def test_array_of_objects():
o = {"foo": [{"bar": "baz"}]}
assert blueprint(o) == O({
".foo": "array",
".foo[]": "object",
".foo[].bar": "string"
})
def draw_trend():
# plot style
style = np.random.choice(plt.style.available); print(style); plt.style.use('grayscale') # [u'dark_background', u'bmh', u'grayscale', u'ggplot', u'fivethirtyeight']
# plot setting
mpl.rcParams['mathtext.fontset'] = 'stix'
mpl.rcParams['font.family'] = 'STIXGeneral'
mpl.rcParams['legend.fontsize'] = 11
# mpl.rcParams['legend.family'] = 'Times New Roman'
mpl.rcParams['font.family'] = ['Times New Roman']
mpl.rcParams['font.size'] = 11.0
# mpl.style.use('classic')
font = {'family' : 'Times New Roman', #'serif',
'color' : 'darkblue',
'weight' : 'normal',
'size' : 11,}
textfont = {'family' : 'Times New Roman', #'serif',
'color' : 'darkblue',
'weight' : 'normal',
'size' : 9,}
df_profiles = pd.read_csv(r"./algorithm.dat", na_values = ['1.#QNAN', '-1#INF00', '-1#IND00'])
df_info = pd.read_csv(r"./info.dat", na_values = ['1.#QNAN', '-1#INF00', '-1#IND00'])
# df_profiles["rpm_cmd"]=df_profiles["ACM.rpm_cmd"]
# df_profiles["rpm_mes"]=df_profiles["ACM.rpm_cmd"]-df_profiles["e_omega"]
#print(df_profiles.keys())
no_samples = df_profiles.shape[0]
no_traces = df_profiles.shape[1]
print(df_info, 'Simulated time: %g s.'%(no_samples * df_info['TS'].values[0] * df_info['DOWN_SAMPLE'].values[0]), 'Key list:', sep='\n')
for key in df_profiles.keys():
print('\t', key)
time = np.arange(1, no_samples+1) * df_info['DOWN_SAMPLE'].values[0] * df_info['TS'].values[0]
ax_list = []
for i in range(0, no_traces, 6):
ax_list += list(get_axis((1,6)))
for idx, key in enumerate(df_profiles.keys()):
plot_it(ax_list[idx], key, O([
(str(idx), df_profiles[key]),
# (str(idx), df_profiles[key]),
]),time,font)
#print(idx,key)
# ax_list[12].plot(time,df_profiles["rpm_mes"],color="red")
plt.show()
ax.plot(time, v, '-', lw=1)
else:
# ax.plot(time, v, '-', lw=2, label=k)
ax.plot(time, v, '-', lw=1)
# ax.legend(loc='lower right', shadow=True)
# ax.legend(bbox_to_anchor=(1.08,0.5), borderaxespad=0., loc='center', shadow=True)
ax.set_ylabel(ylabel, fontdict=font)
# ax.set_xlim(0,35) # shared x
# ax.set_ylim(0.85,1.45)
time = np.arange(
1, no_samples +
1) * df_info['DOWN_SAMPLE'].values[0] * df_info['TS'].values[0]
ax_list = []
for i in range(0, no_traces, 5):
ax_list += list(get_axis((1, 5)))
for idx, key in enumerate(df_profiles.keys()):
plot_it(
ax_list[idx],
key,
O([
(str(idx), df_profiles[key]),
# (str(idx), df_profiles[key]),
]))
plt.show()
quit()
print('\t', key)
# print all key of the data ,which is the first row string in data file
# the value of key is corresponding list
time = np.arange(1, no_samples+1) * df_info['WRITE_PER_TIME'].values[0]
ax_list = []
# for i in range(0, no_traces, 2): # for i in range(1, 101，1) print from 1 to 101
# ax_list += list(get_axis((1,2))) # add axis list to ax_list
for i in range(0, no_traces): # for i in range(1, 101，1) print from 1 to 101
ax_list.append(get_axis())
#ax_list.append(get_axis(1,1)) # add axis list to ax_list
for ID_x, key in enumerate(df_profiles.keys()): # enumerate() is utilized to give num for every key
plot_it(ax_list[ID_x], key, O([
(str(ID_x), df_profiles[key]),
# (str(idx), df_profiles[key]),
]), time)
# input("Wait for key")
# time.sleep(0.05) # wait for 0.05 second
# fig.savefig('figure_name.png') must be placed before plt.show
#fig.savefig('%s.tif',ylabel)
# the final direction in the script to creat the defined figure
plt.show()
# do some stuff in script
variable = input('input something!: ')
quit()
def test_string():
assert blueprint({"foo": "bar"}) == O({".foo": "string"})
def test_array():
o = {"foo": [1, 2, 3]}
assert blueprint(o) == O({".foo": "array", ".foo[]": "number"})
def test_object():
o = {"foo": {"bar": "baz"}}
assert blueprint(o) == O({".foo": "object", ".foo.bar": "string"})
def test_boolean():
assert blueprint({"foo": True}) == O({".foo": "boolean"})
def test_number():
assert blueprint({"foo": 1}) == O({".foo": "number"})
assert blueprint({"foo": 1.1}) == O({".foo": "number"})