def test_examples(self):
"""Apply algorithm and check expected results."""
example_results = dict(zip(self.df.columns, [40, 79, 41]))
for column, expected in example_results.items():
cycles = detect_cycles(self.df[column])
result = cycles.sum().sum() # sum along column sums (kind of ID)
msg = ('Test for example ' + column +
' failed. Expected checksum ' + str(expected) + ', is ' +
str(result) + '.' + str(cycles))
eq_(result, expected, msg)
def test_example(self):
"""Apply algorithm and check expected results."""
cycles = detect_cycles(self.ds)
# using an input series with DatetimeIndex returns the timestamps
# for start, end and minimum as well as a timedelta as duration
t0 = datetime.strptime('2019-01-02', '%Y-%m-%d')
t1 = datetime.strptime('2019-01-06', '%Y-%m-%d')
t2 = datetime.strptime('2019-01-10', '%Y-%m-%d')
tmin1 = datetime.strptime('2019-01-04', '%Y-%m-%d')
tmin2 = datetime.strptime('2019-01-08', '%Y-%m-%d')
expected = pd.DataFrame(
[[t0, t1, tmin1, 1.0, t1 - t0], [t1, t2, tmin2, 1.0, t2 - t1]],
columns=['t_start', 't_end', 't_minimum', 'doc', 'duration'])
assert_frame_equal(cycles, expected)
def test_ValueError_length(self):
"""Invoke ValueErrors."""
detect_cycles(pd.Series([0, 1, 0]))
def test_ValueError_no_cycle(self):
"""Invoke ValueErrors."""
detect_cycles(pd.Series([0, 1, 2, 3, 2, 1, 0]))
def test_TypeError(self):
"""Invoke TypeErrors."""
detect_cycles(self.df['nones'])
def test_ValueError_no_valley(self):
"""Invoke ValueErrors."""
detect_cycles(self.df['no_valley'])
def test_ValueError_no_peak(self):
"""Invoke ValueErrors."""
detect_cycles(self.df['no_peak'])
)
# Cylces ---------------------------------------------------------
cycles = {}
cycles_h = {}
cycles_l = {}
for dir in os.listdir(path):
df = pd.read_csv(
os.path.join(path, dir, "output", "filling_levels.csv"),
index_col=[0],
parse_dates=True,
)
df["phs"] = df[[c for c in df.columns if "phs" in c]].sum(axis=1)
if sum(df["battery"] != 0):
cycles_l[dir] = detect_cycles(df["battery"])
if sum(df["phs"] != 0):
cycles_h[dir] = detect_cycles(df["phs"])
for v in base_scenarios:
if v in cycles_l:
ax = sns.jointplot(
x=cycles_l[v]["duration"] / np.timedelta64(1, "h"),
y=cycles_l[v]["doc"],
marginal_kws=dict(bins=100, rug=True),
kind="scatter",
color="m",
edgecolor="purple",
)
ax.set_axis_labels("Duration of cycle in h",
"Normalised depth of cycle")
# filling levels --------------------------------------------------------------
cycles = {}
cycles_h = {}
cycles_l = {}
for dir in os.listdir(path):
if dir in base_scenarios:
df = pd.read_csv(
os.path.join(path, dir, "output", "filling_levels.csv"),
index_col=[0],
parse_dates=True,
)
if not "flex" in dir:
try:
cycles[dir] = detect_cycles(df["DE-flex-decentral_heat-tes"])
except:
pass
for dir in os.listdir(path):
df = pd.read_csv(
os.path.join(path, dir, "output", "filling_levels.csv"),
index_col=[0],
parse_dates=True,
)
if sum(df["DE-hydrogen-storage"] != 0):
cycles_h[dir] = detect_cycles(df["DE-hydrogen-storage"])
if sum(df["DE-lithium-battery"] != 0):
cycles_l[dir] = detect_cycles(df["DE-lithium-battery"])
for v in base_scenarios: