12340932278

Committed 15 Dec 2024 05:42PM CUT coverage: 45.447%. Remained the same

Build # 12340932278

Build Type

Pull #4

github

Committed by

web-flow

Commit Message

Merge d90cfa984 into 2ffee6f33

Pull Request Pull Request #4: build(deps): bump actions/setup-python from 4 to 5

Run Details

529 of 1164 relevant lines covered (45.45%)

2.27 hits per line

Source File
Press 'n' to go to next uncovered line, 'b' for previous

19.15

/src/pyslice/pyslice_lib/PySPG/MeanCalculation.py

import copy
import sys

from .Load import dumpData, loadData
from .ParamParser import ParamParser

#
#
# :::~ Author: Claudio Juan Tessone <tessonec@imedea.uib.es> (c) 2002-2003
#
# Distributed According to GNU Generic Purpose License (GPL)
# Please visit www.gnu.org
#
#


class MeanCalculation:
    """
    When a simulation is run with a given number of repetitions what is needed is a
    tool for calculating the mean values. This is that tool
    """

    def __init__(self, lsLines):
        ls2 = [i for i in lsLines if i.strip()[0] != "#"]

        self.pp_varying = ParamParser(ls2)

    def searchinfile(self, fname, st):
        return [i.split() for i in open(fname).readlines() if i.split()[0] == st]

    def mean(self, fin_name="out.dat", fout_name="out.mean"):
        dirname = self.pp_varying.directory_tree()

        fsearch = f"{dirname}{fin_name}"
        fout = f"{dirname}{fout_name}"

        try:
            all_data = loadData(fsearch)
        except Exception:
            print(f"Error! '{fsearch}' file doesn't exist or not enough permissions...")
            sys.exit()

        try:
            columnas = list(range(len(all_data[0])))
        except Exception:
            sys.stderr.write(f"mean-> error! in directory '{dirname}'\n")
            sys.stderr.write(f"all_data = {str(all_data)}\n")

        tmpDict = {i[0]: 0 for i in all_data}
        valuesX = sorted(tmpDict.keys())
        xDict = {}
        nPoints = {}

        zeros = [0 for _ in columnas]

        for i in valuesX:
            xDict[i] = copy.copy(zeros)
            nPoints[i] = 0.0

        for row in all_data:
            if len(row) == len(all_data[0]):
                nPoints[row[0]] += 1.0
                vec = xDict[row[0]]
                for j in columnas:
                    vec[j] += row[j]

        dataOut = [
            [(xDict[x][j] // nPoints[x]) for j in columnas]
            for x in valuesX
            if nPoints[x] != 0
        ]
        try:
            dumpData(fout, dataOut)
        except Exception:
            print(f"Error! Couldn't create output file '{fsearch}' ...")
            sys.exit()

    def doit(self, fin_name="out.dat", fout_name="mean.dat"):
        for _ in self.pp_varying:
            self.mean(fin_name, fout_name)


#
#

1	import copy	5✔
2	import sys	5✔
3
4	from .Load import dumpData, loadData	5✔
5	from .ParamParser import ParamParser	5✔
6
7	#
8	#
9	# :::~ Author: Claudio Juan Tessone <tessonec@imedea.uib.es> (c) 2002-2003
10	#
11	# Distributed According to GNU Generic Purpose License (GPL)
12	# Please visit www.gnu.org
13	#
14	#
15
16
17	class MeanCalculation:	5✔
18	"""
19	When a simulation is run with a given number of repetitions what is needed is a
20	tool for calculating the mean values. This is that tool
21	"""
22
23	def __init__(self, lsLines):	5✔
24	ls2 = [i for i in lsLines if i.strip()[0] != "#"]	×
25
26	self.pp_varying = ParamParser(ls2)	×
27
28	def searchinfile(self, fname, st):	5✔
29	return [i.split() for i in open(fname).readlines() if i.split()[0] == st]	×
30
31	def mean(self, fin_name="out.dat", fout_name="out.mean"):	5✔
32	dirname = self.pp_varying.directory_tree()	×
33
34	fsearch = f"{dirname}{fin_name}"	×
35	fout = f"{dirname}{fout_name}"	×
36
37	try:	×
38	all_data = loadData(fsearch)	×
39	except Exception:	×
40	print(f"Error! '{fsearch}' file doesn't exist or not enough permissions...")	×
41	sys.exit()	×
42
43	try:	×
44	columnas = list(range(len(all_data[0])))	×
45	except Exception:	×
46	sys.stderr.write(f"mean-> error! in directory '{dirname}'\n")	×
47	sys.stderr.write(f"all_data = {str(all_data)}\n")	×
48
49	tmpDict = {i[0]: 0 for i in all_data}	×
50	valuesX = sorted(tmpDict.keys())	×
51	xDict = {}	×
52	nPoints = {}	×
53
54	zeros = [0 for _ in columnas]	×
55
56	for i in valuesX:	×
57	xDict[i] = copy.copy(zeros)	×
58	nPoints[i] = 0.0	×
59
60	for row in all_data:	×
61	if len(row) == len(all_data[0]):	×
62	nPoints[row[0]] += 1.0	×
63	vec = xDict[row[0]]	×
64	for j in columnas:	×
65	vec[j] += row[j]	×
66
67	dataOut = [	×
68	[(xDict[x][j] // nPoints[x]) for j in columnas]
69	for x in valuesX
70	if nPoints[x] != 0
71	]
72	try:	×
73	dumpData(fout, dataOut)	×
74	except Exception:	×
75	print(f"Error! Couldn't create output file '{fsearch}' ...")	×
76	sys.exit()	×
77
78	def doit(self, fin_name="out.dat", fout_name="mean.dat"):	5✔
79	for _ in self.pp_varying:	×
80	self.mean(fin_name, fout_name)	×
81
82
83	#
84	#

timcera / pyslice / 12340932278

Source File Press 'n' to go to next uncovered line, 'b' for previous

Source File
Press 'n' to go to next uncovered line, 'b' for previous