Objectives:

Data and Software:

What you will turn in:

Lab 7 Submission Quiz

Welcome to Lab 7 for ESRM433/SEFS533!

In this lab we are going to be expanding on modeling with lidar. In particular we’re going to attempt to model wetlands across a landscape covered by our lidar data. This kind of modeling will use

This might be a big leap from linear regression but we’ll step through this lab exercise and think of things more conceptually.

For this lab we are also heading north outside of Washington and into Southeast Alaska which is home to the Tongass Rainforest.

The Tongass Rainforest and Tongass National Forest is the largest U.S. National Forest and worlds largest temperate rainforest at 17 million acres. The overall climate is perhumid or basically the wettest with nearly 200in per year on average and mean annual temperature between 39-54oF. With this climate, the forest is dominated by Sitka Spruce, Western Hemlock, and some Yellow Cedar. It is also home to brown bears, black bears, wolves, deer, and salmon. (https://en.wikipedia.org/wiki/Tongass_National_Forest)

The Tongass has been since time immemorial and still is home to Tlingit and Haida indigenous people who have been stewards of the land until Russian, European, and American settlement. (https://en.wikipedia.org/wiki/Tlingit)

As you may have guessed, this climate leads to the formation of many wetlands and peatlands. These wetlands and peatlands are especially critical to conserve because they store a lot of carbon and keep it from entering the atmosphere https://iopscience.iop.org/article/10.1088/1748-9326/aaed52

A Soil Carbon Map in Southeast Alaska McNicol et al., 2019

Part 1: Introduction to Scenario

Scenario: There is a small city-owned ski area in Juneau, Alaska that is looking to expand some of their skiing terrain and add a new ski lift. This is actually happening

But, the ski area sits on land that is covered by wetlands and peatlands due to the soggy and cool climate of the Tongass Rainforest

Wetlands on Eaglecrest

The city wants to limit the disturbance of wetlands but when consulting National Wetland Inventory (NWI) maps of the area, there are many that appear to be missing or poorly mapped.

Furthermore, many of the wetlands are forested and cannot be seen with aerial imagery, posing a challenge for the city to identify them.

The city is asking you, a lidar mapping consultant, if you can help them with a better map of wetlands in the area.

The ski area was more or less recently mapped with lidar in 2013 and could provide the necessary detail to map these wetlands. You will need to download and analyze the data and use the NWI as reference to build a new wetland map.

Part 2: Where is our lidar data?

This should be sort of familiar by now, but gathering data is sometimes the most difficult part of the job. Thankfully as a start the Alaska Department of Natural Resources Division of Geological & Geophysical Surveys maintains a data portal similar to the Washington Lidar Portal.

QUESTION 1: Check out some of the other data available in this database. Name 3 other lidar projects/acquisitions in Southeast Alaska

Turn off all the data to see better and navigate to Juneau, Alaska.

It’s north of Seatle

Turn on the Juneau 2013 Boundary and/or DTM Hillshade. These are visualization tools to get data. We’re looking for this small block within the total acquisition.

Zoom in a bit and use the box tool in the upper right to delineate the area to download data from. Then select the Juneau 2013 All Points (laz point cloud files), DTM, and Metadata

It’s sort of a big file with 714Mbs but half of that is the DTM. To speed up time downloading we can remove the DTM as well. I am hoping Madrona is OK with this…

Once the files are downloaded and extracted from the .zip folders, you can check out the metadata.

QUESTION 2: What was the point density of the first returns in P1 pts/m2 compared to ground returns in P1 pts/m2?

QUESTION 3: In Table 8, what is the classification number for Ground Points, Water, and Ice?

Also look in the laz folder, there are a lot of .laz files!

Part 4: Creating a DTM

So the data did come with a lidar derived DTM but we’re going to go through and create our own with a slightly lower resolution to save space.

Remember we can use R and the lidR package to generate DTMs

Let’s open up RStudio and load our libraries

library(lidR)
library(terra) #for rasters and spatrasters
library(sf) #needed for readLAScatalog
library(mapview)

We can now use the readLAScatalog function to “read” our .laz data. Note, this is the efficient way to read data since it does not do so completely. We can also set the chunk size setting the opt_chunk_size to something smaller than our tile size and opt_output_files to write files out of R and process things more efficiently

Check out ?readLAScatalog again and the LAScatalog-engine for details. https://cran.r-project.org/web/packages/lidR/vignettes/lidR-LAScatalog-engine.html

QUESTION 4: Read the above lidR LAScatalog-engine link. What are the roles of the catalog engine? Copy/Paste is fine but list one of the roles that is the most confusing for you

ctg <- readLAScatalog("Lab 7/Lab72023/data/dds4/juneau_2013_usgs/point_cloud_data/")
    #check the units for meters

plot(ctg, mapview = T)

We’re also going to do some filtering to get rid small tiles with less than 2,111,716 points. This leaves us with some nice squares to work with and lessens the data processing strain

ctg <- ctg[ctg$Number.of.point.records > 2111716]

plot(ctg, mapview = T)

I’m going to grab the shapefile of the lidar catalog footprint here

rgdal::writeOGR(as.spatial(ctg), "data/","las_footprint", driver="ESRI Shapefile")
plot(vect("data/las_footprint.shp"), type = "polygons")

Here we’re going to set our LAScatalog options for processing

All of this is set for our processing of our catalog file ctg

opt_chunk_size(ctg) <- 0 #the chunk size. 0 means the entire tile. 

opt_select(ctg) <- "zic"

opt_output_files(ctg) <- opt_output_files(ctg) <- "Lab 7/Lab72023/data/dds4/juneau_2013_usgs/point_cloud_data/written_chunks/{ORIGINALFILENAME}_dtm"

opt_laz_compression(ctg) <- TRUE #we want output as laz vs las

opt_progress(ctg) <- TRUE

optional: I just wanted to overwrite the files I made from these functions

ctg@output_options$drivers$SpatRaster$param$overwrite <- TRUE

Let’s create our DTM. This will take a while! So I would encourage you to write the file to your lab folder and save it in a safe place once it’s done. Then you should never have to do this again.

dtm <- rasterize_terrain(ctg, 5, tin(), pkg = "terra")
writeRaster(dtm, "Lab 7/Lab72023/newlidardtm.tif", overwrite = T)

When it finishes you should have something like this!

dtm <- rast("Lab 7/Lab72023/newlidardtm.tif")
plot(dtm)

We can also grab the intensity values while we’re at itn but we need to specify the function to get mean Intensity. This also could take a while so write out and save for easy future use.

m <- ~list(avgI = mean(Intensity))

intensity_raster <- pixel_metrics(ctg, m, res = 5)
writeRaster(intensity_raster, filename = "Lab 7/Lab72023/intensity_raster.tif")

et voila!

intensity_raster <- rast("Lab 7/Lab72023/intensity_raster.tif")
plot(intensity_raster,  main = "Lidar Intensity")

“QUESTION 5: Take a screenshot of your DTM but with a different color scheme. You and do this with the plot() function and setting col = c('red', "blue', 'purple') or some other combination

Part 5: DTM Metrics

Now that we have our DTM we’re going to calculate some metrics that refer to topography. The reason we focus on topography is so that we can see beneath the canopy at what is going on on the ground.

Check out this collaborator paper on hidden wetlands: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-665/

QUESTION 6: From the paper what is the tool used to identify wetlands called? Name one of the terrain variables used in the model. Hint: look at one of the figures

To create metrics we need some additional help from GIS interfacing packages. One of those is whiteboxfrom Whitebox Geospatial Inc. They have a great manual for a bunch of tools related to terrain analysis

https://www.whiteboxgeo.com/manual/wbt_book/intro.html

and a complete list of tools in R specifically are listed here:

https://whiteboxr.gishub.org/reference/index.html

Download and install whitebox

install.packages("whitebox")
whitebox::install_whitebox() #NEED TO DO AND MAKE SURE IT WORKS
library(whitebox)

Check out the lidar tools

print(wbt_list_tools("lidar"))
##  [1] "All 50 Tools containing keywords:"                                                                                                                                                                                             
##  [2] "AsciiToLas: Converts one or more ASCII files containing LiDAR points into LAS files."                                                                                                                                          
##  [3] "ClassifyBuildingsInLidar: Reclassifies a LiDAR points that lie within vector building footprints."                                                                                                                             
##  [4] "ClassifyOverlapPoints: Classifies or filters LAS points in regions of overlapping flight lines."                                                                                                                               
##  [5] "ClipLidarToPolygon: Clips a LiDAR point cloud to a vector polygon or polygons."                                                                                                                                                
##  [6] "ErasePolygonFromLidar: Erases (cuts out) a vector polygon or polygons from a LiDAR point cloud."                                                                                                                               
##  [7] "FilterLidarClasses: Removes points in a LAS file with certain specified class values."                                                                                                                                         
##  [8] "FilterLidarScanAngles: Removes points in a LAS file with scan angles greater than a threshold."                                                                                                                                
##  [9] "FindFlightlineEdgePoints: Identifies points along a flightline's edge in a LAS file."                                                                                                                                          
## [10] "FlightlineOverlap: Reads a LiDAR (LAS) point file and outputs a raster containing the number of overlapping flight-lines in each grid cell."                                                                                   
## [11] "HeightAboveGround: Normalizes a LiDAR point cloud, providing the height above the nearest ground-classified point."                                                                                                            
## [12] "LasToAscii: Converts one or more LAS files into ASCII text files."                                                                                                                                                             
## [13] "LasToMultipointShapefile: Converts one or more LAS files into MultipointZ vector Shapefiles. When the input parameter is not specified, the tool grids all LAS files contained within the working directory."                  
## [14] "LasToShapefile: Converts one or more LAS files into a vector Shapefile of POINT ShapeType."                                                                                                                                    
## [15] "LasToZlidar: Converts one or more LAS files into the zlidar compressed LiDAR data format."                                                                                                                                     
## [16] "LidarBlockMaximum: Creates a block-maximum raster from an input LAS file. When the input/output parameters are not specified, the tool grids all LAS files contained within the working directory."                            
## [17] "LidarBlockMinimum: Creates a block-minimum raster from an input LAS file. When the input/output parameters are not specified, the tool grids all LAS files contained within the working directory."                            
## [18] "LidarClassifySubset: Classifies the values in one LiDAR point cloud that correspond with points in a subset cloud."                                                                                                            
## [19] "LidarColourize: Adds the red-green-blue colour fields of a LiDAR (LAS) file based on an input image."                                                                                                                          
## [20] "LidarDigitalSurfaceModel: Creates a top-surface digital surface model (DSM) from a LiDAR point cloud."                                                                                                                         
## [21] "LidarElevationSlice: Outputs all of the points within a LiDAR (LAS) point file that lie between a specified elevation range."                                                                                                  
## [22] "LidarGroundPointFilter: Identifies ground points within LiDAR dataset using a slope-based method."                                                                                                                             
## [23] "LidarHexBinning: Hex-bins a set of LiDAR points."                                                                                                                                                                              
## [24] "LidarHillshade: Calculates a hillshade value for points within a LAS file and stores these data in the RGB field."                                                                                                             
## [25] "LidarHistogram: Creates a histogram of LiDAR data."                                                                                                                                                                            
## [26] "LidarIdwInterpolation: Interpolates LAS files using an inverse-distance weighted (IDW) scheme. When the input/output parameters are not specified, the tool interpolates all LAS files contained within the working directory."
## [27] "LidarInfo: Prints information about a LiDAR (LAS) dataset, including header, point return frequency, and classification data and information about the variable length records (VLRs) and geokeys."                            
## [28] "LidarJoin: Joins multiple LiDAR (LAS) files into a single LAS file."                                                                                                                                                           
## [29] "LidarKappaIndex: Performs a kappa index of agreement (KIA) analysis on the classifications of two LAS files."                                                                                                                  
## [30] "LidarNearestNeighbourGridding: Grids LiDAR files using nearest-neighbour scheme. When the input/output parameters are not specified, the tool grids all LAS files contained within the working directory."                     
## [31] "LidarPointDensity: Calculates the spatial pattern of point density for a LiDAR data set. When the input/output parameters are not specified, the tool grids all LAS files contained within the working directory."             
## [32] "LidarPointStats: Creates several rasters summarizing the distribution of LAS point data. When the input/output parameters are not specified, the tool works on all LAS files contained within the working directory."          
## [33] "LidarRansacPlanes: Performs a RANSAC analysis to identify points within a LiDAR point cloud that belong to linear planes."                                                                                                     
## [34] "LidarRbfInterpolation: Interpolates LAS files using a radial basis function (RBF) scheme. When the input/output parameters are not specified, the tool interpolates all LAS files contained within the working directory."     
## [35] "LidarRemoveDuplicates: Removes duplicate points from a LiDAR data set."                                                                                                                                                        
## [36] "LidarRemoveOutliers: Removes outliers (high and low points) in a LiDAR point cloud."                                                                                                                                           
## [37] "LidarRooftopAnalysis: Identifies roof segments in a LiDAR point cloud."                                                                                                                                                        
## [38] "LidarSegmentation: Segments a LiDAR point cloud based on differences in the orientation of fitted planar surfaces and point proximity."                                                                                        
## [39] "LidarSegmentationBasedFilter: Identifies ground points within LiDAR point clouds using a segmentation based approach."                                                                                                         
## [40] "LidarTINGridding: Creates a raster grid based on a Delaunay triangular irregular network (TIN) fitted to LiDAR points."                                                                                                        
## [41] "LidarThin: Thins a LiDAR point cloud, reducing point density."                                                                                                                                                                 
## [42] "LidarThinHighDensity: Thins points from high density areas within a LiDAR point cloud."                                                                                                                                        
## [43] "LidarTile: Tiles a LiDAR LAS file into multiple LAS files."                                                                                                                                                                    
## [44] "LidarTileFootprint: Creates a vector polygon of the convex hull of a LiDAR point cloud. When the input/output parameters are not specified, the tool works with all LAS files contained within the working directory."         
## [45] "LidarTophatTransform: Performs a white top-hat transform on a Lidar dataset; as an estimate of height above ground, this is useful for modelling the vegetation canopy."                                                       
## [46] "NormalVectors: Calculates normal vectors for points within a LAS file and stores these data (XYZ vector components) in the RGB field."                                                                                         
## [47] "SelectTilesByPolygon: Copies LiDAR tiles overlapping with a polygon into an output directory."                                                                                                                                 
## [48] "ZlidarToLas: Converts one or more zlidar files into the LAS data format."                                                                                                                                                      
## [49] "LidarShift: Shifts the x,y,z coordinates of a LiDAR file."                                                                                                                                                                     
## [50] "IndividualTreeDetection: Identifies points in a LiDAR point cloud that are associated with the tops of individual trees."                                                                                                      
## [51] "NormalizeLidar: Normalizes a LiDAR point cloud."

