def test_create_non_equidistant_calendar():
dfs0file = r'random12.dfs0'
data = np.random.random([1000, 2])
data[2, :] = np.nan
start_time = datetime.datetime(2017, 1, 1)
time_vector = []
for i in range(1000):
time_vector.append(start_time + datetime.timedelta(hours=i * 0.1))
title = 'Hello Test'
names = ['VarFun01', 'NotFun']
variable_type = [100000, 100000]
unit = [1000, 1000]
data_value_type = [0, 1]
dfs = dfs0.dfs0()
dfs.create_non_equidistant_calendar(dfs0file=dfs0file,
data=data,
time_vector=time_vector,
names=names,
title=title,
variable_type=variable_type,
unit=unit,
data_value_type=data_value_type)
assert True
def test_create_timestep_7days():
dfs0File = r"zeros_ones.dfs0"
data = []
nt = 10
d1 = np.zeros(nt)
data.append(d1)
d2 = np.ones(nt)
data.append(d2)
names = ["Zeros", "Ones"]
dfs = dfs0.dfs0()
dfs.create(filename=dfs0File,
data=data,
names=names,
title="Zeros and ones",
timeseries_unit=TimeStep.DAY,
dt=7)
assert True
res = dfs.read(dfs0File)
dt = res.time[1] - res.time[0]
assert dt == timedelta(days=7)
os.remove(dfs0File)
def test_create_equidistant_calendar():
dfs0file = r'random11.dfs0'
data = np.random.random([1000, 2])
data[2, :] = np.nan
start_time = datetime.datetime(2017, 1, 1)
timeseries_unit = 1402
title = 'Hello Test'
names = ['VarFun01', 'NotFun']
variable_type = [100000, 100000]
unit = [1000, 1000]
data_value_type = [0, 1]
dt = 5
dfs = dfs0.dfs0()
dfs.create_equidistant_calendar(dfs0file=dfs0file,
data=data,
start_time=start_time,
timeseries_unit=timeseries_unit,
dt=dt,
names=names,
title=title,
variable_type=variable_type,
unit=unit,
data_value_type=data_value_type)
#os.remove(dfs0file)
assert True
def test_read_dfs0_to_matrix():
dfs0file = r'tests/testdata/random.dfs0'
dfs = dfs0.dfs0()
mat = dfs.read(dfs0file, indices=[0])[0]
assert np.isnan(mat[2, 0])
def test_read_dfs0_to_matrix():
dfs0file = r'tests/testdata/random.dfs0'
dfs = dfs0.dfs0()
(data, t, names) = dfs.read(filename=dfs0file)
assert len(data) == 2
def test_read_dfs0_to_pandas():
dfs0file = r'tests/testdata/random.dfs0'
dfs = dfs0.dfs0()
pd = dfs.read_to_pandas(dfs0file)
assert np.isnan(pd[pd.columns[0]][2])
def test_read_dfs0_single_item():
dfs0file = r'tests/testdata/random.dfs0'
dfs = dfs0.dfs0()
(data, t, names) = dfs.read(dfs0file, item_numbers=[1])
assert len(data) == 1
def test_read_dfs0_to_pandas_single_item():
dfs0file = r'tests/testdata/random.dfs0'
dfs = dfs0.dfs0()
df = dfs.read_to_pandas(dfs0file, item_numbers=[1])
assert df.shape[1] == 1
def test_read_dfs0_single_item_named_access():
dfs0file = r'tests/testdata/random.dfs0'
dfs = dfs0.dfs0()
res = dfs.read(dfs0file, item_numbers=[1])
data = res.data
assert len(data) == 1
def convertDFS02Excel(dfs0Filename, outputFilename="out.csv"):
dfs = dfs0.dfs0()
data, t, names = dfs.read(dfs0Filename)
print("outputfilename = " + outputFilename)
fileHandle = open(outputFilename, "w")
fileHandle.write(names[0] + "\n")
for i in range(len(data)):
line = str(t[i]) + ',' + str(data[i][0]) + '\n'
fileHandle.write(line)
fileHandle.close()
print('data')
print(data[0])
print('t')
print(t[0])
print('names')
print(names)
def test_simple_create():
dfs0File = r"simple.dfs0"
data = []
nt = 100
d = np.random.random([nt])
data.append(d)
dfs = dfs0.dfs0()
dfs.create(filename=dfs0File, data=data)
assert True
os.remove(dfs0File)
def test_multiple_create():
dfs0File = r"zeros_ones.dfs0"
data = []
nt = 10
d1 = np.zeros(nt)
data.append(d1)
d2 = np.ones(nt)
data.append(d2)
names = ["Zeros", "Ones"]
dfs = dfs0.dfs0()
dfs.create(filename=dfs0File,
data=data,
names=names,
title="Zeros and ones")
assert True
os.remove(dfs0File)
def dfs0Generating(inputFilename,
outputFilename='default',
titleName="Rainfall",
name='Binh_Dinh'):
dfs0file = outputFilename + '.dfs0'
# dfs0file_1 = outputFilename + 'bis.dfs0'
readData = np.loadtxt(fname=inputFilename,
delimiter=',',
skiprows=0,
dtype='str')
print("inputfile = " + inputFilename)
print('outputfile = ' + outputFilename)
if (len(readData) == 0):
return
newData = []
for i in readData[:, 1]:
newData.append([float(i)])
newData1 = np.asarray(newData)
time_vector = []
for i in range(len(newData)):
time_vector.append(
datetime.strptime(readData[i, 0], '%d-%m-%Y %H:%M:%S'))
title = titleName
names = [name]
if (titleName == 'Rainfall'):
variable_type = [100004]
unit = [1002] #1000 meter, 1001 km, 1002 mm
data_value_type = [2]
elif (titleName == 'Evaporation'):
variable_type = [100005]
unit = [1002] #1000 meter, 1001 km, 1002 mm
data_value_type = [2]
elif (titleName == 'WaterLevel'):
variable_type = [100000]
unit = [1000] #1000 meter, 1001 km, 1002 mm
data_value_type = [0]
else:
variable_type = [100004]
unit = [1002] #1000 meter, 1001 km, 1002 mm
data_value_type = [2]
dfs = dfs0.dfs0()
dfs.create_non_equidistant_calendar(dfs0file=dfs0file,
data=newData1,
time_vector=time_vector,
names=names,
title=title,
variable_type=variable_type,
unit=unit,
data_value_type=data_value_type)
start_time = time_vector[0]
#1400 = 5s
#1401 = 5m
#1402 = 5h
#1403 =
#1404 = 5h
# timeseries_unit = 1403
# dt = 5
# dfs.create_equidistant_calendar(dfs0file=dfs0file_1, data=newData1,
# start_time=start_time,
# timeseries_unit=timeseries_unit, dt=dt,
# names=names, title=title,
# variable_type=variable_type,
# unit=unit, data_value_type=data_value_type)
assert True