def xPPMT(rate=0, per=0, nper=0, pv=0, fv=0, type1=0):
# 一直用等额本金 默认0
per = check_value(type(per))(per)
if per == 0:
return 0
rate = check_value(type(rate))(rate)
nper = check_value(type(nper))(nper)
pv = check_value(type(pv))(pv)
fv = check_value(type(fv))(fv)
try:
a = numpy.ppmt(rate, per, nper, pv, fv)
except:
a = 0
return a
def __init__(self, NN, sigma, km_filepath='./data/color_bins/pts_in_hull.npy', cc=-1):
if(util.check_value(cc, -1)):
self.cc = np.load(km_filepath)
else:
self.cc = cc
self.K = self.cc.shape[0]
self.NN = int(NN)
self.sigma = sigma
self.nbrs = nn.NearestNeighbors(n_neighbors=self.NN, algorithm='auto').fit(self.cc)
def __init__(self, NN, sigma, km_filepath='/shared/foss/interactive-deep-colorization/data/color_bins/pts_in_hull.npy', cc=-1):
if(util.check_value(cc, -1)):
self.cc = np.load(km_filepath)
else:
self.cc = cc
self.K = self.cc.shape[0]
self.NN = int(NN)
self.sigma = sigma
self.nbrs = nn.NearestNeighbors(n_neighbors=self.NN, algorithm='auto').fit(self.cc)
def go(filename, address, categories=None, walking_time=None, full_info=False):
'''
Process the raw data and run the whole program.
Inputs:
filename: (str) filename of the data
address:(str) address of user
categories: (list of strings) the list of attempted service types
walking_time: (int) the desired maximum walking time
full_info: (boolean) whether or not to return the full information
'''
try:
pd.options.display.max_colwidth = 650
df = pd.read_csv(filename, dtype=COL_TYPES, index_col=0)
except IOError:
print("Could not read file: {}".format(filename))
assert util.check_value(address, categories, walking_time, full_info) \
is True, 'Value Error!'
df, dict_of_geocode = get_nearest_spots(\
df, address, categories, walking_time, full_info)
map_url = map_plot(address, dict_of_geocode)
export_output(df, map_url, address)
def get(self, debugmode, FLAG = -9999):
"""
Get method that reads the weather reports off the web. In the LaSilla case, it download a `meteo.last` and interprets the data.
:param debugmode: whether or not POUET is in debugmode. If true, it ought to return some static and dummy data
:param FLAG: what to return in case the weather report cannot be downloaded or treated. Currently, POUET expect -9999 as a placeholder.
:return: Wind direction, speed, temperature and humidity
.. warning:: Such a method *must* return the following variables in that precise order: wind direction, wind speed, temperature and humidity
"""
#todo: add a "no connection" message if page is not reachable instead of an error
WS=[]
WD=[]
RH = None
Temps = []
error_msg = "Cannot download weather data. Either you or the weather server is offline!"
if debugmode:
fname = os.path.join(os.path.dirname(os.path.abspath(inspect.stack()[0][1])), "meteoDebugMode.last")
fi = open(fname, mode='r')
data = ""
with fi:
line = fi.read()
data += line
else:
try:
#data=urllib.request.urlopen(self.location.get("weather", "url")).read()
data = requests.get(self.config.get("weather", "url")).content
except requests.ConnectionError:
logger.warning(error_msg)
return FLAG, FLAG, FLAG, FLAG
data = data.decode("utf-8")
if "404 Not Found" in data:
logger.warning(error_msg)
return FLAG, FLAG, FLAG, FLAG
data=data.split("\n") # then split it into lines
for line in data:
if re.match( r'WD', line, re.M|re.I):
WD.append(int(line[20:25])) # AVG
if re.match( r'WS', line, re.M|re.I):
WS.append(float(line[20:25])) # AVG
if re.match( r'RH', line, re.M|re.I):
RH = float(line[20:25]) # AVG
if re.match( r'T ', line, re.M|re.I):
