def test_random(self):
"""
python -m unittest tests.test_numpy.NumpyTest.test_random
:return:
"""
v: np.array = np.random.uniform(0., 1000., 5)
coutln(v)
def test_zeros(self):
"""
Learn numpy.zeros()
python -m unittest tests.test_numpy.NumpyTest.test_zeros
"""
v: np.ndarray = np.zeros((5, ), dtype=float, order='C')
coutln(v)
def test_matrix(self):
"""
python -m unittest tests.test_numpy.NumpyTest.test_matrix
:return:
"""
m = np.zeros((2, 2))
m[1, 1] += 5.
coutln(m)
def test_datetime(self):
"""
python -m unittest tests.test_datetime.DatetimeTest.test_datetime
:return:
"""
now = datetime.datetime.now()
coutln(str(now))
coutln(now.strftime("%d/%m/%Y %I:%M:%S %p"))
coutln(now.strftime("%-d/%-m/%Y %I:%M:%S %p"))
def test_searchsorted(self):
"""
python -m unittest tests.test_numpy.NumpyTest.test_searchsorted
:return:
"""
a: np.array = np.linspace(0., 1000., 9)
coutln(a)
v: np.array = np.random.uniform(0., 1000., 10)
coutln(v)
i: np.array = np.searchsorted(a, v)
coutln(i)
def test_linspace(self):
"""
Learn numpy.linspace()
python -m unittest tests.test_numpy.NumpyTest.test_linspace
:return:
"""
v: np.ndarray = np.linspace(0., 1000., 5)
coutln('>>> np.linspace(0., 1000., 5)')
coutln(v)
coutln(v.dtype)
def test_arange(self):
"""
Learn numpy.arange()
python -m unittest tests.test_numpy.NumpyTest.test_arange
"""
v: np.ndarray = np.arange(10)
coutln('>>> np.arange(10)')
coutln(v)
coutln('>>> v.dtype')
coutln(v.dtype)
# 1 dim array
self.assertEqual(1, v.ndim)
# shape = (10,)
self.assertEqual(1, len(v.shape))
self.assertEqual(10, v.shape[0])
self.assertEqual(10, v.size)
l: list = list(range(0, 10))
self.assertTrue(np.array_equal(l, v))
def test_scatter_demo2(self):
"""
https://matplotlib.org/gallery/lines_bars_and_markers/scatter_demo2.html#sphx-glr-gallery-lines-bars-and-markers-scatter-demo2-py
python -m unittest tests.test_matplotlib.MatplotlibTest.test_scatter_demo2
:return:
"""
with cbook.get_sample_data('goog.npz') as datafile:
# coutln(datafile)
data: np.lib.npyio.NpzFile = np.load(datafile)
price_data: np.recarray = data['price_data'].view(np.recarray)
# coutln('>>> price_data')
# coutln(price_data)
coutln('>>> type(price_data)')
coutln(type(price_data))
coutln('>>> price_data.dtype')
coutln(price_data.dtype)
# coutln(price_data.view(np.recarray))
# coutln('>>> price_data.size')
# coutln(price_data.size)
# for i in data.keys():
# coutln(i)
price_data = price_data[-250:]
# coutln('>>> price_data')
# coutln(price_data)
coutln(">>> price_data.adj_close.size")
coutln(price_data.adj_close.size)
# price_data.adj_close[:-1]: first to second to the last
delta1: np.ndarray = np.diff(
price_data.adj_close) / price_data.adj_close[:-1]
coutln(">>> delta1.size")
coutln(delta1.size)
volume = (15 * price_data.volume[:-2] / price_data.volume[0])**2
coutln('volume.size: {}'.format(volume.size))
close = 0.003 * price_data.close[:-2] / 0.003 * price_data.open[:-2]
fig: matplotlib.figure.Figure
ax: matplotlib.axes.Axes
fig, ax = plt.subplots()
ax.scatter(delta1[:-1], delta1[1:], c=close, s=volume, alpha=0.5)
ax.set_xlabel(r'$\Delta_i$', fontsize=15)
ax.set_ylabel(r'$\Delta_{i+1}$', fontsize=15)
ax.set_title('Volume and percent change')
ax.grid(True)
fig.tight_layout()
plt.show()