Or tool parameters

print(wbt_tool_parameters("slope"))
## [1] "{\"parameters\": [{\"name\":\"Input DEM File\",\"flags\":[\"-i\",\"--dem\"],\"description\":\"Input raster DEM file.\",\"parameter_type\":{\"ExistingFile\":\"Raster\"},\"default_value\":null,\"optional\":false},{\"name\":\"Output File\",\"flags\":[\"-o\",\"--output\"],\"description\":\"Output raster file.\",\"parameter_type\":{\"NewFile\":\"Raster\"},\"default_value\":null,\"optional\":false},{\"name\":\"Z Conversion Factor\",\"flags\":[\"--zfactor\"],\"description\":\"Optional multiplier for when the vertical and horizontal units are not the same.\",\"parameter_type\":\"Float\",\"default_value\":null,\"optional\":true},{\"name\":\"Units\",\"flags\":[\"--units\"],\"description\":\"Units of output raster; options include 'degrees', 'radians', 'percent'\",\"parameter_type\":{\"OptionList\":[\"degrees\",\"radians\",\"percent\"]},\"default_value\":\"degrees\",\"optional\":true}]}"

One thing that is a bit weird with whitebox is that the functions require a file path but not a variable. So the file path in quotes should go to the file and the output should also be a filepath with a file name.

Here’s an example using the wbt_mean_curvature

wbt_mean_curvature(
  dem = "Lab 7/Lab72023/newlidardtm.tif", #file path not variable
  output = "Lab 7/Lab72023/mean_curv.tif")

Now we can open the mean_curv.tif using the rast function from the terra package and plot it

plot(rast("Lab 7/Lab72023/mean_curv.tif"))

You are now going to build many terrain derivatives from your new dtm that correspond to potential wetlands. We’re kind of going to follow a couple sources that have done something similar:

We can use a few of the important variables from these sources for our wetland mapping particularly: slope, curvature, topographic wetness index, and topographic position index

“QUESTION 7: From the whitebox tools manual find Wetness Index under the Geomorphometric Section and find its equation. Notice it takes 2 paramters”As” and “Slope”. What is the As? https://www.whiteboxgeo.com/manual/wbt_book/intro.html 

wbt_slope(
    dem = "Lab 7/Lab72023/newlidardtm.tif",
    output = "Lab 7/Lab72023/slope.tif"
)

wbt_total_curvature(
    dem = "Lab 7/Lab72023/newlidardtm.tif",
    output = "Lab 7/Lab72023/totalcurve.tif"
)

wbt_relative_topographic_position(
    dem = "Lab 7/Lab72023/newlidardtm.tif", 
    output = "Lab 7/Lab72023/rTPI.tif", 
    filterx = 5, filtery = 5
)

wbt_dev_from_mean_elev(
    dem = "Lab 7/Lab72023/newlidardtm.tif",
    output = "Lab 7/Lab72023/DEV.tif",
    filterx = 11, filtery = 11
)

In order to get topographic wetness index or just wetness index in whitebox we need to calculate specific contributing area (SCA) or in whitebox: flow accumulation which needs a hydrologically condition DTM where there are flow paths down the entire area. Usually this means we fill depressions in a DTM but we can use a breach depressions method that is a lower impact. This is described here:

wbt_breach_depressions_least_cost(
    dem = "Lab 7/Lab72023/newlidardtm.tif",
    output = "Lab 7/Lab72023/newlidardtm_breach.tif",
    dist = 10
)

wbt_d_inf_flow_accumulation(
    input = "Lab 7/Lab72023/newlidardtm_breach.tif",
    output = "Lab 7/Lab72023/d_inf_sca.tif",
    log = F
)
plot(log(rast("Lab 7/Lab72023/d_inf_sca.tif")), main = "Log Transformed SCA/FlowAccumulation")

Now let’s calculate topographic wetness index

wbt_wetness_index(
    sca = "Lab 7/Lab72023/d_inf_sca.tif",
    slope = "Lab 7/Lab72023/slope.tif", 
    output = "Lab 7/Lab72023/TWI.tif"
)
plot(rast("Lab 7/Lab72023/TWI.tif"), main = "Topographic Wetness Index")

Now we have a bunch of metrics calculated from our lidar DTM. In the paper examples there were many more metrics being used but for this lab we will use just a few. Next, we need to set up a wetland prediction analysis.

Part 6: Wetland Prediction with Random Forest and the National Wetland Inventory

Random Forest is probably something you’ve heard about but if not, this will be a short overview of it, but not comprehensive. There are many resources to check out for this but here are a couple:

Think of your data randomly split up and walking on a trail in the woods then making certain decisions at trail forks. Those decisions on which way to go for at each trail fork will end up at certain locations. In this case the locations are classifications.

A Random Forest consists of multiple random decision/classification trees. Two types of randomnesses are built into the trees. First, each tree is built on a random sample from the original data. Second, at each tree node, a subset of features are randomly selected to generate the best split.

Basically we start with a dataset that has our lidar attributes and known classifications. We call this dataset training data. We build additional small sets of data by randomly sampling our training data in a process called bagging. Each “bag” is then run through a decision/classification tree whose branches are split based on randomly selected variables from our dataset. The result of each decision/classification tree is a classification and the final part of the Random Forest is taking a majority vote from all trees on what is the best classification.

An Example Dataset

An example tree built from dataset

A brief view of Random Forest with multiple decision trees

This is a super simplified explanation so take it with a grain of salt. But Random Forest is used in many remote sensing and lidar applications so getting introduced to it is really important. For this lab we essentially want to use our lidar data in a classification scheme and Random Forest is one of the best tools

We want to know whether a pixel in our study area is a wetland or not

Let’s start with loading the libraries we need:

install.packages("randomForest")
install.packages("caret")

library(randomForest)
library(caret)

We also need some training data that tells us where known wetland locations are. We are fortunate that in the U.S. we have a great start with the National Wetland Inventory that can provide us with training data.

Check out the National Wetland Inventory Wetland Mapper and find our study area near Juneau Alaska

QUESTION 8: If we look at some of the NWI wetlands in our area, what is the most common habitat classification type? Also find one wetland and provide the classification code then describe it. It should look like PEM1/SS1B. Hint: click on the description tab in the popup window

QUESTION 9: Take a screenshot of the NWI mapper with an area that might be a wetland but is not covered by an NWI polygon. Why do you think that is area is a wetland? i.e. describe visual cues. No wrong answers really I just want to look at the data

So this is great to have the NWI but how do we get training data from this? Well, it’s a bit complicated. You can do this by:

  1. Downloading the data from the NWI in polygon format

  2. Clipping the polygons to the study area

  3. Generating a stratified sample of points within the polygons and outside of the polygons

  4. Labeling points within NWI wetlands as “wetlands” and points outside of the NWI as “uplands”

  5. Exporting the points for extracting lidar metrics.

Because this is a Lidar class, we are going to ask out “coworker” to provide this data for us. Luckily this coworker has done this before an has created a beautiful training dataset that we should be thankful for. They even took care of some errors with the NWI and fixed points that were wrong 😎

But one of the things you should be aware of is how to get data from different sources, one of which is spreadsheets in .csv form which we have done a bit before. But below this code below:

  1. Opens a .csv file that contains our NWI wetland/upland points

  2. csv spreadsheets don’t have a projection and this one was made in a different projection. So we need to import it using terra::vect and specifying the crs that our “coworker” made the training data in.

  3. We need to then project our data into the crs that we use which is "EPSG:26908"

NWI_training_csv <- read.csv("Lab 7/Lab72023/NWI/AK_douglas_wetlands_pts_studyarea_att.csv")
NWI_training_pts <- terra::vect(NWI_training_csv, geom = c("X", "Y"), crs="EPSG:3338")
NWI_training_pts_prj <- terra::project(NWI_training_pts, "EPSG:26908")

Now we’re going to bring in the NWI wetlands, the las footprint, the dtm, and the training points, put them all in the same projection and map them

NWI_wetlands <- terra::vect("Lab 7/Lab72023/NWI/AK_douglas_wetlands_studyarea.gpkg") # import wetland polygons
## Warning: [vect] Reading layer: AK_douglas_wetlands_studyarea
## Other layers: wetlands
NWI_wetlands_prj <- terra::project(NWI_wetlands, "EPSG:26908") # reproject the wetlands into our las projectedion

las_footprint <- terra::vect("Lab 7/Lab72023/data/las_footprint.shp") # import the las footprint polygons

NWI_wetlands_mask <- terra::mask(NWI_wetlands_prj, las_footprint) # mask or clip the polygons to the las footprint


plot(dtm) # plot the dtm 
plot(NWI_wetlands_mask, type = "polygons", add = T) # add the wetland polygons 
plot(NWI_training_pts_prj, "class", col = c("red", "blue"), cex = 0.5, type= "classes", add = T) # add the NWI training points

Now we have our training dataset and we can extract the lidar metrics.