Temps.append(float(line[20:25])) # AVG
# Remove out-of-band readings
# WD is chosen between station 1 or 2 in EDP pour la Silla.
# We take average
Temps = np.asarray(Temps, dtype=np.float)
Temps = Temps[Temps < 100]
Temps = np.mean(Temps)
# Remove out-of-band readings
# WD is chosen between station 1 or 2 in EDP pour la Silla.
# We take average
WD = np.asarray(WD, dtype=np.float)
WD = WD[WD < 360]
WD = WD[WD > 0]
WD = np.mean(WD)
# WS should be either WS next to 3.6m or max
# Remove WS > 99 m/s
WS = np.asarray(WS, dtype=np.float)
if WS[2] < 99:
WS = WS[2]
else:
logger.warning("Wind speed from 3.6m unavailable, using other readings in LaSilla")
WS = np.asarray(WS, dtype=np.float)
WS = WS[WS > 0]
WS = WS[WS < 99]
WS = np.mean(WS)
for var in [WD, WS, Temps, RH]:
if not np.isnan(var):
var = -9999
WD = util.check_value(WD, FLAG)
WS = util.check_value(WS, FLAG)
Temps = util.check_value(Temps, FLAG)
RH = util.check_value(RH, FLAG)
return WD, WS, Temps, RH
def eng(self, value: str, sheet: str, old_value: str = "A1", count_func=0):
# 主要逻辑函数
# print(sheet + "\t" + old_value + "\t" + str(value) + "\t")
if isinstance(value, str) and value.startswith("="):
if sheet == "财报":
if self.end_time(old_value):
return 0
# 遇见返回行 解析当前坐标并替换函数
if "COLUMN()" in value:
r, c = ret_row_column(old_value)
value = value.replace("COLUMN()", str(c))
if "%" in value:
value = percentile_tu_float(value)
try:
val = self.format_parser(value, sheet)
self.set_temp_data(old_value, val, sheet)
self.set_date(old_value, val, sheet)
return val
except:
# print(sheet + "\t" + old_value + "\t" + str(value) + "\t")
pass
if old_value == "输出":
old_value = "A1"
reference = ""
source_r, source_c = ret_row_column(old_value)
if sheet != "财报" or (sheet == "财报" and
(source_c < 8 or
(source_c >= 8 and count_func != 0))):
# 解析函数
func = formulas.Parser().ast(value)[1].compile()
func_inputs = func.inputs
# 遇见偏移函数，解析偏移标示位
if "OFFSET" in func.outputs[0]:
str_o = func.outputs[0]
sps = str_o.split("OFFSET(")[1]
reference = sps.split(",")[0]
else:
count_func += 1
# 读取解析好的函数
func = self.func_dic[source_r]
# 判断是否是最后一个值
jianshe_time = self.get_temp_data("E230", "假设表")
end_time = jianshe_time + 240 + 12 + 7
source_c_char = row_list[source_c]
source_c_char_1 = row_list[source_c - 1]
source_c_char__1 = row_list[source_c + 1]
func_inputs = []
if source_c == end_time and (source_r in [
228, 254, 260, 265, 270, 275, 280, 287, 292, 297, 302
]):
func = self.func_dic["if_func"]
for i in func.inputs:
if i == "A1":
i = source_c_char + str(source_r - 2)
if i == "A2":
i = source_c_char + str(source_r - 1)
if i == "H18":
i = source_c_char + "18"
func_inputs.append(i)
elif source_r == 530 and source_c > 8:
func = self.func_dic["I530"]
func_inputs.append(source_c_char_1 + "530")
else:
func_outputs = func.outputs[0]
if source_r == 217:
for i in func.inputs:
if i == "H217":
func_inputs.append("H217:" + source_c_char_1 +
"217")
continue
new_input = list(i)
for i, v in enumerate(i):
if v == "I":
new_input[i] = source_c_char
func_inputs.append("".join(new_input))
if "OFFSET" in func_outputs:
str_o = func_outputs
sps = str_o.split("OFFSET(")[1]
reference = list(sps.split(",")[0])
for i, v in enumerate(reference):
if v == "I":
reference[i] = source_c_char
reference = "".join(reference)
else:
for i in func.inputs:
sums = self.ret_sum(source_r, i, source_c_char,
source_c_char_1,
source_c_char__1)
if sums:
func_inputs.append(sums)
continue
new_input = list(i)
for i, v in enumerate(i):
if v == "H":
new_input[i] = source_c_char
if v == "G":
