From 872834f0862afaafb42e5f84cf8c9999a8c19b8c Mon Sep 17 00:00:00 2001
From: Tim <jbt5jf@virginia.edu>
Date: Sun, 21 Apr 2024 13:15:27 -0400
Subject: [PATCH] sigpy not necessary

---
 brkraw/lib/recoSigpy.py | 52 -----------------------------------------
 1 file changed, 52 deletions(-)
 delete mode 100644 brkraw/lib/recoSigpy.py

diff --git a/brkraw/lib/recoSigpy.py b/brkraw/lib/recoSigpy.py
deleted file mode 100644
index ac7fada..0000000
--- a/brkraw/lib/recoSigpy.py
+++ /dev/null
@@ -1,52 +0,0 @@
-"""
-Created on Sat Feb 08 2024
- DEVELOPED FOR BRUKER PARAVISION 360 datasets
- Below code will work for 3D cartesian sequence 
- undersampled in the y-z phase view
- Reconstructions are completed with SIGPY
-
- THIS IS A WORK IN PROGRESS due to a lack of CS 
- datasets from PV360.3.3 or higher
-
-@author: Tim Ho (UVA) 
-"""
-
-import sigpy as sp 
-import sigpy.mri as mr
-import numpy as np 
-
-from .utils import get_value
-
-def compressed_sensing_recon(data, acqp, meth, reco, lamda=0.01, method=None):
-    # Meta Variables
-    ky = get_value(meth,'PVM_EncGenSteps1')
-    kz = get_value(meth,'PVM_EncGenSteps2')
-    PVM_EncGenTotalSteps = get_value(meth, 'PVM_EncGenTotalSteps')
-
-    kspaceShape = [1 for _ in range(7)] 
-    RECO_ft_size = get_value(reco, 'RECO_ft_size')
-    NI = get_value(acqp, 'NI')
-    NR = get_value(acqp, 'NR')
-    kspaceShape[1:len(RECO_ft_size)+1] = RECO_ft_size
-    kspaceShape[0] = data.shape[1]
-    kspaceShape[5] = NI 
-    kspaceShape[6] = NR
-
-    # Resort Raw Sorted to K-SPACE
-    frame_sort = data.reshape((NR, PVM_EncGenTotalSteps, NI, kspaceShape[0], RECO_ft_size[0]))
-
-    k_space = np.zeros(shape=kspaceShape, dtype=complex)
-    for index, (i,j) in enumerate(zip(ky,kz)):
-        k_space[:,:,int(i+max(ky)), j+max(kz)+1,0,:,:]= frame_sort[:,index,:,:,:].transpose(2,3,1,0)
-
-
-    N1, N2, N3, N4, N5, N6, N7 = k_space.shape
-    output = np.zeros(shape=(N2,N3,N4,N6,N7), dtype=complex)
-    for NR in range(N7):
-        for NI in range(N6):
-            k_space = np.squeeze(k_space[:,:,:,:,:,NI,NR])
-            # Compressed Sensing
-            sens = mr.app.EspiritCalib(k_space).run()
-            output[:,:,:,NI,NR] = mr.app.L1WaveletRecon(k_space, sens, lamda=lamda).run()
-    
-    return output
\ No newline at end of file