rainflow error fixed but test fails

wetb/fatigue_tools/fatigue.py

@@ -104,7 +104,7 @@ def eq_load_and_cycles(signals, no_bins=46, m=[3, 4, 6, 8, 10, 12], neq=[10 ** 6
     if 0:  #to be similar to windap
         ampl_bin_mean = (ampl_bin_edges[:-1] + ampl_bin_edges[1:]) / 2
         cycles, ampl_bin_mean = cycles.flatten(), ampl_bin_mean.flatten()
-    eq_loads = [[((np.sum(cycles * ampl_bin_mean ** _m) / _neq) ** (1. / _m)) for _m in np.atleast_1d(m)]  for _neq in np.atleast_1d(neq)]
+    eq_loads = [[((np.nansum(cycles * ampl_bin_mean ** _m) / _neq) ** (1. / _m)) for _m in np.atleast_1d(m)]  for _neq in np.atleast_1d(neq)]
     return eq_loads, cycles, ampl_bin_mean, ampl_bin_edges
 
 
@@ -147,7 +147,8 @@ def cycle_matrix(signals, ampl_bins=10, mean_bins=10, rainflow_func=rainflow_win
     >>> cycles, ampl_bin_mean, ampl_edges, mean_bin_mean, mean_edges = cycle_matrix(signal)
     >>> cycles, ampl_bin_mean, ampl_edges, mean_bin_mean, mean_edges = cycle_matrix([(.4, signal), (.6,signal)])
     """
-
+    if len(signals)==0:
+        print ("here")
     if isinstance(signals[0], tuple):
         weights, ampls, means = np.array([(np.zeros_like(ampl)+weight,ampl,mean) for weight, signal in signals for ampl,mean in rainflow_func(signal[:]).T], dtype=np.float64).T
     else:
@@ -158,12 +159,9 @@ def cycle_matrix(signals, ampl_bins=10, mean_bins=10, rainflow_func=rainflow_win
     cycles, ampl_edges, mean_edges = np.histogram2d(ampls, means, [ampl_bins, mean_bins], weights=weights)
 
     ampl_bin_sum = np.histogram2d(ampls, means, [ampl_bins, mean_bins], weights=weights * ampls)[0]
-    ampl_bin_mean = np.zeros_like(cycles)
-    mask = (cycles > 0)
-    ampl_bin_mean[mask] = ampl_bin_sum[mask] / cycles[mask]
+    ampl_bin_mean = np.nanmean(ampl_bin_sum / np.where(cycles,cycles,np.nan),1)
     mean_bin_sum = np.histogram2d(ampls, means, [ampl_bins, mean_bins], weights=weights * means)[0]
-    mean_bin_mean = np.zeros_like(cycles)+np.nan
-    mean_bin_mean[cycles > 0] = mean_bin_sum[cycles > 0] / cycles[cycles > 0]
+    mean_bin_mean = np.nanmean(mean_bin_sum / np.where(cycles, cycles, np.nan), 1)
     cycles = cycles / 2  # to get full cycles
     return cycles, ampl_bin_mean, ampl_edges, mean_bin_mean, mean_edges