new_input[i] = source_c_char_1
if v == "I":
new_input[i] = source_c_char__1
func_inputs.append("".join(new_input))
if "OFFSET" in func_outputs:
str_o = func_outputs
sps = str_o.split("OFFSET(")[1]
reference = list(sps.split(",")[0])
for i, v in enumerate(reference):
if v == "H":
reference[i] = source_c_char
if v == "G":
reference[i] = source_c_char_1
if v == "I":
reference[i] = source_c_char__1
reference = "".join(reference)
inputs = []
for input in func_inputs:
res = self.input_func(sheet, reference, input, old_value)
inputs.append(res)
val = self.eng(func(*inputs), sheet, old_value)
if isinstance(val, str) and val.startswith("="):
val = self.eng(val, sheet, old_value, count_func=count_func)
return val
else:
# print("写入")
# 假设表 其他机组类型为空时设置0
if not value and sheet != "假设表" or (sheet == "假设表" and
(old_value == "E19"
or old_value == "E20")):
value = 0
if str(value) == "#VALUE!" or str(value) == "#REF!" or str(value) == "0" or str(value) == "[[#VALUE!]]" \
or str(value) == "#DIV/0!":
value = 0
if (old_value == "H195" or old_value == "E247" or old_value == "E246") \
and sheet == "假设表" and value == "":
value = 0
if (old_value == "E41" or old_value == "E247" or old_value == "E246") \
and sheet == "假设表" and value == "":
value = 0
# print(sheet + "\t" + old_value + "\t" + str(value) + "\t")
value = check_value(type(value))(value)
self.set_temp_data(old_value, value, sheet)
self.set_date(old_value, value, sheet)
if "A1" == old_value:
self.save_all_data()
# if sum_count == (2 + save_count) * 10000:
# save_count += 2
# save_all_data()
return value
def input_func(self,
sheet=None,
reference=None,
input=None,
old_value=None):
# 返回input输入参数函数
if reference and input == reference:
# 遇见OFFSET时设置标志位跳过当前位
return reference
if "!" in input:
# 遇见表跳转解析 ’！‘
new_sheet = input.split("!")[0]
new_sheet = new_sheet.replace("'", "")
input = input.split("!")[1]
else:
# 赋值表名不变
new_sheet = sheet
if ":" in input:
sum_sss = self.get_temp_data(input, new_sheet)
if sum_sss == -999999:
# 创建循环
start = input.split(":")[0]
end = input.split(":")[1]
# 解析循环的开始和结束
start_column, start_row = get_row_column(start)
end_column, end_row = get_row_column(end, "")
start_column_list = get_list_row(start_column)
end_column_list = get_list_row(end_column)
sum_sss = []
for i in range(start_row, end_row + 1):
rows = []
for j in [
item for item in end_column_list
if not item in start_column_list[:-1]
]:
val = self.get_temp_data(j + str(i), new_sheet)
if val == -999999:
val = self.eng(self.get_date_value(
j + str(i), new_sheet),
new_sheet,
old_value=j + str(i))
if isinstance(val, str) and val.startswith("="):
val = self.eng(val, new_sheet, j + str(i))
val = check_value(type(val))(val)
rows.append(val)
# 添加循环出来的每一个值
self.set_temp_data(j + str(i), val, new_sheet)
self.set_date(j + str(i), val, new_sheet)
sum_sss.append(rows)
if len(sum_sss) == 1:
sum_sss = sum_sss[0]
self.set_temp_data(input, sum_sss, new_sheet)
sum_sss = np.array(sum_sss)
return sum_sss
else:
val = self.get_temp_data(input, new_sheet)
if val == -999999 or (isinstance(val, str)
and val.startswith("=")):
# 普通数据时直接去解析坐标的值
val = self.eng(self.get_date_value(input, new_sheet),
new_sheet, input)
if isinstance(val, str) and val.startswith("="):
val = self.eng(val, new_sheet, input)
val = check_value(type(val))(val)
self.set_temp_data(input, val, new_sheet)
self.set_date(input, val, new_sheet)
return val
def get(self, debugmode, FLAG=-9999):
"""
Get method that reads the weather reports off the web. In the LaSilla case, it download a `meteo.last` and interprets the data.
