def localGDB(): #
global local
fields = [
'OBJECTID',
'lon',
'lat',
'city_id',
'temp',
'forecast_date2',
'temp_min',
'temp_max', # объявление полей для чтения
'pressure',
'pressure_s_lvl',
'pressure_g_lvl',
'wind_speed',
'wind_degree', # объявление полей для чтения
'clouds',
'weather_description',
'humidity',
'request_date',
'forecast_date', # объявление полей для чтения
'rain',
'snow',
'name'
] # объявление полей для чтения
fc_np = da.FeatureClassToNumPyArray(
weatherinput, fields, skip_nulls=False, null_value=0
) # запрос данных из таблицы в нампай массив #where_clause=exp,
localdf = DataFrame(fc_np) # конвертация массива в датафрейм
localdf.drop_duplicates(subset='forecast_date2', keep='last', inplace=True)
print(localdf.shape) # избавление датафрейма от дубликатов
local = localdf
print(local)
return local
def unique_values(table, field):
"""gets a list of unique values from a table's column
table: path to a table
field: string of a field name
output:
list
"""
try:
if has_pandas:
uvalues = None
chunk_size = calc_chunk_size()
with da.SearchCursor(table, [field]) as cursor:
for group in grouper_it(chunk_size, cursor):
df = pd.DataFrame.from_records(group,
columns=cursor.fields)
column = df[field].unique()
if uvalues is None:
uvalues = column
else:
uvalues = np.concatenate([column, uvalues])
del group
del df
del column
del cursor
if uvalues is None:
return []
return list(set(uvalues.tolist()))
else:
desc = arcpy.Describe(table)
if desc.hasOID:
oidFieldname = desc.OIDFieldName
else:
raise Exception("Table must have an object id table")
template = da.FeatureClassToNumPyArray(
table, [field],
where_clause="{ofield} < 1".format(ofield=oidFieldname))
uvalues = None
chunk_size = calc_chunk_size()
with da.SearchCursor(table, [field]) as cursor:
for group in grouper_it(chunk_size, cursor):
df = np.fromiter(group, template.dtype, -1)
column = np.unique(df[field])
if uvalues is None:
uvalues = column
else:
uvalues = np.unique(np.concatenate([column, uvalues]))
if uvalues is None:
return []
return list(set(uvalues.tolist()))
except:
line, filename, synerror = trace()
raise FunctionError({
"function": "unique_values",
"line": line,
"filename": __file__,
"synerror": synerror,
"arc": str(arcpy.GetMessages(2))
})
def featureToCSV(inFeature, inColumns, outCSV):
arcpy.AddMessage("Converting feature class to csv...")
feature_array = da.FeatureClassToNumPyArray(inFeature,
["SHAPE@XY"] + inColumns)
feature_df = pd.DataFrame(feature_array, columns=inColumns)
feature_df.to_csv(outCSV,
header=True,
index=False,
sep=',',
encoding='utf-8')
def las_tile_to_numpy_pandas(lidar_tile, sr, returns, class_codes,
format_for_library):
temp_lasd = "{0}_temp.lasd".format(splitext(lidar_tile)[0])
if Exists(temp_lasd):
Delete(temp_lasd)
arcpy.CreateLasDataset_management(lidar_tile,
temp_lasd,
spatial_reference=sr)
point_spacing = arcpy.Describe(temp_lasd).pointSpacing
Delete(temp_lasd)
temp_pts_multi = join("in_memory", "temp_pts_multi")
if Exists(temp_pts_multi):
Delete(temp_pts_multi)
LASToMultipoint(input=lidar_tile,
out_feature_class=temp_pts_multi,
average_point_spacing=point_spacing,
class_code=class_codes,
_return=returns,
input_coordinate_system=sr)
if format_for_library == "numpy":
lidar_points = da.FeatureClassToNumPyArray(
in_table=temp_pts_multi,
# field_names=["OID@", "SHAPE@X", "SHAPE@Y", "SHAPE@Z"],
field_names=["SHAPE@X", "SHAPE@Y", "SHAPE@Z"],
# field_names=["SHAPE@XYZ"],
spatial_reference=sr,
explode_to_points=True)
Delete(temp_pts_multi)
