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R3127/BlackDynamite/BDstat.py
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BDstat.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# -*- py-which-shell: "python"; -*-
from __future__ import print_function
from . import job
import numpy as np
class BDStat(object):
"""
"""
def average(self, quantity, run_list, entries_to_average):
result = dict()
run_info = dict()
cpt = dict()
run_counter = 0
nb_runs = len(run_list)
entries_to_average.append("id")
entries_to_consider = []
if (run_list is not []):
for e in run_list[0][1].entries:
if e not in entries_to_average:
entries_to_consider.append(e)
entries_to_consider = sorted(entries_to_consider)
# print (entries_to_consider)
for r, j in run_list:
q = r.getScalarQuantity(quantity)
run_counter += 1
if (q is None):
continue
print("{0:<5} {1:<15} {2:>5}/{3:<5} {4:.1f}%".format(
r.id, q.shape, run_counter, nb_runs,
1.*run_counter/nb_runs*100))
ent = [j[i] for i in entries_to_consider]
ent = tuple(ent)
if (ent not in result.keys()):
# print (ent)
result[ent] = np.zeros([q.shape[0], 3])
cpt[ent] = 0
# print (result[ent].shape)
# print (q.shape)
sz1 = q.shape[0]
sz2 = result[ent].shape[0]
if (sz1 > sz2):
q = q[:sz2]
elif (sz2 > sz1):
result[ent] = result[ent][:sz1]
result[ent][:, 0] += q[:, 0]
result[ent][:, 1] += q[:, 1]
result[ent][:, 2] += q[:, 1]**2
cpt[ent] += 1
for ent in result.keys():
# print (ent)
myjob = job.Job(self.base)
for i in range(0, len(entries_to_consider)):
myjob.entries[entries_to_consider[i]] = ent[i]
result[ent] /= cpt[ent]
result[ent][:, 2] -= result[ent][:, 1]**2
result[ent][:, 2] = np.maximum(
np.zeros(result[ent][:, 2].shape[0]), result[ent][:, 2])
result[ent][:, 2] = np.sqrt(result[ent][:, 2])
result[ent] = {"ref_job": myjob,
"averaged_number": cpt[ent],
"data": result[ent]
}
return result
def averageVector(self, quantity, run_list, entries_to_average):
result = dict()
all_steps = dict()
run_info = dict()
cpt = dict()
run_counter = 0
nb_runs = len(run_list)
entries_to_average.append("id")
entries_to_consider = []
if (run_list is not []):
for e in run_list[0][1].entries:
if e not in entries_to_average:
entries_to_consider.append(e)
for r, j in run_list:
steps, data = r.getAllVectorQuantity(quantity)
run_counter += 1
if (data is None):
continue
print("{0:<5} {1:<15} {2:>5}/{3:<5} {4:.1f}%".format(
r.id, data.shape, run_counter, nb_runs,
1.*run_counter/nb_runs*100))
ent = [j[i] for i in entries_to_consider]
ent = tuple(ent)
if (ent not in result.keys()):
# print (ent)
result[ent] = np.zeros([data.shape[0], data.shape[1], 2])
all_steps[ent] = steps
cpt[ent] = 0
# print (result[ent].shape)
# print (q.shape)
sz1 = data.shape[0]
sz2 = result[ent].shape[0]
if (sz1 > sz2):
data = data[:sz2]
steps = steps[:sz2]
elif (sz2 > sz1):
result[ent] = result[ent][:sz1]
all_steps[ent] = all_steps[ent][:sz1]
result[ent][:, :, 0] += data[:, :]
result[ent][:, :, 1] += data[:, :]**2
cpt[ent] += 1
for ent in result.keys():
# print (ent)
myjob = job.Job(self.base)
for i in range(0, len(entries_to_consider)):
myjob.entries[entries_to_consider[i]] = ent[i]
result[ent] /= cpt[ent]
result[ent][:, :, 1] -= result[ent][:, :, 0]**2
result[ent][:, :, 1] = np.sqrt(result[ent][:, :, 1])
result[ent] = {"ref_job": myjob,
"averaged_number": cpt[ent],
"steps": all_steps[ent],
"data": result[ent]}
return result
def __init__(self, base, **params):
self.base = base
################################################################
__all__ = ["BDStat"]
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