def show_school(userdata):
data_list = load_data.load_data('school')
for i in data_list:
print('-------------- 学校 ----------------')
print('学校名称:%s'%i.school_name)
print('学校校区:%s'%i.campus)
print('------------------------------------')
def show_teacher(userdata):
data_list = load_data.load_data('teacher')
for i in data_list:
print('-------------- 讲师 ----------------')
print('姓名:%s'%i.teacher_name)
print('所属校区:%s'%i.schoolobj.campus)
print('------------------------------------')
def show_classes(userdata):
data_list = load_data.load_data('classes')
for i in data_list:
print('-------------- 班级 ----------------')
print('班级名称:%s'%i.classes_name)
print('课程名称:%s'%i.courseobj.course_name)
print('讲师名称:%s'%i.teacherobj.teacher_name)
print('------------------------------------')
def show_course(userdata):
data_list = load_data.load_data('course')
for i in data_list:
print('-------------- 课程 ----------------')
print('课程名:%s'%i.course_name)
print('周期:%s'%i.cycle)
print('学费:%s'%i.price)
print('------------------------------------')
def show_teacher():
data_list = load_data.load_data('teacher')
for i in data_list:
print('-------------- 讲师 ----------------')
print('id:%s'%i.id)
print('姓名:%s'%i.teacher_name)
print('工资:%s'%i.salary)
print('所属校区:%s'%i.schoolobj.campus)
print('------------------------------------')
def show_student():
data_list = load_data.load_data('student')
for i in data_list:
print('-------------- 学生 ----------------')
print('id:%s'%i.id)
print('学生姓名:%s'%i.student_name)
print('所属校区:%s'%i.schoolobj.school_name)
print('所学课程:%s'%i.courseobj.course_name)
print('课程成绩:%s'%i.score)
print('------------------------------------')
def check_your_student(userdata):
student_list = []
data_list = load_data.load_data('student')
if data_list:
for i in data_list:
if userdata.classesobj.classes_name == i.classesobj.classes_name:
student_list.append(i)
else:
print('\033[33;1m目前暂无学员前来报名或者管理员还未及时更新数据，请耐心等待\033[0m')
for index,i in enumerate(student_list):
print('编号 %s 学员姓名 %s - 年龄 %s - 所在班级 %s - \
课程成绩 %s'%(index+1,i.student_name,i.age,i.classesobj.classes_name,i.score))
return student_list
def auth(identity, username, password):
data = load_data.load_data(identity)
if identity == 'admin':
for i in data:
if username == i.admin_name and password == i.password:
return i
elif identity == 'student':
for i in data:
if username == i.student_name and password == i.password:
return i
elif identity == 'teacher':
for i in data:
if username == i.teacher_name and password == i.password:
return i
def update_school_classes_course(admindata):
keystype = ('school', 'course', 'classes', 'teacher')
inp = input('请输入待修改的类型: [school/course/classes/teacher] ').strip()
while inp not in keystype:
if inp in ('q', 'Q', 'quit', 'exit', 'e', 'E'): break
print('\033[31;1m操作异常，您的账号权限只可以修改学校、课程、班级、讲师的数据\033[0m')
inp = input('请输入待修改的类型: [school/course/classes/teacher] ').strip()
else:
data = load_data.load_data(inp) #还未修改的数据
try:
main.update_data(inp)
admin_log.info('%s update %s’ data' % (admindata.admin_name, inp))
except Exception as e:
print('\033[31;1m修改数据中途发生异常\033[0m')
print(e)
# 原子型操作，回滚到初始状态
load_data.rollback_data(inp, data)
def run_preproc_data(rname, struct):
# Checking there is no missing keyword in configuration structure
misc.check_configuration(struct)
# Defining output filenames
outhdf5 = "../preproc_data/" + rname + ".hdf5"
outpdf = "../preproc_data/" + rname + ".pdf"
# Creating output directories if they do not exist
if not os.path.exists("../preproc_data"):
os.mkdir("../preproc_data")
if os.path.exists(outhdf5):
os.remove(outhdf5)
if os.path.exists(outpdf):
os.remove(outpdf)
# Printing some basic info
print("--------------------------------------------")
print("- Input run name: " + rname)
print("- Survey: " + struct['instrument'])
print("- Wavelength range: " + str(struct['lmin']) + "-" +
str(struct['lmax']))
print("- Target SNR: " + str(struct['snr']))
print("- Min SNR: " + str(struct['snr_min']))
print("- Redshift: " + str(struct['redshift']))
print("- Velscale: " + str(struct['velscale']))
print("- LOSVD Vmax: " + str(struct['vmax']))
print("- Mask file: " + str(struct['mask_file']))
print("- Pol. order: " + str(struct['porder']))
print("- Templates: " + str(struct['template_lib']))
print("- Number of PCA: " + str(struct['npca']))
print("--------------------------------------------")
print("")
# Processing data
print("# Processing data .....")
data_struct = load_data(struct)
# Processing templates
print("# Processing templates .....")
temp_struct = load_templates(struct, data_struct)
# Creating the LOSVD velocity vector
print("")
print("# Creating the LOSVD velocity vector .....")