First we need to build a raster stack, basically layer all our lidar metrics on top of one another. To make this easy to read we can import all of our whitebox metrics into R as variables

Don’t forget we can also add in our lidar intensity too!

slope <- rast("Lab 7/Lab72023/slope.tif")
tot_curve <- rast("Lab 7/Lab72023/totalcurve.tif")
mean_curve <- rast("Lab 7/Lab72023/mean_curv.tif")
topo_position <- rast("Lab 7/Lab72023/rTPI.tif")
sca_flowacc <- rast("Lab 7/Lab72023/d_inf_sca.tif") # let's log transform this one because it big - see below
dev <- rast("Lab 7/Lab72023/DEV.tif")
twi <- rast("Lab 7/Lab72023/TWI.tif")


stack <- c(slope, (tot_curve), mean_curve, topo_position, log(sca_flowacc),dev, twi, intensity_raster)
plot(stack)

Ok we’re stacked and ready to extract these metrics using our wetland upland point data

?terra::extract

pts_extract <- terra::extract(stack, NWI_training_pts_prj, method = "bilinear", bind = T)
pts_extract <- as.data.frame(pts_extract)

#remove any rows that contain NA
pts_extract <- pts_extract[complete.cases(pts_extract),]

#Just some extra styling code for the table
kable(pts_extract, "html") %>% kable_styling("striped") %>% scroll_box(width = "100%", height = "200px")
fid ATTRIBUTE class slope totalcurve mean_curv rTPI d_inf_sca DEV TWI avgI
1 3 PEM1/SS1B WET 15.4285233 0.0001010 -0.0038604 -0.0878110 4.830799 -0.1266186 6.124140 72.96473
2 4 PEM1/SS1B WET 11.2360603 0.0000368 -0.0023864 -0.0344370 6.304861 -0.1063135 7.921163 74.04179
3 5 PEM1/SS1B WET 10.1945076 0.0001078 -0.0053226 -0.1470817 5.373756 -0.1277044 7.103285 74.13510
4 6 PEM1/SS1B WET 13.0368540 0.0000162 0.0006735 0.0245300 5.559433 0.0230528 7.023629 73.69513
5 7 PEM1/SS1B WET 13.4254692 0.0001046 -0.0060223 -0.1308736 6.877510 -0.3121900 8.314783 75.05712
6 11 PEM1/SS1B WET 2.7016722 0.0000373 0.0004811 0.2348728 2.851914 0.1668981 5.986958 70.95591
7 12 PEM1/SS1B WET 9.6953927 0.0000232 0.0022076 0.0907692 2.371331 0.1715326 4.138388 84.83896
8 13 PEM1/SS1B WET 3.5444229 0.0000335 -0.0019773 -0.2410819 7.769579 -0.2457803 10.556160 73.99895
9 14 PEM1/SS1B WET 9.5572531 0.0000180 -0.0017024 -0.0394770 5.740816 -0.0350202 7.522515 80.60236
10 15 PEM1/SS1B WET 8.8394357 0.0000453 0.0024900 0.1224361 2.363076 0.0828913 4.224226 90.32671
11 42 PEM1/SS1B WET 10.8374436 0.0000238 -0.0003153 0.0507437 2.474046 -0.1550018 4.127300 81.56779
12 43 PEM1/SS1B WET 9.3390033 0.0000928 0.0035220 0.2415147 1.738159 0.1800290 3.547434 67.46588
13 44 PEM1/SS1B WET 7.7806188 0.0001057 -0.0000210 -0.1704832 6.611493 -0.3110811 8.611083 63.63974
14 45 PEM1/SS1B WET 13.4614022 0.0001008 0.0046435 0.1209067 2.648347 0.1018279 4.078565 72.18480
15 46 PEM1/SS1B WET 13.6824135 0.0001000 -0.0057998 -0.1803905 4.553503 -0.2281663 5.973629 68.49661
16 47 PEM1/SS1B WET 2.7603216 0.0001059 -0.0050522 -0.1723250 2.054248 -0.2229692 5.170403 44.22005
17 48 PEM1/SS1B WET 6.9011995 0.0000178 -0.0011642 -0.1763267 4.795780 -0.2584912 6.914841 47.58430
18 49 PEM1/SS1B WET 3.7344752 0.0000283 0.0012632 0.1948638 2.226896 -0.2079087 5.215448 61.42358
19 50 PEM1/SS1B WET 0.8826381 0.0000447 0.0019380 0.9262514 1.779569 0.2034058 5.973160 69.21864
20 51 PEM1/SS1B WET 4.2135979 0.0000754 -0.0031155 -0.0410034 1.808993 -0.4188579 4.430646 32.26825
21 52 PUBH WET 3.9502881 0.0000548 -0.0033824 -0.3069497 5.325379 -1.0824736 8.005301 51.19912
22 53 PUBH WET 3.3879424 0.0000149 0.0021984 0.3173064 2.121654 0.2992874 4.970189 68.79109
23 54 PUBH WET 13.2452200 0.0001379 0.0058497 0.1411914 2.268607 0.1937242 3.718556 77.95384
24 55 PUBH WET 5.9512670 0.0001884 -0.0050537 -0.1314860 2.855228 -0.4269709 5.126030 23.14726
25 56 PUBH WET 7.5862593 0.0000040 -0.0005791 -0.0140974 5.260795 -0.0136595 7.276823 85.66704
26 59 PEM1/SS1B WET 31.1214443 0.0001409 -0.0030473 -0.0592352 4.933095 -0.1666624 5.438029 56.35942
27 60 PEM1/SS1B WET 12.4495134 0.0000142 -0.0002375 -0.0060809 3.995636 -0.0731629 5.506391 77.37840
28 61 PEM1/SS1B WET 14.7229596 0.0000497 0.0034415 0.0417739 5.569179 0.0920136 6.906483 73.35294
29 62 PEM1/SS1B WET 11.1718687 0.0000189 -0.0000663 -0.0376204 6.142448 -0.0146729 7.765783 72.52229
30 63 PEM1/SS1B WET 13.5655622 0.0000139 0.0003193 0.0048318 5.697654 0.0684637 7.119763 74.93178
31 64 PEM1/SS1B WET 7.1425060 0.0005090 0.0076892 0.2347859 1.667682 0.4548066 3.772235 68.34502
32 65 PEM1/SS1B WET 9.0746677 0.0006529 0.0155030 0.3343531 1.913196 0.4786112 3.775604 67.19323
33 66 PEM1/SS1B WET 11.7088638 0.0003348 0.0060168 0.1830825 1.876894 0.4375458 3.473521 64.99521
34 67 PEM1/SS1B WET 8.8282648 0.0001487 0.0027625 0.1208068 1.877304 0.2874718 3.746055 53.09577
35 68 PEM1/SS1B WET 12.7046074 0.0002401 0.0070608 0.2483179 1.808019 0.3830897 3.305501 60.61104
36 99 PEM1/SS1B WET 5.9364860 0.0000028 -0.0000117 0.0050809 3.821047 0.0951343 6.084939 73.87518
37 100 PEM1/SS1B WET 10.4458194 0.0000460 -0.0019873 0.0026609 2.960744 -0.0382593 4.653162 118.00105
38 101 PEM1/SS1B WET 8.0253306 0.0000298 0.0021957 0.0557252 2.330124 0.1860635 4.290536 111.71656
39 102 PEM1/SS1B WET 2.2172640 0.0000120 -0.0004798 -0.0680278 5.360872 -0.2906564 8.614608 76.45492
40 103 PEM1/SS1B WET 13.6514553 0.0001423 -0.0039587 -0.0533104 4.848237 -0.2581925 6.268394 65.07863
41 104 PEM1/SS1B WET 10.9403598 0.0029375 0.0298188 0.8486951 1.609438 0.8261487 3.433015 101.24076
42 105 PEM1/SS1B WET 2.1639817 0.0001475 -0.0020553 -0.1879058 2.987384 0.1790857 6.371991 73.51407
43 106 PEM1/SS1B WET 1.8686422 0.0003775 0.0043253 0.0628965 2.022558 0.3534795 5.751120 73.12829
44 107 PEM1/SS1B WET 3.4972841 0.0000510 -0.0010231 -0.0308122 2.848519 -0.0466904 5.660249 74.64726
45 108 PEM1/SS1B WET 7.1612642 0.0002327 0.0005386 -0.1571605 4.694536 0.4056329 6.774995 70.76350
46 109 PEM1/SS1B WET 11.4063182 0.0001375 0.0052485 0.1083904 2.265378 0.1705660 3.866481 62.80339
47 110 PEM1/SS1B WET 10.7386333 0.0000239 0.0022519 0.0105465 4.101019 -0.0386185 5.766064 62.11465
48 111 PEM1/SS1B WET 11.1135114 0.0000190 0.0024037 0.0319058 3.702813 0.0154280 5.330467 62.07716
49 112 PEM1/SS1B WET 10.0260970 0.0000601 0.0029756 0.1356278 3.548427 0.1663873 5.284074 64.56728
50 113 PEM1/SS1B WET 11.8424981 0.0000064 0.0005920 -0.0043106 3.623471 0.0884627 5.185736 64.97349
51 117 PEM1/SS1B WET 13.3488548 0.0000015 -0.0004141 -0.0025808 7.660271 -0.1080257 9.098822 79.07182
52 118 PEM1/SS1B WET 29.1704449 0.0001742 0.0060724 0.0516372 3.565798 0.1818119 4.148950 62.79825
53 119 PEM1/SS1B WET 12.2080123 0.0000088 0.0005683 -0.0041936 6.772998 -0.1250187 8.304661 78.69912
54 120 PEM1/SS1B WET 16.5975452 0.0000593 -0.0038793 0.0013962 5.733582 -0.1891820 6.944580 76.62791
55 121 PEM1/SS1B WET 28.6959122 0.0002712 -0.0042429 -0.0271489 5.715586 -0.1607887 6.319107 66.70105
56 123 PEM1/SS1B WET 13.7265060 0.0000238 0.0014990 0.0140011 4.329833 0.0152760 5.739640 75.79083
57 124 PEM1/SS1B WET 13.3068871 0.0000154 -0.0003303 -0.0057108 4.423271 0.0301811 5.865416 74.27521
58 125 PEM1/SS1B WET 14.8088052 0.0000468 0.0008985 -0.0037670 3.858332 -0.0113112 5.189872 69.48388
59 126 PEM1/SS1B WET 13.5352114 0.0000252 -0.0001170 0.0293428 4.273354 -0.0004917 5.697969 79.88100
60 127 PEM1/SS1B WET 14.2370963 0.0000646 0.0013345 -0.0209382 4.134574 0.0972333 5.508603 65.22009
61 133 PEM1/SS1B WET 14.7423903 0.0001253 -0.0002098 -0.0094111 5.717943 -0.0799564 7.053687 69.15041
62 134 PEM1/SS1B WET 17.7680994 0.0000357 0.0013208 0.0311448 3.314832 -0.0634797 4.454708 73.92441
63 135 PEM1/SS1B WET 15.3499414 0.0001181 0.0049436 0.0684036 3.877798 0.1557503 5.174002 80.97631
64 136 PEM1/SS1B WET 12.6640089 0.0000084 0.0005207 0.0539479 4.414975 0.0650455 5.908009 84.53380
65 137 PEM1/SS1B WET 15.6047399 0.0000560 -0.0036909 -0.0544061 4.621735 -0.1220191 5.897317 70.06134
