removed multi processing from master (moved into extra branch)

SineTools.py

@@ -14,13 +14,6 @@ from scipy.sparse.linalg import lsqr as sp_lsqr
 import matplotlib.pyplot as mp
 import warnings
 
-import multiprocessing
-from multiprocessing import shared_memory
-from tqdm.contrib.concurrent import process_map
-
-
-manager = multiprocessing.Manager()
-mpdata = manager.dict()
 def sampletimes(Fs, T):  #
     """
     generate a t_i vector with \n
@@ -121,33 +114,6 @@ def threeparsinefit(y, t, f0):
     abc = la.lstsq(a.transpose(), y)
     return abc[0][0:3]  ## fit vector a*sin+b*cos+c
 
-# multiprocessing wraper for threeparsinefit
-def multiPrcoess3paramFit(i):
-    """
-    mpdata['t_shared_name'] = shmY.name
-    mpdata['y_shared_name'] = shmT.name
-    mpdata['t_shared_type'] = y.dtype
-    mpdata['y_shared_type'] = t.dtype
-    mpdata['freq'] = f0
-    mpdata['samples_per_block'] = N
-    mpdata['nmumber_of_samples'] = y.size
-    """
-    N=mpdata['samples_per_block']
-
-    existing_shm_t = shared_memory.SharedMemory(name=mpdata['t_shared_name'])
-    t = np.ndarray((mpdata['nmumber_of_samples'],), dtype=mpdata['t_shared_type'], buffer=existing_shm_t.buf)
-    ti = t[i * N: (i + 1) * N]
-
-    existing_shm_y = shared_memory.SharedMemory(name=mpdata['y_shared_name'])
-    y = np.ndarray((mpdata['nmumber_of_samples'],), dtype=mpdata['y_shared_type'], buffer=existing_shm_y.buf)
-    yi = y[i * N: (i + 1) * N]
-    result=threeparsinefit(yi, ti, mpdata['freq'])
-    # Clean up
-    del t  # Unnecessary; merely emphasizing the array is no longer used
-    del y  # Unnecessary; merely emphasizing the array is no longer used
-    existing_shm_t.close()
-    existing_shm_y.close()
-    return result
 
 # sine fit at known frequency and detrending
 def threeparsinefit_lin(y, t, f0):
@@ -215,7 +181,7 @@ def phase_delay(A1, A2, deg=False):
 
 
 # periodical sinefit at known frequency
-def seq_threeparsinefit(y, t, f0, periods=1, multiTasking=True):
+def seq_threeparsinefit(y, t, f0, periods=1):
     """
     period-wise sine-fit at a known frequency\n
     y vector of sample values \n
@@ -239,40 +205,11 @@ def seq_threeparsinefit(y, t, f0, periods=1, multiTasking=True):
     M = int(t.size // N)  ## number of sections or periods
 
     abc = np.zeros((M, 3))
-    if multiTasking==False:
-        for i in range(int(M)):
-            ti = t[i * N : (i + 1) * N]
-            yi = y[i * N : (i + 1) * N]
-            abc[i, :] = threeparsinefit(yi, ti, f0)
-        return abc  ## matrix of all fit vectors per period
-    else:
-        shmY = shared_memory.SharedMemory(create=True, size=y.nbytes)
-        shmT = shared_memory.SharedMemory(create=True, size=t.nbytes)
-        # Now create a NumPy array backed by shared memory
-        #to accesdta use this
-        tmpY = np.ndarray(y.shape, dtype=y.dtype, buffer=shmY.buf)
-        tmpT = np.ndarray(t.shape, dtype=t.dtype, buffer=shmT.buf)
-        tmpY[:] = y[:]
-        tmpT[:] = t[:]
-        mpdata['t_shared_name'] = shmT.name
-        mpdata['y_shared_name'] = shmY.name
-        mpdata['t_shared_type'] = y.dtype
-        mpdata['y_shared_type'] = t.dtype
-        mpdata['freq'] = f0
-        mpdata['samples_per_block']=N
-        mpdata['nmumber_of_samples']=y.size
-        numCoresToBeUsed=int(multiprocessing.cpu_count()-1)
-
-        i = np.arange(M)
-        mpchunksize=10
-        results = process_map(multiPrcoess3paramFit, i,chunksize=mpchunksize, max_workers=numCoresToBeUsed)
-        shmT.close()
-        shmT.unlink()  # Free and release the shared memory block at the very end
-        shmY.close()
-        shmY.unlink()  # Free and release the shared memory block at the very end
-        for i in np.arange(M):
-            abc[i,:]=results[i]
-        return abc
+    for i in range(int(M)):
+        ti = t[i * N : (i + 1) * N]
+        yi = y[i * N : (i + 1) * N]
+        abc[i, :] = threeparsinefit(yi, ti, f0)
+    return abc  ## matrix of all fit vectors per period
 