print("")
xvel, nvel = misc.create_xvel_vector(struct)
# Padding the templates
print("# Padding the templates for convolution .....")
temp_struct_padded = misc.pad_templates(temp_struct, nvel)
# Saving preprocessed information
print("")
print("# Saving preproc data: " + outhdf5)
print("")
f = h5py.File(outhdf5, "w")
#------------
f.create_dataset("in/xvel", data=xvel)
f.create_dataset("in/nvel", data=nvel)
#------------
for key, val in data_struct.items():
if (np.size(val) < 2):
f.create_dataset("in/" + key, data=val)
else:
f.create_dataset("in/" + key, data=val, compression="gzip")
#------------
for key, val in temp_struct_padded.items():
if (np.size(val) < 2):
f.create_dataset("in/" + key, data=val)
else:
f.create_dataset("in/" + key, data=val, compression="gzip")
#------------
f.close()
# Saving a simple plot with some basic figures about the pre-processed data
print("# Plotting some basic info in " + outpdf)
pdf_pages = PdfPages(outpdf)
if data_struct['ndim'] == 2:
# Bin map -----------
fig = plt.figure(figsize=(10, 7))
plt.subplots_adjust(left=0.10,
bottom=0.10,
right=0.98,
top=0.925,
wspace=0.0,
hspace=0.3)
ax0 = plt.subplot2grid((1, 1), (0, 0))
ax0.set_aspect('equal')
ax0.set_title("BinID map")
ax0.plot(data_struct['xbin'], data_struct['ybin'], '+', color='gray')
for i in range(np.amax(data_struct['binID']) + 1):
ax0.text(data_struct['xbin'][i],
data_struct['ybin'][i],
i,
fontsize=5,
horizontalalignment='left',
verticalalignment='center')
pdf_pages.savefig(fig)
plt.close()
# Input central spectra (2D case) or first spectra (1D case) including mask and PCA templates
fig = plt.figure(figsize=(10, 7))
plt.subplots_adjust(left=0.10,
bottom=0.10,
right=0.98,
top=0.925,
wspace=0.0,
hspace=0.0)
ax1 = plt.subplot2grid((3, 1), (0, 0))
ax1.plot(np.exp(data_struct['wave_obs']), data_struct['spec_obs'][:, 0],
'k')
if data_struct['ndim'] == 1:
ax1.set_ylabel("First spec")
else:
ax1.set_ylabel("Central spec")
ax1.set_xlim([
np.exp(temp_struct['lwave_temp'])[0],
np.exp(temp_struct['lwave_temp'])[-1]
])
ax1.axvline(x=np.exp(data_struct['wave_obs'][data_struct['mask'][0]]),
color='k',
linestyle=":")
ax1.axvline(x=np.exp(data_struct['wave_obs'][data_struct['mask'][-1]]),
color='k',
linestyle=":")
w = np.flatnonzero(np.diff(data_struct['mask']) > 1)
if w.size > 0:
for wj in w:
l0 = np.exp(data_struct['wave_obs'][data_struct['mask'][wj]])
l1 = np.exp(data_struct['wave_obs'][data_struct['mask'][wj + 1]])
ax1.axvspan(l0, l1, alpha=0.5, color='red')
ax2 = plt.subplot2grid((3, 1), (1, 0))
ax2.plot(np.exp(temp_struct['lwave_temp']), temp_struct['mean_template'],
'k')
ax2.set_ylabel("Mean Template")
ax2.set_xlim([
np.exp(temp_struct['lwave_temp'])[0],
np.exp(temp_struct['lwave_temp'])[-1]
])
ax3 = plt.subplot2grid((3, 1), (2, 0))
ax3.plot(np.exp(temp_struct['lwave_temp']), temp_struct['templates'])
ax3.set_ylabel("PCA Templates")
ax3.set_xlabel("Restframe wavelength ($\\mathrm{\\AA}$)")
ax3.set_xlim([
np.exp(temp_struct['lwave_temp'])[0],
np.exp(temp_struct['lwave_temp'])[-1]
])
pdf_pages.savefig(fig)
pdf_pages.close()
plt.close()
misc.printDONE(rname)
return
# max length of the n-grams
parser.add_argument('-g', default=5, type=int, help='n-gram max length (default = 6)')
# stop word file
parser.add_argument('-w', default=None, help='stop word directory (default: None)')
# umls directory
parser.add_argument('-u', default=None, help='umls directory (default: None)')
# negation rules
parser.add_argument('-r', default=None, help='negation rule file (default: None)')
# number of processers to use
parser.add_argument('-c', default=1, type=int, help='number of processors (default: 1)')
return parser.parse_args(sys.argv[1:])
if __name__ == '__main__' :
# param
args = _process_args()
# load data
tags = ufile.read_csv (args.fvocab)
log.info ('loaded %d eligibility tags' % len(tags))
cvocab = set()
for t in tags:
cvocab.add (t[0])
(stop, umls, ptag, negrule) = load_data (args.w, args.u, None, args.r)
# exec
nctec_indexing (cvocab, args.o, args.g, stop, umls, negrule, args.c)
print ''
log.info ('task completed\n')