66 141 PEM1/SS4B WET 1.9499296 0.0000269 0.0027615 0.1948946 1.637570 0.1486959 5.021673 81.43428
67 142 PEM1/SS4B WET 4.4020981 0.0000170 0.0008422 -0.0097817 3.944294 0.2467222 6.510980 68.64893
68 143 PEM1/SS4B WET 11.6156413 0.0001749 0.0077951 0.1294650 6.548004 0.1142938 8.134710 65.89048
69 144 PEM1/SS4B WET 1.9405426 0.0000419 -0.0003617 0.1213667 1.632043 -0.2626821 5.064011 72.63384
70 145 PEM1/SS4B WET 4.0985987 0.0000852 -0.0044728 -0.1434701 3.766047 -0.2050135 6.414016 77.96420
71 164 PEM1/SS4B WET 1.6307762 0.0000304 0.0001868 -0.3202621 6.533524 -0.1049596 10.420049 71.47164
72 165 PEM1/SS4B WET 6.4476057 0.0006659 0.0066493 0.2270114 2.435318 0.5239108 4.651359 42.91229
73 166 PEM1/SS4B WET 11.0850553 0.0000131 0.0006675 -0.0392945 3.435703 0.0070512 5.067369 105.89962
74 167 PEM1/SS4B WET 9.3070783 0.0002035 -0.0061115 -0.1047028 4.038583 -0.4344874 5.851274 42.93755
75 168 PEM1/SS4B WET 7.4048719 0.0000143 0.0020006 0.0846019 3.231662 0.1197024 5.280225 79.49529
76 176 PEM1/SS4B WET 7.7429367 0.0000456 0.0006179 0.0165127 3.004655 0.0372113 5.004955 69.76954
77 177 PEM1/SS4B WET 7.8144789 0.0000323 -0.0014951 -0.0304502 5.676705 -0.1121641 7.663042 37.54826
78 178 PEM1/SS4B WET 7.8642110 0.0000106 -0.0001509 0.0367615 2.540755 0.0196582 4.522758 48.47089
79 179 PEM1/SS4B WET 8.1463381 0.0000370 -0.0014159 0.0827146 1.984709 0.0860863 3.935816 87.09481
80 180 PEM1/SS4B WET 9.7090143 0.0000869 0.0013946 -0.0002406 2.789772 0.1006120 4.560559 52.87144
81 203 PEM1B WET 19.9886126 0.0000872 0.0041387 0.0545633 3.808048 0.1415345 4.819559 70.60768
82 204 PEM1B WET 3.4324394 0.0000564 -0.0023977 -0.0959719 7.795427 -0.0661819 10.611748 69.60236
83 205 PEM1B WET 6.4020410 0.0000835 0.0011143 0.0257019 3.637822 -0.0511487 5.836949 80.98850
84 206 PEM1B WET 21.2347891 0.0000683 -0.0028272 -0.0285689 4.713898 -0.2406159 5.659505 72.63076
85 207 PEM1B WET 7.8934982 0.0000600 0.0039702 0.1404274 4.642349 0.1749118 6.640787 70.17915
86 219 PEM1B WET 14.8919272 0.0003778 -0.0110196 -0.2579566 5.756725 -0.4384903 7.109786 69.93726
87 220 PEM1B WET 12.9318992 0.0002023 -0.0062042 -0.1643294 5.762283 -0.2768553 7.237354 79.38264
88 221 PEM1B WET 8.1622631 0.0000247 0.0026807 0.0037980 5.868004 0.1476794 7.810015 64.08674
89 222 PEM1B WET 7.4272340 0.0001831 -0.0013544 -0.1650470 2.512256 0.2043898 4.598336 74.55512
90 223 PEM1B WET 5.0366682 0.0000450 0.0014783 0.1267500 5.002167 -0.1247652 7.460975 77.88508
91 235 PEM1B WET 9.0655422 0.0000668 0.0044799 0.1033226 3.184151 0.3768644 5.024716 71.26437
92 236 PEM1B WET 0.0966983 0.0000000 0.0001299 0.1591504 1.940618 0.4142855 8.330541 56.39075
93 237 PEM1B WET 7.3797616 0.0000827 0.0010535 0.0910557 2.358878 0.0861865 4.414650 75.92640
94 238 PEM1B WET 2.8529625 0.0000034 0.0004449 0.0570517 2.372274 0.4100490 5.373684 77.17806
95 239 PEM1B WET 4.3605895 0.0000220 0.0020525 0.0582360 3.604820 0.2735235 6.181176 74.65243
96 245 PEM1B WET 9.3240114 0.0000051 0.0011289 -0.1495019 3.322404 -0.1348756 5.129339 68.72113
97 246 PEM1B WET 8.6673800 0.0000832 -0.0053453 -0.1680519 3.746393 -0.1380814 5.641377 61.64610
98 247 PEM1B WET 9.4003133 0.0000880 -0.0040063 -0.0476947 3.066770 -0.2192498 4.866020 55.91070
99 248 PEM1B WET 8.6923821 0.0000868 0.0019515 0.0087132 2.611859 -0.0655358 4.493884 47.13327
100 249 PEM1B WET 3.9930724 0.0000174 -0.0009458 -0.1006306 6.282910 -0.0768360 8.975253 41.94822
101 251 PEM1B WET 8.4444884 0.0000349 0.0020234 0.1468342 2.085056 0.1495488 3.992950 55.56453
102 252 PEM1B WET 9.8978001 0.0002121 0.0087662 0.5492008 2.151330 0.6177034 3.907698 38.16983
103 253 PEM1B WET 3.8281423 0.0001133 -0.0010055 -0.1566792 5.076017 -0.0052511 7.841519 34.11610
104 254 PEM1B WET 2.6919371 0.0000445 0.0023385 0.0305064 6.214105 0.0588881 9.286320 57.84997
105 255 PEM1B WET 5.4685783 0.0000204 0.0006766 0.0422791 5.401719 -0.1462312 7.772224 11.87712
106 256 PEM1B WET 15.3723237 0.0001207 0.0037097 0.1871047 1.984400 0.1799207 3.279091 80.33611
107 257 PEM1B WET 10.1066615 0.0002598 0.0059985 0.0851597 5.164393 0.2089469 6.896696 35.59748
108 258 PEM1B WET 12.5894770 0.0003535 0.0084313 0.4852393 3.492568 0.5755259 5.079778 41.13625
109 259 PEM1B WET 6.0532764 0.0001784 -0.0070163 -0.2382195 3.683243 -0.5695206 5.994820 111.52618
110 260 PEM1B WET 8.4192708 0.0002020 -0.0006476 -0.0463253 3.252650 0.1396315 5.185502 21.77407
111 261 PEM1B WET 16.4198696 0.0000544 0.0030899 0.0412626 3.168431 0.0588538 4.392728 86.25028
112 262 PEM1B WET 9.4117961 0.0014237 -0.0194454 -0.8722977 3.963459 -1.0527076 6.106656 65.19433
113 263 PEM1B WET 1.1743341 0.0008806 -0.0180295 -0.7046832 6.699295 -0.8144501 10.819430 69.66031
114 264 PEM1B WET 14.7057380 0.0004240 -0.0094705 -0.2419435 5.951278 -0.5319925 7.317804 71.44786
115 265 PEM1B WET 12.2736165 0.0008273 0.0049082 0.3695282 1.609438 1.0325249 3.159491 78.28107
116 267 PEM1B WET 2.9368660 0.0000726 -0.0039893 -0.4016458 3.121554 0.0598329 6.152415 80.79988
117 268 PEM1B WET 3.0455656 0.0000388 0.0001951 -0.0795868 3.442232 0.1720116 6.408650 76.01599
118 269 PEM1B WET 16.6968852 0.0003200 0.0063764 0.1062392 3.051261 0.3142761 4.271281 54.26737
119 270 PEM1B WET 11.2113903 0.0000632 -0.0018886 -0.0985413 4.779117 -0.1771734 6.398016 64.80353
120 271 PEM1B WET 12.6095905 0.0000018 -0.0002815 0.0105652 5.700297 -0.0581490 7.198441 71.39243
121 272 PEM1B WET 3.5685251 0.0000546 0.0031590 0.4166505 1.803114 0.7244107 4.583109 84.91057
122 273 PEM1B WET 4.3357904 0.0005582 -0.0002515 -0.0511776 2.744830 0.0488949 5.503418 71.11641
123 274 PEM1B WET 1.7859243 0.0000731 0.0038359 0.2770846 1.637591 0.6014039 5.361981 65.44800
124 275 PEM1B WET 1.9202969 0.0000522 0.0004183 0.1060381 2.313359 0.0788698 5.713351 70.33659
125 276 PEM1B WET 3.2124344 0.0000394 -0.0032155 -0.1029353 3.561966 -0.8592746 6.500422 77.38828
126 278 PEM1B WET 18.6311009 0.0002159 -0.0073181 -0.0981326 6.874697 -0.2193890 7.963756 67.02411
127 279 PEM1B WET 22.6508272 0.0006816 -0.0093709 -0.1103445 6.018870 -0.1938638 6.894462 52.04211
128 280 PEM1B WET 14.4358954 0.0000224 0.0004223 0.0136545 3.945940 -0.0217760 5.303166 75.18380
129 281 PEM1B WET 22.9452370 0.0005621 -0.0122778 -0.1316631 5.699676 -0.3921785 6.560691 69.76237
130 282 PEM1B WET 22.3723826 0.0006305 -0.0127583 -0.2351674 4.931269 -0.4254700 5.824944 56.05372
131 296 PEM1B WET 4.5383699 0.0000068 0.0001234 -0.0377635 2.792066 0.0418813 5.326779 58.37490
132 297 PEM1B WET 0.8633994 0.0000120 -0.0007829 -0.3492278 4.002475 0.0724275 8.530938 58.79899
133 300 PEM1B WET 9.6768456 0.0000355 0.0021226 0.0710447 3.276880 -0.0355298 5.046758 84.38342
134 301 PEM1B WET 15.8140836 0.0001379 0.0060091 0.1619183 3.160762 0.3310565 4.425875 85.89872
135 302 PEM1B WET 12.3591955 0.0000308 -0.0027567 -0.0744780 4.382738 -0.1884090 5.902764 57.97769
136 303 PEM1B WET 6.2778230 0.0000235 0.0010346 0.0400108 4.960426 0.0304724 7.168858 84.25110
137 304 PEM1B WET 17.4272946 0.0001726 -0.0057947 -0.0919609 3.942860 -0.2259732 5.104906 42.38535
138 306 PEM1B WET 9.1596157 0.0002320 0.0018620 -0.3558743 2.987630 0.0425056 4.887686 66.96174
139 307 PEM1B WET 4.4428411 0.0000474 0.0029938 0.2580564 3.446530 0.4093315 6.029033 61.92599
140 308 PEM1B WET 15.7359982 0.0001284 -0.0053487 -0.1208234 6.893809 -0.3114266 8.165690 68.09792
141 309 PEM1B WET 3.5005912 0.0000376 -0.0035140 -0.1932876 3.010729 -0.1665194 5.810333 66.12273
142 310 PEM1B WET 3.3192677 0.0000024 -0.0007137 -0.0353210 4.436924 -0.0648230 7.286336 65.02792
143 316 PEM1F WET 2.1553507 0.0000072 -0.0003088 -0.0146712 1.699034 0.3783938 4.988155 51.00723
144 317 PEM1F WET 3.1608266 0.0001058 0.0022985 0.1571500 2.357051 0.6279496 5.277160 58.53384
145 318 PEM1F WET 3.6689787 0.0000181 -0.0011806 -0.0397990 2.907433 -0.1071062 5.687166 36.88445