 
 
@@ -310,34 +247,6 @@ def fourparsinefit(y, t, f0, tol=1.0e-7, nmax=1000):
 
     return np.hstack((abcd[0:3], w / (2 * np.pi)))
 
-# multiprocessing wraper for threeparsinefit
-def multiPrcoess4paramFit(i):
-    """
-    mpdata['t_shared_name'] = shmY.name
-    mpdata['y_shared_name'] = shmT.name
-    mpdata['t_shared_type'] = y.dtype
-    mpdata['y_shared_type'] = t.dtype
-    mpdata['freq'] = f0
-    mpdata['samples_per_block'] = N
-    mpdata['nmumber_of_samples'] = y.size
-    """
-    N=mpdata['samples_per_block']
-
-    existing_shm_t = shared_memory.SharedMemory(name=mpdata['t_shared_name'])
-    t = np.ndarray((mpdata['nmumber_of_samples'],), dtype=mpdata['t_shared_type'], buffer=existing_shm_t.buf)
-    ti = t[i * N: (i + 1) * N]
-
-    existing_shm_y = shared_memory.SharedMemory(name=mpdata['y_shared_name'])
-    y = np.ndarray((mpdata['nmumber_of_samples'],), dtype=mpdata['y_shared_type'], buffer=existing_shm_y.buf)
-    yi = y[i * N: (i + 1) * N]
-    result=fourparsinefit(yi, ti, mpdata['freq'])
-    # Clean up
-    del t  # Unnecessary; merely emphasizing the array is no longer used
-    del y  # Unnecessary; merely emphasizing the array is no longer used
-    existing_shm_t.close()
-    existing_shm_y.close()
-    return result
-
 
 def calc_fourparsine(abcf, t):
     """
@@ -372,7 +281,7 @@ endfunction
 """
 
 # periodical sinefit at known frequency
-def seq_fourparsinefit(y, t, f0, tol=1.0e-7, nmax=1000, periods=1, debug_plot=False, multiTasking=True):
+def seq_fourparsinefit(y, t, f0, tol=1.0e-7, nmax=1000, periods=1, debug_plot=False):
     """
     sliced or period-wise sine-fit at a known frequency\n
     y vector of sample values \n
@@ -398,39 +307,10 @@ def seq_fourparsinefit(y, t, f0, tol=1.0e-7, nmax=1000, periods=1, debug_plot=Fa
     M = int(t.size // N)  ## number of sections or periods
 
     abcd = np.zeros((M, 4))
-    if multiTasking == False:
-        for i in range(M):
-            ti = t[i * N : (i + 1) * N]
-            yi = y[i * N : (i + 1) * N]
-            abcd[i, :] = fourparsinefit(yi, ti, f0, tol=tol, nmax=nmax)
-    else:
-        shmY = shared_memory.SharedMemory(create=True, size=y.nbytes)
-        shmT = shared_memory.SharedMemory(create=True, size=t.nbytes)
-        # Now create a NumPy array backed by shared memory
-        #to accesdta use this
-        tmpY = np.ndarray(y.shape, dtype=y.dtype, buffer=shmY.buf)
-        tmpT = np.ndarray(t.shape, dtype=t.dtype, buffer=shmT.buf)
-        tmpY[:] = y[:]
-        tmpT[:] = t[:]
-        mpdata['t_shared_name'] = shmT.name
-        mpdata['y_shared_name'] = shmY.name
-        mpdata['t_shared_type'] = y.dtype
-        mpdata['y_shared_type'] = t.dtype
-        mpdata['freq'] = f0
-        mpdata['samples_per_block']=N
-        mpdata['nmumber_of_samples']=y.size
-        numCoresToBeUsed=int(multiprocessing.cpu_count()-1)
-
-        i = np.arange(M)
-        mpchunksize=10
-        results = process_map(multiPrcoess4paramFit, i,chunksize=mpchunksize, max_workers=numCoresToBeUsed)
-        shmT.close()
-        shmT.unlink()  # Free and release the shared memory block at the very end
-        shmY.close()
-        shmY.unlink()  # Free and release the shared memory block at the very end
-        for i in np.arange(M):
-            abcd[i,:]=results[i]
-        return abcd
+    for i in range(M):
+        ti = t[i * N : (i + 1) * N]
+        yi = y[i * N : (i + 1) * N]
+        abcd[i, :] = fourparsinefit(yi, ti, f0, tol=tol, nmax=nmax)
 
     if debug_plot:
         mp.ioff()