# Quiet Sun Model

|   Source
In [2]:
```import pymc
import numpy as np
import matplotlib.pyplot as pl
import seaborn as sn
import ipdb
%matplotlib inline
```
In [ ]:
```def LOS_to_LV(theta, costhetaB_LOS, sinthetaB_LOS, cosphiB_LOS, sinphiB_LOS):
costhetaB_LV = np.cos(theta) * costhetaB_LOS - np.sin(theta) * sinthetaB_LOS * cosphiB_LOS
sinthetaB_LV = np.sqrt(1.0-costhetaB_LV**2)
cosphiB_LV = (np.cos(theta) * sinthetaB_LOS * cosphiB_LOS + costhetaB_LOS * np.sin(theta)) / sinthetaB_LV
sinphiB_LV = sinthetaB_LOS * sinphiB_LOS / sinthetaB_LV

return costhetaB_LV, sinthetaB_LV, cosphiB_LV, sinphiB_LV

def LV_to_LOS(theta, costhetaB_LV, sinthetaB_LV, cosphiB_LV, sinphiB_LV):
costhetaB_LOS = np.cos(theta) * costhetaB_LV + np.sin(theta) * sinthetaB_LV * cosphiB_LV
sinthetaB_LOS = np.sqrt(1.0-costhetaB_LOS**2)
cosphiB_LOS = (np.cos(theta) * sinthetaB_LV * cosphiB_LV - costhetaB_LV * np.sin(theta)) / sinthetaB_LOS
sinphiB_LOS = sinthetaB_LV * sinphiB_LV / sinthetaB_LOS

return costhetaB_LOS, sinthetaB_LOS, cosphiB_LOS, sinphiB_LOS
```
Generate some fake data.
In [ ]:
```nPatches = 10
nMax = 50
nPointsPatch = nMax * np.random.rand(nPatches)
muPatch = np.random.rand(nPatches)

for i in range(nPatches):

```