爱星光(istar)高端网站建设,腾讯企业邮箱入口网页版,微网站和网站的区别,百度主页入口本模块的主要内建子模块如下#xff1a; 如何获得完整代码#xff1a; 回复博主 或者 留言/私信 。 注意#xff1a;本代码完全开源#xff0c;可随意修改使用。 但如果您的成果使用或参考了本段代码#xff0c;给予一定的引用说明#xff08;非强制#xff09;#xf…
本模块的主要内建子模块如下 如何获得完整代码 回复博主 或者 留言/私信 。 注意本代码完全开源可随意修改使用。 但如果您的成果使用或参考了本段代码给予一定的引用说明非强制包括但不限于
1.作者洛2.网站gma.luosgeo.com3.PyPIhttps://pypi.org/project/gma/3.GitHubhttps://github.com/LiChongrui
其中 clindex气候指标计算函数 cmana气候诊断函数 et0蒸散计算函数 static气候常量 utils通用工具 示例代:1
from ..core.arraypro import *
from .utils import *#################################### 累积概率计算
def GammaCP(Data, Axis):gamma 分布累积概率if np.nanmin(Data) 0:Data Data np.abs(np.nanmin(Data)) * 2 # Data Data 1000PF ParameterFitting(Data, Axis Axis)Data PF.DataAxis PF.Axis# 计算 0 值概率并填充 0 值 为 NaNZeros (Data 0).sum(axis Axis, keepdims True)ProbabilitiesOfZero Zeros / Data.shape[Axis]Data[Data 0] np.nanAlphas, Betas ParameterFitting(Data, Axis Axis).MLE()# 使用gamma CDF 查找 gamma 概率值GammaProbabilities stats.gamma.cdf(Data, a Alphas, scale Betas)Probabilities ProbabilitiesOfZero (1 - ProbabilitiesOfZero) * GammaProbabilitiesreturn Probabilities def LogLogisticCP(Data, Axis):Log-Logistic 分布累积概率PF ParameterFitting(Data, Axis)Alpha, Beta, Gamma1 PF.LMoment()Probabilities 1 / (1 (Alpha / (PF.Data - Gamma1)) ** Beta)# 由于 scipy 对 non 值处理过于简单这里不使用 scipy 的函数# Probabilities stats.fisk.cdf(PF.Data, Beta, loc Gamma1, scale Alpha)return Probabilitiesdef Pearson3CP(Data, Axis):pearson III 分布累积概率if np.nanmin(Data) 0:Data Data np.abs(np.nanmin(Data)) * 2 PF ParameterFitting(Data, Axis)Data PF.DataAxis PF.Axis Loc, Scale, Skew PF.LMoment2()Alpha 4.0 / (Skew ** 2)MINPossible Loc - ((Alpha * Scale * Skew) / 2.0)Zeros (Data 0).sum(axis Axis, keepdims True)ProbabilitiesOfZero Zeros / Data.shape[Axis]Probabilities stats.pearson3.cdf(Data, Skew, Loc, Scale)Probabilities[(Data 0.0005) (ProbabilitiesOfZero 0.0)] 0.0Probabilities[(Data 0.0005) (ProbabilitiesOfZero 0.0)] 0.0005Probabilities[(Data MINPossible) (Skew 0)] 0.0005Probabilities[(Data MINPossible) (Skew 0)] 0.9995Probabilities ProbabilitiesOfZero (1.0 - ProbabilitiesOfZero) * Probabilitiesreturn Probabilitiesdef _ReshapeAndExtend(Data, Axis, Periodicity):更改输入数据维度为 Axis / Periodicity, Periodicity, N并补充末尾缺失数据# 交换设置轴到 0 if Data.ndim 1:Data np.swapaxes(Data, 0, Axis)S Data.shapeS0, S1 S[0], np.prod(S[1:], dtype int)Data Data.reshape((S0, S1))else:Data np.expand_dims(Data, -1)# 填充不足 Data.shape[0] / PeriodicityB Data.shape[0] % PeriodicityPW 0 if B 0 else Periodicity - BData np.pad(Data, ((0, PW), (0,0)), mode constant, constant_values np.nan)# 更改为目标维度3维PeriodicityTimes Data.shape[0] // Periodicity return Data.reshape(PeriodicityTimes, Periodicity, Data.shape[1])def _RestoreReshapeAndExtend(Data, Axis, Shape):对 _ReshapeAndExtend 修改的维度和数据进行还原# 还原为原始维度2维Data Data.reshape(np.prod(Data.shape[:2]), *Data.shape[2:])# 去除尾部填充值Data Data[:Shape[Axis]]# 还原到初始状态SHP list(Shape)SHP.pop(Axis)SHP [Shape[Axis]] SHPData Data.reshape(SHP)Data np.swapaxes(Data, Axis, 0)return Data############### 不同的计算方式
def _Fit(WBInScale, Periodicity, Distribution):计算标准化指数# 1.计算累积概率Probabilities eval(f{Distribution}CP)(WBInScale, 0)if Periodicity 1:Probabilities np.expand_dims(Probabilities, 1)# 2.生成结果OutInScale stats.norm.ppf(Probabilities)return OutInScaledef _API(WBInScale, Axis):计算距平指数# 1.计算平均值或趋势值Mean np.nanmean(WBInScale, axis Axis, dtype np.float64, keepdims True)# 4.生成结果OutInScale (WBInScale - Mean) / Meanreturn OutInScale############### 计算结果
def _Compute(Data, Axis, Scale, Periodicity, Distribution):自动计算 Periodicity ValueType(Periodicity, pint)# 0.数据准备DP DataPreparation(Data, Axis) Data DP.DataSHP Data.shapeAxis DP.Axis# 1.计算尺度WBInScale DP.SumScale(Scale)if not (SHP[Axis] Periodicity) and (SHP[Axis] Scale):return np.full(WBInScale.shape, np.nan)# 2.更改输入数据维度为 Axis / Periodicity, Periodicity, NWBInScale _ReshapeAndExtend(WBInScale, Axis, Periodicity)# 3.生成结果if Distribution API:OutInScale _API(WBInScale, Axis)else:OutInScale _Fit(WBInScale, Periodicity, Distribution)# 4.还原数据OutInScale _RestoreReshapeAndExtend(OutInScale, Axis, SHP) return OutInScale示例代码2
#################################### SPEI
def SPEI(PRE, PET, Axis None, Scale 1, Periodicity 12, Distribution LogLogistic):计算SPEIDistribution GetDistribution(Distribution)PRE, PET INITArray(PRE, PET)WB np.subtract(PRE, PET, dtype PRE.dtype)SPEIInScale _Compute(WB, Axis, Scale, Periodicity, Distribution)return SPEIInScale#################################### SPI
def SPI(PRE, Axis None, Scale 1, Periodicity 12, Distribution Gamma):计算 SPIDistribution GetDistribution(Distribution)SPIInScale _Compute(PRE, Axis, Scale, Periodicity, Distribution)return SPIInScale#################################### PAP
def PAP(PRE, Axis None, Scale 1, Periodicity 12):降水距平百分率PAPInScale _Compute(PRE, Axis, Scale, Periodicity, API) return PAPInScale
