667</td>
      <td>201.111111</td>
      <td>-0.194734</td>
      <td>-0.171356</td>
      <td>-0.176014</td>
      <td>0.127047</td>
    </tr>
    <tr>
      <th>std</th>
      <td>0.173617</td>
      <td>0.174897</td>
      <td>77.745061</td>
      <td>9.345358</td>
      <td>63.326337</td>
      <td>0.292090</td>
      <td>0.315597</td>
      <td>0.315857</td>
      <td>0.416223</td>
    </tr>
    <tr>
      <th>min</th>
      <td>0.500000</td>
      <td>0.000000</td>
      <td>138.000000</td>
      <td>9.000000</td>
      <td>110.800000</td>
      <td>-0.700730</td>
      <td>-0.759615</td>
      <td>-0.762749</td>
      <td>-0.470000</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>0.643750</td>
      <td>0.225000</td>
      <td>178.250000</td>
      <td>19.500000</td>
      <td>162.500000</td>
      <td>-0.307284</td>
      <td>-0.385859</td>
      <td>-0.392710</td>
      <td>-0.175833</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>0.725000</td>
      <td>0.337500</td>
      <td>214.000000</td>
      <td>23.000000</td>
      <td>183.500000</td>
      <td>-0.203846</td>
      <td>-0.118924</td>
      <td>-0.116919</td>
      <td>0.101313</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>0.875000</td>
      <td>0.431250</td>
      <td>275.000000</td>
      <td>27.500000</td>
      <td>234.200000</td>
      <td>-0.041898</td>
      <td>0.048740</td>
      <td>0.049328</td>
      <td>0.424242</td>
    </tr>
    <tr>
      <th>max</th>
      <td>1.050000</td>
      <td>0.650000</td>
      <td>468.000000</td>
      <td>43.000000</td>
      <td>331.900000</td>
      <td>0.275547</td>
      <td>0.250000</td>
      <td>0.241685</td>
      <td>0.788732</td>
    </tr>
  </tbody>
</table>
</div>




```python
timemvec_df = mvec_df[mvec_df['Adjustment']=='Time']
# wt_only = timemvec_df[timemvec_df['Genotype']=='WT']
lag_plt = sns.boxplot(x="Genotype",y="LagSec", data=timemvec_df)
plt.show()
```


    <IPython.core.display.Javascript object>







```python
plt.figure()
sns.boxplot(x="Genotype",y="AggPercEnd", data=timemvec_df)
plt.show()
plt.figure()
sns.swarmplot(x="Genotype",y="AggPercEnd", data=timemvec_df)
```


    <IPython.core.display.Javascript object>









    <matplotlib.axes._subplots.AxesSubplot at 0x1f6b61f2f60>




    <IPython.core.display.Javascript object>









    <matplotlib.axes._subplots.AxesSubplot at 0x1f6b6267dd8>




```python
plt.figure()
sns.boxplot(x="Genotype",y="AggPercSix", data=timemvec_df)
plt.show()
```


    <IPython.core.display.Javascript object>









    <matplotlib.axes._subplots.AxesSubplot at 0x1f6b62ab7b8>




```python
plt.figure()
sns.boxplot(x="Genotype",y="Slope", data=timemvec_df)
plt.show()
```


    <IPython.core.display.Javascript object>









    <matplotlib.axes._subplots.AxesSubplot at 0x1f6b6321860>




```python
plt.figure()
sns.boxplot(x="Genotype",y="AUC", data=timemvec_df)
plt.show()
```


    <IPython.core.display.Javascript object>









    <matplotlib.axes._subplots.AxesSubplot at 0x1f6b631f6a0>




```python
plt.figure()
sns.boxplot(x="Genotype",y="PercWBC", data=timemvec_df)
plt.show()
```


    <IPython.core.display.Javascript object>









    <matplotlib.axes._subplots.AxesSubplot at 0x1f6b640b320>




```python
plt.figure()
sns.boxplot(x="Genotype",y="PercRBC", data=timemvec_df)
plt.show()
```


    <IPython.core.display.Javascript object>









    <matplotlib.axes._subplots.AxesSubplot at 0x1f6b6476fd0>




```python
plt.figure()
sns.boxplot(x="Genotype",y="PercHCT", data=timemvec_df)
plt.show()
```


    <IPython.core.display.Javascript object>









    <matplotlib.axes._subplots.AxesSubplot at 0x1f6b64ef400>




```python
plt.figure()
sns.boxplot(x="Genotype",y="PercPLT", data=timemvec_df)
plt.show()
```


    <IPython.core.display.Javascript object>









    <matplotlib.axes._subplots.AxesSubplot at 0x1f6b6561eb8>



298fd206b7d6092603945d1e77300a155a3671c4
ipynb
Jupyter Notebook
genetic-analyses/summaryM.ipynb
jeffchiou/mini-data-analyses
d25522f10308959585933cdbacd391500dfa6bb1
MIT
genetic-analyses/summaryM.ipynb
jeffchiou/mini-data-analyses
d25522f10308959585933cdbacd391500dfa6bb1
MIT
genetic-analyses/summaryM.ipynb
jeffchiou/mini-data-analyses
d25522f10308959585933cdbacd391500dfa6bb1
MIT
Qwen/Qwen-72B
1. YES
2. YES