146 319 PEM1F WET 4.1519502 0.0000963 -0.0030154 -0.3700406 3.326963 -0.4453078 5.993961 38.16423
147 320 PEM1F WET 0.1279381 0.0000000 -0.0001000 -0.5072736 3.140818 -0.8804298 9.247788 55.21976
148 344 PFO4/EM1B WET 1.0018434 0.0000170 -0.0007650 0.0579315 1.609438 0.1624235 5.673115 90.90421
149 346 PFO4/EM1B WET 8.1417921 0.0000303 -0.0000463 -0.0027413 3.434499 -0.0848372 5.379394 86.21484
150 353 PFO4A WET 22.6701998 0.0013001 -0.0167424 -0.5583701 6.123171 -0.7008089 7.012939 42.67944
151 354 PFO4A WET 1.5127774 0.0000113 -0.0007635 -0.3517769 2.972467 -0.2823676 6.649198 30.34791
152 355 PFO4A WET 7.4437860 0.0008719 -0.0127430 -0.2993240 2.387492 -0.5431197 4.663597 67.07518
153 356 PFO4A WET 2.1794471 0.0000235 -0.0022071 -0.4230693 3.144073 -0.5411000 6.429734 31.30891
154 357 PFO4A WET 2.3605340 0.0000114 0.0008044 0.0652817 1.817080 0.2807929 5.030043 31.64187
155 387 PSS1/EM1A WET 4.3694196 0.0000228 0.0030321 0.1395715 8.816212 0.0609757 11.389011 69.90208
156 388 PSS1/EM1A WET 3.9143852 0.0000115 0.0016447 0.1286329 2.638092 0.2194031 5.326025 103.97805
157 389 PSS1/EM1A WET 3.4191994 0.0000018 -0.0002759 -0.0289863 3.536317 -0.0629340 6.354241 69.26847
158 390 PSS1/EM1A WET 4.7174108 0.0000019 0.0005179 0.0831351 2.553316 0.0856084 5.048167 77.28357
159 391 PSS1/EM1A WET 4.4582056 0.0000222 -0.0018208 -0.0442225 4.353220 -0.3091261 6.905819 78.61685
160 393 PSS1C WET 1.4829315 0.0000523 0.0029175 0.0927006 8.379578 0.0090905 12.090939 71.92680
161 394 PSS1C WET 5.8652806 0.0001994 -0.0045938 -0.3224535 8.123527 -1.0789593 10.477797 57.58499
162 395 PSS1C WET 6.0997759 0.0000564 -0.0026373 -0.0085150 2.275943 -0.5124282 4.517496 61.61575
163 396 PSS1C WET 9.4680891 0.0002204 -0.0056209 -0.3242363 5.336090 -0.7885025 7.127541 69.87168
164 397 PSS1C WET 2.3833904 0.0000305 0.0017275 0.0887825 2.726402 -0.0004238 5.934734 99.77488
165 398 PUBH WET 3.4980341 0.0000397 0.0030031 0.4863809 1.609438 0.3824549 4.469725 58.20656
166 399 PUBH WET 11.5124119 0.0000999 -0.0053245 -0.0907712 3.565142 -0.4455864 5.156530 73.90394
167 400 PUBH WET 0.9985954 0.0000683 0.0057851 0.3032865 1.609438 1.3973911 5.670735 76.49144
168 401 PUBH WET 2.8660065 0.0000165 -0.0022885 -0.2132429 8.830673 -0.6885241 11.828138 79.29595
169 402 PUBH WET 3.2017004 0.0003149 0.0096101 0.6370276 1.609438 0.8443615 4.512695 72.77826
170 431 PUBH WET 0.5929725 0.0000008 0.0000334 0.3319307 1.628968 -0.3426140 6.412908 65.29253
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172 433 PUBH WET 0.1588023 0.0000002 0.0002317 0.6695845 1.809864 1.0832334 7.794400 51.03313
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347 723 UPL 12.7083229 0.0003195 -0.0086294 -0.1740441 3.983838 -0.1933928 5.497281 84.20320
348 725 UPL 32.8483594 0.0001287 0.0009157 0.0017759 3.802535 -0.0108666 4.240104 66.75720
349 726 UPL 11.1535391 0.0000858 -0.0017572 -0.0246558 3.472616 -0.1084244 5.107736 61.95038
350 728 UPL 40.9675195 0.0004494 -0.0042351 -0.0273074 3.537760 0.1064576 3.679968 63.50911
351 729 UPL 20.2796794 0.0017674 0.0247371 0.4394948 1.850517 0.9545356 2.882083 62.17902
352 732 UPL 24.6538865 0.0000319 -0.0019703 -0.0239480 5.055450 -0.0812164 5.834352 51.14380
353 733 UPL 20.3300538 0.0001511 -0.0065888 -0.1450693 5.151078 -0.1280137 6.147484 49.34172
354 736 UPL 16.7493986 0.0000413 0.0031252 -0.0594832 4.767513 0.0269731 5.969432 52.43880
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356 738 UPL 32.2201082 0.0009672 -0.0056582 -0.0592510 5.103548 -0.1126704 5.567096 69.96988
357 739 UPL 42.1705791 0.0001450 0.0045993 0.0394851 3.689172 0.0370354 3.788103 53.41489
358 740 UPL 40.0837761 0.0017361 -0.0118758 -0.0977609 3.571636 -0.2354718 3.744858 52.21373
359 741 UPL 36.4555445 0.0003177 0.0060982 0.0809983 2.516610 0.0093118 2.819411 24.56261
360 742 UPL 15.1293431 0.0000187 0.0017577 0.0605286 2.960561 0.1325826 4.268543 73.74798
361 743 UPL 31.0098934 0.0000045 -0.0001087 -0.0048852 4.022810 0.0210954 4.531839 66.33821
362 744 UPL 27.3457137 0.0000548 -0.0020023 0.0131085 3.884635 -0.1436855 4.544098 49.24796
363 746 UPL 35.4712844 0.0000632 0.0030210 0.0299773 4.695026 0.0448741 5.033951 38.65751
364 748 UPL 27.5624411 0.0014562 0.0182746 0.2030336 4.638471 0.2570512 5.292263 64.38068
365 749 UPL 34.6607378 0.0003358 -0.0059154 -0.0074647 4.875590 -0.1285749 5.244873 46.28945
366 753 UPL 51.6010481 0.0016585 0.0040364 -0.0439945 5.029839 0.0208325 4.797091 55.47476
367 754 UPL 17.7579859 0.0000014 -0.0002742 -0.0040485 7.092152 -0.0848841 8.230787 65.19685
368 755 UPL 2.7696502 0.0000419 0.0039956 0.1593024 3.746412 0.2506353 6.776853 27.59347
369 756 UPL 19.8263746 0.0000256 0.0006913 -0.0019484 3.908715 0.0529051 4.929891 68.71510
370 758 UPL 8.3687897 0.0001191 -0.0067633 -0.1891304 3.588460 -0.3292017 5.523491 45.24174
371 760 UPL 28.8046403 0.0002545 -0.0077502 -0.0698381 5.783206 -0.2666277 6.382109 56.42723
372 761 UPL 35.0219063 0.0001471 -0.0021389 0.0172191 5.566024 -0.0666595 5.921636 66.46685
373 762 UPL 11.7855297 0.0000514 -0.0005022 0.0604943 2.449875 0.1893960 4.017774 57.26250
374 763 UPL 34.5058370 0.0003005 0.0065405 0.0392476 4.079997 0.1491547 4.454930 57.42583
375 764 UPL 3.5184494 0.0001345 -0.0069525 -0.3981479 4.178788 -0.9598800 6.972259 31.73838
376 765 UPL 18.7826227 0.0002769 -0.0038387 -0.0641353 3.281678 -0.0936420 4.360232 68.39593
377 766 UPL 18.4220925 0.0000643 0.0008982 -0.0304910 5.375938 -0.0473903 6.475818 51.57778
378 767 UPL 8.3787804 0.0000124 -0.0004096 -0.0030927 3.984695 -0.0223915 5.901434 60.48452
379 768 UPL 50.5981633 0.0019537 0.0005937 0.0764949 2.714026 0.2248068 2.516585 70.82626
380 769 UPL 37.0956864 0.0042179 -0.0268578 -0.1841649 7.166089 -0.6612165 7.446457 29.61672
381 770 UPL 25.9018641 0.0003681 -0.0062574 -0.0447350 4.827454 -0.2559889 5.550084 39.67642
382 771 UPL 15.1942134 0.0001014 -0.0043616 -0.0574834 5.348540 -0.0718287 6.652322 43.86373
383 774 UPL 22.9807263 0.0003993 0.0071898 0.0701238 3.767414 0.1893086 4.627844 60.57986
384 775 UPL 18.2647393 0.0000289 -0.0001206 -0.0131026 4.182373 0.0978959 5.291928 57.99252
385 776 UPL 43.6473433 0.0011683 0.0069135 0.1100324 4.410243 0.1172667 4.457836 34.13917
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387 778 UPL 23.7707061 0.0001112 -0.0006814 -0.0670944 5.142479 -0.1445991 5.962599 46.16231
388 780 UPL 37.3062742 0.0002981 -0.0040940 -0.1108116 5.583823 -0.1619136 5.856107 64.98811
389 781 UPL 33.8368552 0.0002505 0.0057163 0.1250350 2.675569 0.2053138 3.075841 56.79694
390 785 UPL 28.0159747 0.0000825 0.0016603 0.0606605 3.618758 0.0830589 4.250885 41.03379
391 787 UPL 39.8080579 0.0091317 -0.0135475 -0.3670068 3.121176 -0.3640141 3.331551 37.37079
392 789 UPL 17.2348655 0.0003020 -0.0081881 -0.0790169 6.531389 -0.2513143 7.702214 68.82034
393 790 UPL 28.8917729 0.0000959 -0.0042193 -0.0567500 4.866820 -0.1573809 5.461465 45.50578
394 791 UPL 38.5612367 0.0009351 -0.0007338 0.0121317 3.364935 -0.0683306 3.591952 73.68752
395 792 UPL 24.9172073 0.0002587 -0.0072512 -0.0666010 4.075618 -0.2452432 4.842531 55.79820
396 793 UPL 41.6202147 0.0005404 -0.0073913 -0.1232275 4.972756 -0.1656440 5.091082 50.39422
397 794 UPL 35.0935406 0.0002873 -0.0055209 -0.0569498 4.378450 -0.0848575 4.732523 62.96260
398 796 UPL 35.5261681 0.0000940 0.0032987 0.0168246 4.357080 0.0967469 4.694015 47.65184
399 797 UPL 34.6428032 0.0020428 -0.0118764 -0.1271561 4.179981 -0.2317578 4.550482 73.13025
400 799 UPL 12.1411815 0.0000963 -0.0026237 0.0193056 5.178637 -0.0203985 6.716029 64.47905
401 800 UPL 9.8797468 0.0004815 -0.0111177 -0.2904946 8.770168 -0.5072938 10.526725 68.64540
402 802 UPL 24.6187941 0.0000501 -0.0018367 -0.0326433 5.925069 -0.0882294 6.705453 48.61513
403 803 UPL 39.6438670 0.0025983 0.0025146 0.0791574 3.569182 0.1245145 3.764889 77.85546