# Numpy Processing Operation Goes Here!
numpy_operation_here(lidar_points)
elif format_for_library == "pandas":
lidar_points = pd.DataFrame.spatial.from_featureclass(
location=temp_pts_multi)
#fields=["SHAPE@X", "SHAPE@Y", "SHAPE@Z"])
Delete(temp_pts_multi)
# Numpy Processing Operation Goes Here!
pandas_operation_here(lidar_points)
del lidar_points
def get_change_detection_statistics(self, fc):
"""calculates the change detection statistic"""
try:
#FeatureToNumPyArray
sum_array = da.FeatureClassToNumPyArray(fc,
self.year_list,
skip_nulls=True)
arcpy.AddMessage("Creating CD SUM array")
change_detection_sum = []
for i in range(0, len(sum_array[self.year_list[0]])):
results = 0
for year in self.year_list:
results = sum_array[i][year]+ results
change_detection_sum.append(results)
arcpy.AddMessage('Using default SUM statistics')
self.minus2Sigma = np.percentile(change_detection_sum,
self.MINUS_2_SIGMA)
self.minus1Sigma = np.percentile(change_detection_sum,
self.MINUS_1_SIGMA)
self.median = np.median(change_detection_sum)
self.plus1Sigma = np.percentile(change_detection_sum,
self.PLUS_1_SIGMA)
self.plus2Sigma = np.percentile(change_detection_sum,
self.PLUS_2_SIGMA)
arcpy.AddMessage('Minus 2 Sigma = ' + str(self.minus2Sigma))
arcpy.AddMessage('Minus 1 Sigma = ' + str(self.minus1Sigma))
arcpy.AddMessage('Median = ' + str(self.median))
arcpy.AddMessage('Plus 1 Sigma = ' + str(self.plus1Sigma))
arcpy.AddMessage('Plus 2 Sigma = ' + str(self.plus2Sigma))
except:
line, filename, synerror = trace()
raise FunctionError({
"function": "get_change_detection_statistics",
"line": line,
"filename": __file__,
"synerror": synerror,
"arc" : str(arcpy.GetMessages(2))
})
#rename the datasets
inputFC = r'USA_Train'
globalFC = r'EMU_Global_90m_FillMissingVa'
#group the variables
predictVars = [
'DISS02', 'NITRATE', 'PHOSPHATE', 'SALINITY', 'SILICATE', 'SRTM30', 'TEMP'
]
classVar = ['PRESENT']
#catconate the variable
allVars = predictVars + classVar
#spaital reference the data
trainFC = DA.FeatureClassToNumPyArray(inputFC, ["SHAPE@XY"] + allVars)
spatRef = ARCPY.Describe(inputFC).spatialReference
#create the structure for the training data
data = PD.DataFrame(trainFC, columns=allVars)
corr = data.astype('float64').corr()
ax = SEA.heatmap(corr,
cmap=SEA.diverging_palette(220, 10, as_cmap=True),
square=True,
annot=True,
linecolor='k',
linewidths=1)
PLOT.show()
#take the data sample size
from arcpy import da, GetParameterAsText, AddMessage, SelectLayerByAttribute_management
import pandas as pd
EventTable = GetParameterAsText(0)
RouteId = GetParameterAsText(1)
FromMeas = GetParameterAsText(2)
ToMeas = GetParameterAsText(3)
OverlapOID = GetParameterAsText(4)
OID_field = 'OID@'
np_array = da.FeatureClassToNumPyArray(EventTable,
[RouteId, FromMeas, ToMeas, OID_field])
df = pd.DataFrame(np_array)
AddMessage(list(df))
route_list = df[RouteId].unique().tolist()
for route in route_list:
df_route = df.loc[df[RouteId] == route]
flipped_i = df_route.loc[df_route[FromMeas] > df_route[ToMeas]].index
df_route.loc[flipped_i,
[FromMeas, ToMeas]] = df_route.loc[flipped_i,
[ToMeas, FromMeas]].values
df_route.sort_values(by=[FromMeas, ToMeas], inplace=True)