:param debugmode: whether or not POUET is in debugmode. If true, it ought to return some static and dummy data
:param FLAG: what to return in case the weather report cannot be downloaded or treated. Currently, POUET expect -9999 as a placeholder.
:return: Wind direction, speed, temperature and humidity
.. warning:: Such a method *must* return the following variables in that precise order: wind direction, wind speed, temperature and humidity
"""
#todo: add a "no connection" message if page is not reachable instead of an error
WS = []
WD = []
RH = None
Temps = []
error_msg = "Cannot download weather data. Either you or the weather server is offline!"
if debugmode:
fname = os.path.join(
os.path.dirname(os.path.abspath(inspect.stack()[0][1])),
"meteoDebugMode.last")
fi = open(fname, mode='r')
data = ""
with fi:
line = fi.read()
data += line
else:
try:
#data=urllib.request.urlopen(self.location.get("weather", "url")).read()
data = requests.get(self.config.get("weather", "url")).content
except requests.ConnectionError:
logger.warning(error_msg)
return FLAG, FLAG, FLAG, FLAG
data = data.decode("utf-8")
if "404 Not Found" in data:
logger.warning(error_msg)
return FLAG, FLAG, FLAG, FLAG
data = data.split("\n") # then split it into lines
for line in data:
if re.match(r'WD', line, re.M | re.I):
WD.append(int(line[20:25])) # AVG
if re.match(r'WS', line, re.M | re.I):
WS.append(float(line[20:25])) # AVG
if re.match(r'RH', line, re.M | re.I):
RH = float(line[20:25]) # AVG
if re.match(r'T ', line, re.M | re.I):
Temps.append(float(line[20:25])) # AVG
# Remove out-of-band readings
# WD is chosen between station 1 or 2 in EDP pour la Silla.
# We take average
Temps = np.asarray(Temps, dtype=np.float)
Temps = Temps[Temps < 100]
Temps = np.mean(Temps)
# Remove out-of-band readings
# WD is chosen between station 1 or 2 in EDP pour la Silla.
# We take average
WD = np.asarray(WD, dtype=np.float)
WD = WD[WD < 360]
WD = WD[WD > 0]
WD = np.mean(WD)
# WS should be either WS next to 3.6m or max
# Remove WS > 99 m/s
WS = np.asarray(WS, dtype=np.float)
if WS[2] < 99:
WS = WS[2]
else:
logger.warning(
"Wind speed from 3.6m unavailable, using other readings in LaSilla"
)
WS = np.asarray(WS, dtype=np.float)
WS = WS[WS > 0]
WS = WS[WS < 99]
WS = np.mean(WS)
for var in [WD, WS, Temps, RH]:
if not np.isnan(var):
var = -9999
WD = util.check_value(WD, FLAG)
WS = util.check_value(WS, FLAG)
Temps = util.check_value(Temps, FLAG)
RH = util.check_value(RH, FLAG)
return WD, WS, Temps, RH