404 804 UPL 26.8321789 0.0000984 0.0020499 0.0158700 4.485231 0.0453185 5.167870 48.38497
405 806 UPL 17.3300214 0.0002488 0.0080628 0.1722952 3.782582 0.1563451 4.949208 40.95404
406 807 UPL 22.5168559 0.0000309 0.0015417 0.0544486 4.191111 0.0031060 5.071764 54.69492
407 808 UPL 9.4211543 0.0001136 -0.0058794 -0.2079419 2.931330 -0.3698279 4.753470 76.55915
408 809 UPL 22.0374648 0.0001036 -0.0014604 -0.1037149 3.922254 -0.0706825 4.826714 43.88567
409 811 UPL 15.9180685 0.0002874 0.0089256 0.2599998 1.636293 0.1828464 2.896934 39.68217
410 812 UPL 29.6751288 0.0009519 -0.0100379 -0.0934148 5.240588 -0.2427740 5.804743 64.29099
411 813 UPL 18.5632332 0.0005200 0.0126470 0.1982301 2.352794 0.4155608 3.444252 71.76325
412 814 UPL 26.5837003 0.0001184 -0.0026921 -0.0196128 4.176356 -0.0916474 4.869075 67.65946
413 816 UPL 16.8762203 0.0000382 0.0019447 0.0385241 3.938672 0.0489303 5.131530 61.09092
414 817 UPL 11.1754764 0.0005440 -0.0097706 -0.3520234 6.179389 -0.3309341 7.882852 25.44542
415 818 UPL 30.7142779 0.0004455 0.0095688 0.1479723 3.097970 0.2622974 3.620775 60.44882
416 819 UPL 22.0941684 0.0002297 0.0014746 -0.0087153 6.433158 -0.0416422 7.336659 46.24575
417 820 UPL 27.5444826 0.0009882 -0.0125950 -0.0956672 3.617696 -0.1352137 4.271922 50.48597
418 822 UPL 40.4541918 0.0009491 0.0013586 -0.0067228 4.279647 -0.0799369 4.439286 48.00922
419 823 UPL 15.4463284 0.0020039 0.0253551 0.4101488 1.612880 0.5979597 2.900148 39.60108
420 828 UPL 35.2674270 0.0001737 0.0014629 0.0038571 3.424806 0.1007150 3.771294 57.84911
421 829 UPL 27.6079568 0.0000435 0.0024978 0.0139774 3.218834 0.0871699 3.867224 54.40943
422 830 UPL 15.4218648 0.0000805 0.0039581 0.0576159 5.121995 0.0900540 6.410254 42.22585
423 832 UPL 16.6860157 0.0000779 -0.0016963 -0.0154228 4.630794 -0.1270814 5.835952 66.31003
424 837 UPL 12.2016609 0.0000706 -0.0010363 -0.0085972 4.400827 -0.0026361 5.933255 84.01993
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426 844 UPL 37.9173455 0.0010570 -0.0085592 -0.0870556 5.983091 -0.2044137 6.233258 19.02824
427 845 UPL 31.3764125 0.0004644 0.0086863 0.1094434 2.534827 0.3275689 3.030727 61.70566
428 846 UPL 13.3835798 0.0001146 -0.0024486 -0.0474418 5.401420 -0.0267059 6.845855 77.34976
429 848 UPL 17.1403844 0.0001728 -0.0072408 -0.1070243 6.957361 -0.1948004 8.138995 46.80411
430 849 UPL 14.8181816 0.0000890 -0.0055672 -0.0834404 6.029358 -0.1928755 7.361931 69.69374
431 851 UPL 31.7286757 0.0001044 0.0026037 0.0229074 5.458371 -0.0259913 5.939176 44.55873
432 853 UPL 26.3195183 0.0006451 -0.0025360 -0.1323762 4.563984 0.0145717 5.270603 46.35818
433 854 UPL 17.9452528 0.0010595 0.0024740 0.1410525 2.985870 0.2310624 4.128288 57.77000
434 858 UPL 36.5626317 0.0003065 -0.0003112 0.0264971 2.951118 0.0018980 3.250525 57.59493
435 859 UPL 26.1280293 0.0000876 0.0017371 -0.0012783 3.321257 -0.0282564 4.033911 67.16967
436 863 UPL 16.4289639 0.0006403 0.0146451 0.2030974 2.859125 0.5134800 4.084510 51.48870
437 864 UPL 6.7019480 0.0000383 0.0000211 0.0830713 3.018091 -0.1149345 5.162300 46.28153
438 865 UPL 8.6398775 0.0001668 -0.0040274 -0.1179596 3.285907 -0.1864260 5.212356 40.33705
439 868 UPL 18.5998230 0.0000508 -0.0016337 -0.0251424 4.344084 0.0076480 5.433535 34.00651
440 869 UPL 17.8536288 0.0001405 0.0053743 0.1043176 2.203644 0.1239167 3.347062 44.73129
441 870 UPL 30.5333598 0.0004074 0.0079122 0.1348833 2.888789 0.1721820 3.417298 60.07147
442 871 UPL 45.5637308 0.0029556 -0.0095488 -0.0748283 5.152455 -0.0329628 5.132775 74.76081
443 872 UPL 20.9713003 0.0004684 -0.0087308 -0.0819190 5.862327 -0.1241472 6.821872 72.60018
444 874 UPL 55.9397622 0.0179267 -0.0174085 -0.4091203 4.049525 -0.5858539 3.649075 55.77892
445 875 UPL 25.9733427 0.0000601 -0.0041360 -0.0364103 7.591303 -0.0835984 8.310606 39.61803
446 877 UPL 28.5362559 0.0024494 0.0228724 0.2451981 2.088509 0.4507573 2.699313 63.84552
447 878 UPL 29.6368253 0.0002002 -0.0051594 -0.0381794 3.781834 -0.1174042 4.346670 56.19830
448 879 UPL 36.7908400 0.0019583 0.0132147 0.0929045 2.410503 0.2910602 2.701441 28.30194
449 881 UPL 33.7547206 0.0020089 0.0154036 0.2429708 4.328776 0.3909740 4.747434 62.59299
450 882 UPL 25.9395735 0.0000431 0.0005786 0.0184455 4.381891 -0.0036142 5.102785 56.22049
451 884 UPL 32.7043691 0.0000433 0.0019364 0.0101335 3.241992 0.0458196 3.685056 67.84812
452 885 UPL 37.1651137 0.0005197 0.0066676 0.0787472 4.308870 0.2179986 4.586316 67.97814
453 887 UPL 22.4499733 0.0000289 -0.0005902 -0.0158737 4.521385 -0.0173584 5.405672 42.83620
454 888 UPL 35.6645325 0.0011166 -0.0122462 -0.0799911 5.165700 -0.4739469 5.498083 47.92155
455 889 UPL 18.3931124 0.0001588 -0.0033200 -0.1292380 4.960726 -0.1257199 6.066368 53.89003
456 891 UPL 30.1230139 0.0000703 0.0004225 -0.0195967 4.219691 0.0733211 4.764270 63.42454
457 893 UPL 51.7913310 0.0031853 0.0122535 0.1432913 3.593027 0.1742371 3.352536 46.69434
458 894 UPL 21.5891135 0.0001576 0.0040782 0.0374270 2.731915 0.0423944 3.660006 31.58335
459 896 UPL 35.3165932 0.0000256 -0.0012746 -0.0134152 4.184061 -0.0176947 4.528768 58.90145
460 897 UPL 28.8518665 0.0002580 0.0068108 0.0598934 3.427716 0.2037587 4.023933 53.15236
461 899 UPL 25.8139988 0.0001556 0.0004405 -0.0055579 4.906167 0.0656235 5.633344 61.66092
462 901 UPL 35.7842589 0.0006080 0.0111242 0.1096033 3.877503 0.1739785 4.204949 63.57062
463 902 UPL 44.4702852 0.0005665 -0.0055460 -0.1015774 4.346776 -0.1187675 4.365260 52.36200
464 904 UPL 8.7288183 0.0011195 -0.0139777 -0.4216759 7.578875 -0.6995070 9.849196 36.58600
465 906 UPL 42.8731998 0.0002989 0.0026206 0.0310893 4.251165 0.0187058 4.325710 61.46393
466 908 UPL 33.7450619 0.0000527 0.0005488 -0.0146716 4.454866 -0.0199531 4.858451 61.50819
467 909 UPL 19.9677228 0.0000632 0.0001499 -0.0258934 3.463806 0.0786657 4.476310 64.55765
468 910 UPL 23.5073726 0.0001265 0.0042198 -0.0059083 3.360548 0.0367958 4.193268 58.60084
469 911 UPL 33.0243971 0.0009223 -0.0139728 -0.1746563 4.218194 -0.2492020 4.650550 47.54003
470 912 UPL 25.0740410 0.0001722 -0.0058972 -0.0581185 7.610020 -0.0803709 8.369729 51.58605
471 914 UPL 58.6531388 0.0030783 0.0067959 0.1903153 2.717342 0.3538324 2.220627 56.87700
472 915 UPL 10.4204701 0.0000489 -0.0037058 -0.1407336 4.024493 -0.1203032 5.722078 59.37639
473 916 UPL 39.5471460 0.0009883 0.0013844 -0.0174811 5.325939 0.0836766 5.517545 39.68711
474 917 UPL 27.0637502 0.0000000 -0.0000081 -0.0001117 5.305665 0.0051664 5.977192 63.70301
475 918 UPL 46.9798271 0.0046638 0.0237724 0.1230167 2.212292 0.4522243 2.142241 75.27634
476 920 UPL 21.4878863 0.0001149 0.0060129 0.0329606 3.353941 0.1374589 4.286781 42.21175
477 921 UPL 27.3283340 0.0000287 -0.0021422 -0.0221580 4.542525 -0.1453979 5.202731 72.57197
478 922 UPL 14.9569760 0.0000443 -0.0034692 -0.0839258 6.619559 -0.2249871 7.946640 69.59081
479 923 UPL 21.6028176 0.0001390 -0.0064287 -0.0848490 5.912255 -0.1880236 6.840482 46.62688
480 924 UPL 16.0791346 0.0000784 -0.0044237 -0.1296580 3.577803 -0.0711503 4.836083 42.35191
481 925 UPL 38.1357351 0.0041023 0.0110072 0.0859168 2.266501 0.3868685 2.510473 67.74837
482 929 UPL 42.8503931 0.0001449 0.0011999 0.0102655 3.576265 -0.0361920 3.651422 54.27072
483 930 UPL 34.9675454 0.0000791 -0.0033295 -0.0121953 4.165906 -0.0900643 4.523544 62.15423
484 932 UPL 39.6696093 0.0001697 -0.0014278 -0.0431576 3.758579 0.0159481 3.945765 58.47987
485 933 UPL 33.3575076 0.0002093 0.0051339 0.0051119 4.456059 0.0690452 4.874469 46.14132
486 934 UPL 25.4726886 0.0001477 -0.0024201 -0.0410704 4.309711 -0.0522425 5.053840 38.49037
487 938 WET 2.3003450 0.0000178 -0.0006072 -0.0283446 2.810449 0.3040790 6.027315 70.97816
488 939 UPL 26.8166167 0.0004330 0.0040559 0.0236881 3.614398 0.1448169 4.298598 90.79218
489 940 UPL 30.0467293 0.0004970 -0.0103375 -0.0977481 4.686259 -0.0923914 5.235512 62.29997