for index, row in df_route.iterrows():
complete_overlap = ((df_route[FromMeas] > row[FromMeas]) &
(df_route[ToMeas] < row[ToMeas]))
partial_front_overlap = ((df_route[FromMeas] < row[FromMeas]) &
(df_route[ToMeas] > row[FromMeas]))
overlap_i = df_route.loc[partial_front_overlap
def anal():
timeDF = (pd.DataFrame(da.FeatureClassToNumPyArray(analysinput, 'date', skip_nulls=False, null_value=0)).tail(1))
starttime = pd.to_datetime(timeDF['date'].values[0])
dtable = date((starttime.year), (starttime.month), (starttime.day))
#d1 = date(2018, 5, 15)
d1 = dtable+timedelta(days = 1)
d2 = (date.today())
delta=d2-d1
fieldstoread = ['lat','lon','city_id', 'temp', 'clouds', 'weather_description', 'forecast_date2', 'forecast_date', 'rain',
'snow', 'name']
fieldstowrite = ['SHAPE@XY','name','id','rainsumm','snowsum','rainday','acttemp','avgtemp','clearday','date','t1','t2','t3','t4','t5']#
for i in range(len(s)):
print(s[i])
exp = "city_id = {}".format(s[i])
fc_np = da.FeatureClassToNumPyArray(weatherinput, fieldstoread, where_clause=exp, skip_nulls=False, null_value=0)
local = pd.DataFrame(fc_np)
local.drop_duplicates(subset='forecast_date2', keep='last', inplace=True)
latlon = (float(local['lat'].values[0]), float(local['lon'].values[0]))
print(type(latlon))
print(latlon)
for z in range(delta.days ):
x = str(d1 + timedelta(days=z))
rainsum = float(local.loc[local['forecast_date2'].str.contains('{}'.format(x))]['rain'].sum())
snowsum = float(local.loc[local['forecast_date2'].str.contains('{}'.format(x))]['snow'].sum())
acttempdf = (local.loc[local['forecast_date2'].str.contains('{}'.format(x))][local['temp']>10])
avgtemp = float(local.loc[local['forecast_date2'].str.contains('{}'.format(x))]['temp'].mean())
avgsun = float(local.loc[local['forecast_date2'].str.contains('{}'.format(x))]['clouds'].mean())
name = (local['name'].values[0])
t1df = (local.loc[local['forecast_date2'].str.contains('{}'.format(x))][local['temp']>10])
t2df = (local.loc[local['forecast_date2'].str.contains('{}'.format(x))][local['temp']> 1])
t3df = (local.loc[local['forecast_date2'].str.contains('{}'.format(x))][local['temp']> 2])
t4df = (local.loc[local['forecast_date2'].str.contains('{}'.format(x))][local['temp']> 5])
t5df = (local.loc[local['forecast_date2'].str.contains('{}'.format(x))][local['temp']> 8])
id = int(s[i])
if rainsum > 0:
rainday = 1
else:
rainday = 0
if avgsun < 16:
clearday = 1
else:
clearday = 0
if t1df.shape[0]<8:
t1 = 0
else:
t1 = float(acttempdf['temp'].mean())
if t2df.shape[0]<8:
t2 = 0
else:
t2 = float(acttempdf['temp'].mean())
if t3df.shape[0]<8:
t3 = 0
else:
t3 = float(acttempdf['temp'].mean())
if t4df.shape[0]<8:
t4 = 0
else:
t4 = float(acttempdf['temp'].mean())
if t5df.shape[0]<8:
t5 = 0
else:
t5 = float(acttempdf['temp'].mean())
if acttempdf.shape[0]<8:
acttemp = 0
else:
acttemp=float(acttempdf['temp'].mean())
pass
cursor = da.InsertCursor(analysinput, fieldstowrite)
print("creating row {} {} {} {} {} {} {}".format(latlon,name,id,rainsum,avgtemp,clearday,x))
cursor.insertRow((latlon,name,id,rainsum,snowsum,rainday,acttemp,avgtemp,clearday,x,t1,t2,t3,t4,t5))#
del cursor