SwampThingPaul

map2=base.map+tm_shape(TN.GM)+
  tm_symbols(size=0.5,col="Geomean",breaks=c(-Inf,0.5,1,2,Inf),showNA=T,palette=cols.rmp,
             title.col="Annual Geometric \nMean TN \nConcentration (mg/L)",
             labels=c("\u003C 0.5","0.5 - 1.0","1.0 - 2.0", "\u003E2.0"),
             border.lwd=0.5,colorNA = "white")+
  tm_compass(type="arrow",position=c("left","bottom"))+
  tm_scale_bar(position=c("left","bottom"))+
  tm_layout(bg.color=cols[2],fontfamily = "serif",legend.outside=T,scale=1,asp=NA,
            outer.margins=c(0.005,0.01,0.005,0.01),inner.margins = 0,between.margin=0,
            legend.text.size=1,legend.title.size=1.25)

SwampThingPaul

N=60
mu=0
sd=2

np.random.seed(0)
ran = np.random.normal(size=N)
error1 = sd**2 * ran + mu
error2 = sd*.5 * ran + mu

lin = np.linspace(-15., 15., num=N)

SwampThingPaul

import numpy as np
from numpy.linalg import inv
import statsmodels.api as sm

# from scratch
x = sm.add_constant(x) # add constant in the 0 index
b = inv(x.T.dot(x)).dot(x.T).dot(y)
yest_ols = np.array([b[2]*v**2 + b[1]*v + b[0] for v in x.T[0]])

# with using numpy.linalg.lstsq

SwampThingPaul

import numpy.linalg as la
def tls(X,y):
    
    if X.ndim is 1: 
        n = 1 # the number of variable of X
        X = X.reshape(len(X),1)
    else:
        n = np.array(X).shape[1] 
    
    Z = np.vstack((X.T,y)).T

SwampThingPaul

import scipy.odr as odr

def odr_line(B, x):
    y = B[0]*x + B[1]*x**2
    return y

def perform_odr(x, y, xerr, yerr):
    quadr = odr.Model(odr_line)
    mydata = odr.Data(x, y, wd=1./xerr, we=1./yerr)
    #mydata = odr.Data(x, y)

SwampThingPaul

packs=c("sp","rgdal","gstat","raster","spatstat","maptools","rgeos","tmap","GISTools","rasterVis","spdep","spsurvey")

for(i in 1:length(packs)){
  test=packs[i] %in% rownames(installed.packages())
  if(test==T){print("package already installed")}else{install.packages(packs[i])}
}

SwampThingPaul

require(spstat)

point.dat;# a shapefile of points with associated data.
study.area;# a shapefile of the study area/sampling area.

# Generate Thessian polygon and assign CRS
th=as(dirichlet(as.ppp(point.dat)),"SpatialPolygons")
proj4string(th)=proj4string(point.dat)

# Join thessian polygon with actual data

SwampThingPaul

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
plt.style.use('ggplot')

fig, (ax1, ax2) = plt.subplots(ncols=2,figsize=(16,6))
plt.xlim((0, 10))
plt.ylim((0, 7))
plt.tight_layout(w_pad=1.5)
#red line

SwampThingPaul

## Code was compiled by Paul Julian
## contact infor: [email protected]

#Libraries
library(HURDAT)
library(plyr)
library(sp)
library(tmap)

#Projection

SwampThingPaul

library(pwr)

##

r.val=seq(-0.9,-0.01,0.05)
n.val=4:20

power.rslt=data.frame()
for(j in 1:length(n.val)){