490 942 UPL 17.2970109 0.0001193 0.0005920 -0.0263188 5.317487 -0.0480845 6.485709 50.02290
491 943 UPL 10.9435793 0.0000121 0.0012066 0.0170516 4.287187 0.0737391 5.930609 67.55728
492 944 UPL 36.0624129 0.0001028 0.0000779 -0.0072040 4.088724 -0.0159374 4.405916 56.27621
493 946 UPL 38.6120469 0.0023176 -0.0215509 -0.1825479 4.555854 -0.4173037 4.780889 49.95957
494 947 UPL 21.5758449 0.0000929 -0.0020211 -0.0228810 5.758029 -0.0098801 6.685969 75.42447
495 948 UPL 33.2844257 0.0014252 -0.0079834 -0.0917840 4.145654 -0.1258252 4.569905 62.20424
496 951 UPL 32.3367605 0.0003666 -0.0084713 -0.0713808 4.161854 -0.1760853 4.620037 39.61953
497 953 UPL 18.4081428 0.0000496 -0.0031569 -0.0426691 5.925639 -0.1443195 7.025891 52.50760
498 954 UPL 18.6501412 0.0005107 -0.0060307 -0.0927395 7.668713 -0.0073571 8.756012 56.87337
499 955 UPL 28.2726942 0.0000406 -0.0025857 -0.0175388 4.143194 -0.0214006 4.763600 44.78071
500 956 UPL 18.0779583 0.0001631 -0.0045308 -0.0803851 8.514749 -0.2648003 9.636785 54.60104
502 958 UPL 12.3262863 0.0002083 0.0061509 0.3826277 1.806221 0.1405290 3.334420 46.01606
503 959 UPL 25.0517227 0.0004781 -0.0098613 -0.1293186 7.289515 -0.1725689 8.050452 41.96133
504 960 UPL 2.1185002 0.0000553 -0.0033800 -0.2389151 2.773650 -0.4255921 6.110984 86.09883
505 961 UPL 29.7278658 0.0000595 -0.0020427 -0.0079466 5.495277 -0.0559282 6.056830 55.63226
506 962 UPL 24.8385494 0.0002887 0.0096223 0.1014159 3.348887 0.1984121 4.122339 60.92061
507 963 UPL 15.0068382 0.0001407 0.0046028 0.2515033 1.836102 0.1683811 3.164867 55.39922
508 964 UPL 26.2338907 0.0002887 -0.0024133 0.0257671 6.614785 -0.0992268 7.324944 47.30550
509 965 UPL 29.0273704 0.0001484 -0.0057174 -0.0599992 6.949248 -0.1700611 7.538754 100.53424
510 966 UPL 32.5351276 0.0003537 -0.0084007 -0.0670264 4.868441 -0.1728523 5.318416 56.87132
511 967 UPL 14.5734369 0.0000402 -0.0005092 0.0203788 6.072819 -0.0484633 7.420388 79.62517
512 969 UPL 17.4334392 0.0001182 -0.0052214 -0.0794487 3.820461 -0.0602473 4.989796 50.38097
513 970 UPL 32.8335669 0.0000501 0.0002998 -0.0064521 5.001011 0.0038026 5.439129 55.71019
514 971 UPL 30.1226679 0.0002363 0.0031296 0.0232908 4.539617 0.0907698 5.084542 51.45345
515 972 UPL 12.7527478 0.0001587 0.0031194 0.0763874 3.048436 0.1144550 4.538083 44.02117
516 973 UPL 30.6912718 0.0000000 -0.0000152 -0.0003130 4.237042 -0.0173616 4.758675 59.32227
517 974 UPL 23.3119596 0.0001142 -0.0012867 -0.0350113 3.370258 -0.1183798 4.215012 50.24306
518 976 UPL 33.6784572 0.0018758 -0.0178339 -0.3491460 3.966382 -0.5173501 4.372984 41.79805
519 978 UPL 33.8881480 0.0005926 0.0108418 0.1068860 2.413803 0.2960311 2.812105 62.88721
520 980 UPL 14.5633082 0.0001026 -0.0063175 -0.1191670 5.202880 -0.1959024 6.556216 56.22082
521 981 UPL 28.8684236 0.0005265 -0.0044273 -0.0045247 4.807377 -0.1710121 5.404534 47.23608
522 982 UPL 26.6745937 0.0004689 0.0066150 0.0638484 4.211037 0.2856805 4.899851 63.20652
523 983 UPL 22.5277876 0.0000478 0.0016287 0.0114226 5.199355 -0.0550533 6.079554 68.49846
524 984 UPL 27.4363006 0.0004882 0.0072104 0.0992579 2.468774 -0.0001859 3.126608 71.23412
525 986 UPL 20.7582426 0.0004991 -0.0038121 -0.0379749 2.607512 -0.0477306 3.580515 57.05567
526 987 UPL 17.0296701 0.0001050 -0.0051544 -0.0795353 5.952939 -0.2861941 7.137455 52.66050
527 988 UPL 22.5379469 0.0000163 -0.0002214 0.0031631 4.326595 -0.0444194 5.206167 70.30623
528 989 UPL 45.7022891 0.0002157 0.0024558 0.0728199 2.917424 0.2214988 2.892875 75.67044
529 990 UPL 34.5210848 0.0012958 -0.0038026 -0.0068577 3.050735 0.0640404 3.425320 59.44569
530 991 UPL 14.6798176 0.0000447 0.0007998 0.0208302 3.446614 0.0122826 4.787164 82.40481
531 993 UPL 50.4870388 0.0004908 -0.0048121 -0.0030741 2.946702 -0.0585919 2.753890 71.68054
532 994 UPL 12.2944899 0.0005088 0.0118393 0.1726239 2.654599 0.1365375 4.181163 56.40040
533 995 UPL 23.3736551 0.0001363 -0.0051853 -0.0569274 5.531488 -0.1287493 6.370590 44.36733
534 997 UPL 20.6806044 0.0003827 -0.0045436 -0.0911561 4.446878 -0.2153623 5.423781 65.16702
535 999 UPL 18.1161730 0.0000100 0.0008589 -0.0305337 5.048628 0.0285617 6.165986 61.57717
536 1000 UPL 17.9389210 0.0002468 0.0081213 0.0545202 3.180696 0.3472959 4.312542 60.48317
537 1001 UPL 27.3455054 0.0002994 0.0073523 0.0579197 3.848503 0.0309020 4.508217 44.99615
538 1002 UPL 25.9396158 0.0005813 -0.0117337 -0.1936408 5.191776 -0.3767147 5.919287 69.96826
539 1003 UPL 18.4902689 0.0012757 0.0157065 0.1584371 2.446429 0.2612130 3.545738 45.32217
540 1004 UPL 32.1665209 0.0001074 0.0011648 0.0071173 3.412527 -0.0035443 3.876654 52.26962
541 1005 UPL 13.4351050 0.0000994 0.0050290 0.1220263 2.705801 0.1967481 4.138118 61.89322
542 1006 UPL 24.9280359 0.0000951 0.0046268 0.0379804 3.956610 0.1581013 4.723385 49.54287
543 1007 UPL 31.4070083 0.0000934 -0.0018468 -0.0406568 4.322341 -0.0181308 4.817145 61.93785
544 1008 UPL 22.1000907 0.0005533 0.0056865 0.0833859 6.596439 0.1211888 7.503474 49.99685
545 1009 UPL 49.9531460 0.0005140 -0.0006394 -0.0377549 4.405894 0.0319183 4.231845 32.21017
547 1011 UPL 11.0371464 0.0000568 -0.0008515 -0.0745874 3.786282 -0.1566180 5.422364 35.51735
548 1012 UPL 28.8459686 0.0001814 -0.0033542 -0.0091138 3.831072 -0.1146887 4.427535 59.49186
549 1014 UPL 31.5452376 0.0005938 0.0027272 0.0518245 2.839771 0.0667776 3.328762 63.64684
550 1015 UPL 34.0162107 0.0036017 0.0226101 0.1921513 2.333098 0.2326700 2.726329 65.00746
551 1016 UPL 22.0464854 0.0000739 -0.0003642 0.0014780 5.225232 -0.0380790 6.129431 67.00172
552 1019 UPL 11.2892323 0.0007252 -0.0138651 -0.1784704 7.087426 -0.3299831 8.713188 34.63729
553 1020 UPL 34.4745039 0.0012369 -0.0107738 -0.1332029 7.952772 -0.1178624 8.328910 47.28544
554 1021 UPL 14.3414755 0.0000810 -0.0054887 -0.1369960 6.981221 -0.1342081 8.351432 66.75017
555 1022 UPL 23.6950856 0.0000217 -0.0017064 -0.0083421 4.427438 -0.1129330 5.251019 77.57574
556 1024 UPL 15.2469773 0.0000067 0.0010381 0.0175925 3.877233 0.0646420 5.177081 58.29667
557 1026 UPL 14.5902191 0.0002765 -0.0095211 -0.1540021 6.435272 -0.2446089 7.789750 71.95851
558 1029 UPL 22.8482694 0.0000978 0.0009074 0.0330675 6.389400 0.0283547 7.256609 34.37480
559 1030 UPL 32.4284890 0.0004542 -0.0010826 0.0265662 3.957541 0.0241373 4.411267 61.52971
560 1031 UPL 15.3179159 0.0000989 0.0003287 0.0311626 4.671710 -0.0476930 5.970235 57.51848
561 1032 UPL 22.7208435 0.0000291 -0.0016040 -0.0079357 4.228059 -0.0115486 5.099881 37.86014
562 1033 UPL 23.3317024 0.0003850 0.0092270 0.1480089 2.528956 0.3996658 3.372365 46.85671
563 1034 UPL 31.6599494 0.0009559 0.0097844 0.0482545 2.971227 0.3281200 3.456539 62.54884
564 1036 UPL 29.4652507 0.0002183 0.0056694 0.0767632 3.918349 0.1779806 4.490400 62.36087
565 1038 UPL 26.3258045 0.0000546 -0.0019094 -0.0133420 5.662067 0.0111489 6.365893 90.89504
566 1039 UPL 24.0926318 0.0000937 0.0017774 -0.0415226 6.376731 0.1140868 7.186074 53.29085
567 1041 UPL 25.7780002 0.0001054 -0.0004490 0.0043554 6.052242 0.0310248 6.780160 66.44125
568 1042 UPL 30.5706966 0.0003367 0.0052486 0.1232726 5.213567 0.1452032 5.741642 51.56131
569 1043 UPL 43.8475034 0.0015239 -0.0110135 -0.0973728 5.894169 -0.2307214 5.934696 65.61160
570 1044 UPL 28.5839775 0.0000445 -0.0002271 -0.0183604 4.196902 -0.0622740 4.804200 55.70666
571 1045 UPL 31.4185387 0.0001601 -0.0036586 -0.0342077 3.502945 -0.0981771 3.996022 58.57654
572 1047 UPL 9.8442204 0.0000085 0.0005742 0.0090412 4.827530 0.0487748 6.580108 64.04039
573 1050 UPL 21.5968249 0.0010284 0.0158256 0.2406850 3.926748 0.4914803 4.867640 45.35344
574 1052 UPL 29.8810301 0.0000244 -0.0015900 0.0036589 7.685087 -0.0616893 8.239231 37.59465
575 1053 UPL 18.5824955 0.0002040 0.0083627 0.1341196 3.106712 0.5587476 4.198546 67.02020
