def get_data_for_pre(name, cl_data, res_data, is_lo):
pre_data = []
if is_lo:
col = R_NAME_1_COL
col_other = R_NAME_2_COL
the_range = LO_D_RANGE
the_other_range = VI_D_RANGE
else:
col = R_NAME_2_COL
col_other = R_NAME_1_COL
the_range = VI_D_RANGE
the_other_range = LO_D_RANGE
cl = get_cl_data_for_name(name, cl_data)
final_cl = [cl[i] for i in the_range]
for res_d in res_data:
if res_d[col] == name:
cl_other = get_cl_data_for_name(res_d[col_other], cl_data)
final_cl_other = [cl_other[i] for i in the_other_range]
new_row = [cl_other[CL_POS_COL]]
new_row.extend(final_cl_other)
new_row.extend([res_d[RES_ELT_COL]])
pre_data.append(new_row)
return pre_data, [cl[CL_POS_COL]] + final_cl
def get_data_for_pre(name, cl_data, res_data, is_lo):
pre_data = []
if is_lo:
col = R_NAME_1_COL
col_other = R_NAME_2_COL
the_range = LO_D_RANGE
the_other_range = VI_D_RANGE
else:
col = R_NAME_2_COL
col_other = R_NAME_1_COL
the_range = VI_D_RANGE
the_other_range = LO_D_RANGE
cl = get_cl_data_for_name(name, cl_data)
final_cl = [cl[i] for i in the_range]
for res_d in res_data:
if res_d[col] == name:
cl_other = get_cl_data_for_name(res_d[col_other], cl_data)
final_cl_other = [cl_other[i] for i in the_other_range]
new_row = [ cl_other[CL_POS_COL] ]
new_row.extend(final_cl_other)
new_row.extend([res_d[RES_ELT_COL]])
pre_data.append(new_row)
return pre_data, [cl[CL_POS_COL]] + final_cl
def generate_final_hist(index, b1_hist, a2_hist, k, pro_data, pre_data, p_data, cldat):
hist = b1_hist + a2_hist
for i, k_elt in enumerate(k):
if k_elt[K_TYPE_COL] == TYPE_1_COL:
cl = cldat.b1
else:
cl = cldat.a2
new_row = [int(k_elt[TYPE_COL]), '']
if HIST_TYPE:
new_row.extend(pro_data[i])
new_row.extend(pre_data[i])
new_row.extend(p_data[i])
else:
cl_lo = get_cl_data_for_name(k_elt[NAME_LO_COL], cl)
new_row.extend([cl_lo[i] for i in LO_D_RANGE])
cl_vi = get_cl_data_for_name(k_elt[NAME_VI_COL], cl)
new_row.extend([cl_vi[i] for i in VI_D_RANGE])
new_row.extend([k_elt[NAME_LO_COL], k_elt[NAME_VI_COL]])
hist.append(new_row)
save_data_to_csv(AP_HIST_FILE, hist)
def gen_hist(res, cl, file_name, p_type):
print "Generating historical data for: %s" % file_name
hist = read_input_file(file_name)
j_res_max = max([ int(r[R_J_COL]) for r in res
if int(r[R_J_COL]) >= FIRST_ENTRY_HIST])
for j in range(FIRST_ENTRY_HIST, j_res_max + 1):
res_j = [ r for r in res if int(r[R_J_COL]) == j ]
res_data = [ r for r in res if int(r[R_J_COL]) < j ]
for r in res_j:
new_row = [int(p_type), r[R_M_COL]]
if HIST_TYPE:
pro_data = calc_pro_data(r, cl)
pre_data = calc_pre_data(r, cl, res_data)
p_data = PDat.calc_final_p(pro_data, pre_data, p_type)
new_row = [int(p_type), r[R_M_COL]]
new_row.extend(pro_data)
new_row.extend(pre_data)
new_row.extend(p_data)
new_row.extend([r[R_NAME_1_COL], r[R_NAME_2_COL]])
else:
cl_lo = get_cl_data_for_name(r[R_NAME_1_COL], cl)
new_row.extend([cl_lo[i] for i in LO_D_RANGE])
cl_vi = get_cl_data_for_name(r[R_NAME_2_COL], cl)
new_row.extend([cl_vi[i] for i in VI_D_RANGE])
new_row.extend([r[R_NAME_1_COL], r[R_NAME_2_COL]])
hist.append(new_row)
save_data_to_csv(file_name, hist)
return hist
def get_data_for_pro(name_col, p_range, cl_data):
data_from_name = get_cl_data_for_name(name_col, cl_data)
"""
print name_col
print data_from_name
print CL_POS_COL
"""
pos = int(data_from_name[CL_POS_COL])
data_from_range = [int(data_from_name[i]) for i in p_range]
data_for_calc = Pro._calculate_pro_data(data_from_range)
return data_for_calc, pos
def get_data_for_pro(name_col, p_range, cl_data):
data_from_name = get_cl_data_for_name(name_col, cl_data)
"""
print name_col
print data_from_name
print CL_POS_COL
"""
pos = int(data_from_name[CL_POS_COL])
data_from_range = [int(data_from_name[i]) for i in p_range]
data_for_calc = Pro._calculate_pro_data(data_from_range)
return data_for_calc, pos
