def test_repr():
detector = src.detector("Name", 2, 4)
assert (
detector.__repr__() ==
"<File Name, Period: 2, n_Cycles: 4, num_procs: 1, filter_detectable: False, filter_zero: False>"
)
def test_filter_zero():
cwd = os.getcwd()
cwd = cwd.split("RPx")[0]
detector = src.detector(cwd + "RPx/datasets/young_values.txt",
1,
2,
filter_zero=True)
df = detector.read_file(delim="tab")
df = detector.filter_zero(df)
assert df["zero"].mean() == 1
def test_calculate_p_value_5_processes():
cwd = os.getcwd()
cwd = cwd.split("RPx")[0]
detector = src.detector(
cwd + "RPx/datasets/young_values.txt",
1,
2,
filter_detectable=True,
filter_zero=True,
num_permutations=10,
num_procs=5,
)
df = detector.read_file(delim="tab")
df["RPx"] = df.iloc[:, 1 : (detector.config["LEN_SIGNALS"] + 1)].apply(
detector.compute_rpx, axis=1
)
indexes = list(range(1, detector.config["LEN_SIGNALS"] + 1)) + [
len(df.columns.tolist()) - 1
]
start = time.time()
df["p_values"] = df.iloc[:, indexes].apply(detector.compute_p_value, axis=1)
end = time.time()
print(end - start)
p_values = df.head(10)["p_values"].tolist()
for i in range(len(p_values)):
if isinstance(p_values[i], float):
p_values[i] = 0.0
assert np.array_equal(
p_values,
[
"NOTEST",
"NOTEST",
"NOTEST",
"NOTEST",
0.0,
"NOTEST",
"NOTEST",
0.0,
"NOTEST",
0.0,
],
)
def test_detect():
cwd = os.getcwd()
cwd = cwd.split("RPx")[0]
detector = src.detector(
cwd + "RPx/datasets/young_values.txt",
1,
2,
filter_detectable=True,
filter_zero=True,
num_permutations=10,
)
df = detector.read_file(delim="tab")
df = detector.detect(df)
def test_read_csv():
cwd = os.getcwd()
cwd = cwd.split("RPx")[0]
detector = src.detector(cwd + "RPx/datasets/young_values.txt", 1, 2)
df = detector.read_file(delim="tab")
assert df.columns.tolist() == [
"symbol",
"ZT0_R1",
"ZT4_R1",
"ZT8_R1",
"ZT12_R1",
"ZT16_R1",
"ZT20_R1",
"ZT0_R2",
"ZT4_R2",
"ZT8_R2",
"ZT12_R2",
"ZT16_R2",
"ZT20_R2",
"detectable",
"zero",
]
assert np.array_equal(
df.iloc[1, :].values.tolist(),
[
"CG2678",
2.2014400000000003,
2.30626,
2.6552700000000002,
2.65123,
1.6633099999999998,
1.96986,
2.2431,
1.67356,
3.18127,
2.0783,
2.9474299999999998,
2.12705,
1,
1,
],
)
def test_calculate_rpx():
cwd = os.getcwd()
cwd = cwd.split("RPx")[0]
detector = src.detector(cwd + "RPx/datasets/young_values.txt",
1,
2,
filter_detectable=True)
df = detector.read_file(delim="tab")
df = detector.filter_detectable(df)
df = detector.filter_zero(df)
df["RPx"] = df.iloc[:, 1:(detector.config["LEN_SIGNALS"] + 1)].apply(
detector.compute_rpx, axis=1)
RPxs = [round(x, 4) for x in df.head(5)["RPx"].tolist()]
assert np.array_equal(RPxs, [-1.9083, -2.4264, -1.9803, -1.3145, 0.7344])
def test_calculate_mean():
cwd = os.getcwd()
cwd = cwd.split("RPx")[0]
detector = src.detector(
cwd + "RPx/datasets/young_values.txt",
1,
2,
filter_detectable=True,
filter_zero=True,
)
df = detector.read_file(delim="tab")
df["mean"] = df.iloc[:, 1 : (detector.config["LEN_SIGNALS"] + 1)].apply(
np.mean, axis=1
)
means = [round(x, 4) for x in df.head(5)["mean"].tolist()]
assert np.array_equal(means, [2.3082, 16.4625, 12.9011, 261.8394, 121.8818])
def test_init():
assert src.detector
detector = src.detector("Name", 2, 4)
assert detector.config["FILE_NAME"] == "Name"
assert detector.config["PERIOD"] == 2
assert detector.config["N_CYCLES"] == 4
default_config = {
"EPS": 1e-5,
"STAGGER": False,
"USE_Z_SCORE": False,
"NUM_PERMUTATIONS": 5000,
"NUM_PROCS": 1,
"SHUFFLE_WITH_REPLACEMENT": False,
"MIN_RP24": 0.0,
"FILTER_DETECTABLE": False,
"FILTER_ZERO": False,
"LEN_SIGNALS": None,
}
for param in default_config:
assert detector.config[param] == default_config[param]
cfg.getint('Detector', 'cfg_size_height'))
classes_file = cfg.get('Detector', 'classes_file')
iou_thresh = cfg.getfloat('Detector', 'iou_thresh')
conf_thresh = cfg.getfloat('Detector', 'conf_thresh')
nms_thresh = cfg.getfloat('Detector', 'nms_thresh')
use_gpu = cfg.getboolean('Detector', 'use_gpu')
# tracker configs
draw_tracks = cfg.getboolean('Tracker', 'draw_tracks')
export_data = cfg.getboolean('Tracker', 'export_data')
# Initializing the Detector
detectNet = detector(weights,
config,
conf_thresh=conf_thresh,
netsize=cfg_size,
nms_thresh=nms_thresh,
gpu=use_gpu,
classes_file=classes_file)
print('Network initialized!')
###################################################################
fps, duration = 0, 0
frame_num = 0
classes = None
vid_out = None
video = None
frameA = None
Tracking = None
num_det = 0
"""