Rebalance temporal profile

I've discovered where the mass consistency problem may come from, related to the rebalance.

I've made some modifications in the code to get only the vertical/temporal factors as values, and I discovered interesting things:

• If you run 12 monthly outputs, the 12 values of each cell sum 12: everything is OK.
• If you run 365 daily output, it doesn't sum 365 for some cases
• It also happens when you run 8760 hours (a whole year)

Taking a vector profile, not flat, the results are the following +- 0.27%

e.g.

12 = (0.95)+(0.96)+(1.02)+(1)+(1.01)+(1.03)+(1.03)+(1.01)+(1.04)+(1.03)+(1.01)+(0.91)
365.04 = (31*0.95)+(28*0.96)+(31*1.02)+(30*1)+(31*1.01)+(30*1.03)+(31*1.03)+(31*1.01)+(30*1.04)+(31*1.03)+(30*1.01)+(31*0.91)

100*(365.04-365)/365 = 0.01%

The run of the 8760 hours provides the following results:

data = nessy.variables[var_name]['data'].sum(axis=(0,1))
print(data.min(), data.max())
# 8741.963936749911 8783.184

print(100*abs(data.min()-8760)/8760, 100*abs(data.max()-8760)/8760)
# 0.20589113299188097 0.2646575342465672

0.20% and 0.26%

FYI: @mguevara