576 1055 UPL 40.0283486 0.0001151 0.0032047 0.0202661 4.093433 0.0154411 4.267924 51.43933
577 1057 UPL 31.5099487 0.0012900 0.0143335 0.1290468 2.902341 0.2715749 3.394197 53.71278
578 1059 UPL 37.7722737 0.0001213 0.0033987 0.0668534 3.800689 0.2065869 4.055853 62.15176
579 1060 UPL 23.9976139 0.0001390 -0.0050744 -0.0448115 5.462293 -0.1439606 6.272312 42.65722
580 1061 UPL 10.4220029 0.0005746 -0.0081572 -0.1327083 3.139138 -0.0861151 4.835133 69.84684
581 1063 UPL 25.6029165 0.0002518 0.0087641 0.0932234 6.640007 0.1493833 7.379453 64.46751
582 1064 UPL 16.8820841 0.0000400 0.0019144 0.0009901 6.684465 -0.0085080 7.877333 42.36479
583 1068 UPL 13.2688937 0.0000142 -0.0003994 -0.0225043 3.781580 -0.0122815 5.227351 51.79165
584 1071 UPL 24.8481623 0.0003197 -0.0083966 -0.2112604 4.876885 -0.2984898 5.650890 48.85103
585 1072 UPL 9.4195046 0.0013354 -0.0125617 -0.3205367 2.734660 -0.4687182 4.602724 58.58467
586 1074 UPL 17.6740738 0.0000273 0.0008657 0.0179312 5.384370 0.0002125 6.529052 62.73501
587 1075 UPL 7.6798729 0.0000398 0.0011230 0.0943091 2.629343 0.2235661 4.638446 61.52600
588 1076 UPL 31.4723895 0.0002002 -0.0061131 -0.0523075 4.740050 -0.1306885 5.230955 88.25693
589 1077 UPL 47.9944494 0.0003892 -0.0045440 -0.0161652 3.859921 -0.0647729 3.755134 75.31126
590 1078 UPL 25.9887791 0.0001331 -0.0020436 -0.0174519 6.094957 -0.2185044 6.815364 68.03629
591 1079 UPL 13.5865875 0.0000745 -0.0043986 -0.0603643 7.092562 -0.1667709 8.513124 58.13739
592 1080 UPL 17.0495412 0.0000302 0.0011834 0.0759351 3.275388 0.1121596 4.458362 80.50760
593 1082 UPL 28.1458110 0.0008351 -0.0056125 0.0020696 5.225823 -0.1174667 5.851897 66.36618
594 1083 UPL 43.4246782 0.0012321 -0.0107012 -0.2224544 4.411495 -0.2447816 4.466760 63.58169
595 1084 UPL 21.4187383 0.0006272 -0.0069149 -0.0914155 7.613849 -0.1559749 8.551816 72.81740
596 1085 UPL 32.9032684 0.0002439 -0.0049048 -0.0734266 5.456711 -0.0149053 5.892665 27.71500
597 1086 UPL 17.8291425 0.0010341 -0.0161993 -0.3187924 3.808063 -0.8686092 4.972223 49.49786
598 1087 UPL 33.1752418 0.0005394 -0.0076966 -0.0993039 4.149641 -0.0883559 4.574855 47.33923
599 1088 UPL 14.3034383 0.0000698 0.0028504 0.0667220 3.224996 0.2466801 4.591877 43.65742
600 1089 UPL 20.9689175 0.0000477 0.0013089 0.0553738 4.013450 -0.0176910 4.974952 56.02180
601 1091 UPL 38.2935101 0.0003611 -0.0023991 -0.0358753 3.557944 -0.0573925 3.794777 49.42801
602 1092 UPL 2.4739628 0.0000787 -0.0035947 -0.4461487 5.584321 -0.5366917 8.835469 35.71932
603 1095 UPL 23.5437811 0.0000677 0.0027948 -0.0058050 4.523164 0.1324957 5.357180 61.04798
604 1096 UPL 32.2704942 0.0003381 0.0081308 0.1874736 2.848745 0.1082471 3.309161 45.80932
605 1097 UPL 39.3158826 0.0000720 -0.0005295 -0.0090338 4.974467 -0.0488388 5.174216 36.19977
606 1098 UPL 39.8547407 0.0004254 -0.0075944 -0.0788047 5.962766 -0.1629517 6.143460 30.10805
607 1100 UPL 64.9392430 0.0064393 0.0162063 0.1743704 3.116549 0.2413879 2.351765 59.44955
608 1101 UPL 18.1778002 0.0001553 0.0021297 0.0336492 5.335636 -0.0292739 6.449589 64.36283
609 1102 UPL 21.1575444 0.0006754 0.0034171 -0.0216363 2.713030 0.2697166 3.674724 61.41019
610 1104 UPL 20.0284708 0.0001496 -0.0003457 0.0157027 4.019227 -0.1121131 5.033547 31.06428
611 1105 UPL 35.4722745 0.0019379 0.0186435 0.1830171 2.012583 0.2331149 2.351601 45.72304
612 1106 UPL 44.5106660 0.0008736 0.0072507 0.0762866 3.821025 0.1429736 3.838149 63.96226
613 1108 UPL 43.6439161 0.0001534 -0.0028757 -0.0474357 3.919558 0.0025941 3.967097 60.67178
614 1109 UPL 27.8977511 0.0003833 0.0096906 0.1028350 4.549853 0.2455636 5.187378 52.95070
615 1110 UPL 26.7568487 0.0005808 0.0100407 0.1279141 3.700222 0.1357447 4.386310 36.59211
616 1112 UPL 26.4777426 0.0001662 -0.0021558 -0.0219401 3.142254 0.0109754 3.842203 54.42602
617 1113 UPL 37.2469370 0.0005615 -0.0058436 -0.0683850 6.940371 -0.0893391 7.214535 54.56906
618 1114 UPL 24.9348472 0.0001676 0.0029733 0.0685647 4.148212 -0.0040768 4.915311 70.61870
619 1115 UPL 2.9671699 0.0000084 -0.0004634 0.2400015 1.678644 0.0160816 4.673616 67.80321
620 1116 UPL 31.7484138 0.0001208 -0.0019126 -0.0299377 3.539965 -0.0116087 4.020207 66.54765
621 1117 UPL 14.8035973 0.0036276 -0.0262699 -0.4001343 7.266086 -0.8540947 8.645610 72.92679
622 1118 UPL 46.0929519 0.0001980 -0.0012587 -0.0130441 4.105498 -0.0138526 4.067321 60.16260
623 1119 UPL 9.1122676 0.0002137 0.0086209 0.1531745 2.395570 0.5018727 4.226816 66.34494
624 1121 UPL 30.1967329 0.0005780 -0.0071495 -0.0852721 4.857155 -0.3026427 5.398659 64.32149
625 1122 UPL 9.4877576 0.0000533 -0.0015871 0.0528050 2.897304 -0.1236125 4.688323 76.92860
626 1124 UPL 32.1076414 0.0001259 -0.0017678 0.0036374 3.614210 -0.0660958 4.080627 72.69374
627 1125 UPL 46.3746513 0.0004649 -0.0029483 -0.0710198 4.580280 -0.0866087 4.532262 40.91527
628 1126 UPL 31.0702318 0.0000277 0.0008553 -0.0080077 3.612300 0.0009669 4.118996 68.45772
629 1127 UPL 46.4948356 0.0042693 0.0192929 0.2065212 2.140590 0.5408450 2.088240 52.99115
630 1128 UPL 29.3412614 0.0001805 -0.0034140 -0.0305870 4.685511 -0.0546489 5.262150 42.83368
631 1129 UPL 22.9911291 0.0000476 0.0031843 0.0512237 3.242458 0.0694356 4.100033 77.28249
632 1130 UPL 20.3471237 0.0000361 0.0007435 0.0198529 3.601891 0.0145084 4.594204 43.47335
633 1131 UPL 23.6896668 0.0000273 0.0014895 -0.0081002 4.188900 0.0629494 5.013120 65.49023
634 1132 UPL 16.7311629 0.0002382 0.0073814 0.2440725 4.376373 0.3392322 5.589095 76.89922
635 1133 UPL 43.7679585 0.0014077 -0.0088191 -0.0319233 3.359357 -0.1629994 3.402414 68.22294
636 1136 UPL 9.0782786 0.0000411 -0.0029860 -0.0383984 3.578455 -0.1662114 5.413930 70.77818
637 1137 UPL 36.4517054 0.0022701 -0.0209090 -0.2365854 5.031154 -0.3524986 5.337910 56.62105
638 1138 UPL 44.6392247 0.0049855 0.0264915 0.3740802 1.988624 0.5558010 2.002410 81.19725
639 1139 UPL 19.6387377 0.0009293 0.0115513 0.1287413 2.265531 0.1577926 3.298415 69.43923
640 1141 UPL 37.6631602 0.0003055 -0.0043983 -0.0606663 4.323421 -0.0868594 4.582991 44.67833
641 1142 UPL 32.0006496 0.0003135 -0.0067850 -0.0998670 3.565353 -0.1182358 4.036487 49.15243
642 1145 UPL 19.7989977 0.0000309 -0.0010821 0.0217123 4.578635 -0.0054787 5.600453 66.17433
643 1147 UPL 37.1873506 0.0004255 -0.0046996 -0.0940634 3.801380 -0.1516419 4.078637 63.17588
644 1148 UPL 32.1407708 0.0005265 -0.0027729 -0.0654709 3.287280 -0.1370993 3.754882 70.20147
645 1149 UPL 24.7028751 0.0007604 -0.0135270 -0.1483280 4.670099 -0.0533813 5.448483 54.59712
646 1150 UPL 23.4799939 0.0001943 -0.0059296 -0.0508398 5.712666 -0.2087865 6.550345 56.96334
647 1151 UPL 42.8661608 0.0020586 -0.0121512 -0.0329692 4.429658 -0.1093753 4.504309 49.70303
648 1153 UPL 12.9411971 0.0000833 -0.0032443 -0.1692294 4.427007 -0.2601569 5.898311 47.23226
649 1154 UPL 13.3677773 0.0001423 -0.0045547 -0.0829213 5.828989 -0.2558131 7.268212 64.42703
650 1156 UPL 28.7370023 0.0001178 0.0037280 0.0301601 4.672829 0.0800045 5.273758 42.69235
651 1157 UPL 20.8537095 0.0002730 0.0015842 0.0101140 3.685280 0.1573148 4.658314 52.91368
652 1158 UPL 45.0905375 0.0008604 0.0005534 -0.0143530 3.432411 -0.0124123 3.429241 49.80496
653 1159 UPL 26.0099454 0.0008663 0.0094332 0.0840627 2.760987 0.3123391 3.478984 69.89437
654 1161 UPL 45.0804701 0.0000440 0.0009256 0.0167806 4.618517 0.0566038 4.615709 34.72084
655 1162 UPL 35.1077644 0.0071106 0.0380370 0.4423725 1.773658 0.5508749 2.136047 74.98856
656 1163 UPL 2.7611352 0.0000055 0.0001532 -0.0409112 6.493193 -0.0476626 9.525071 51.69039
657 1164 UPL 18.7762850 0.0001133 -0.0036338 -0.0368972 3.585243 -0.0526110 4.668755 70.38471
658 1165 UPL 8.0388467 0.0000250 0.0012255 0.0546587 3.489215 -0.1368250 5.447565 66.30085
659 1166 UPL 29.0691397 0.0003427 -0.0065031 -0.1188402 4.571807 -0.2134743 5.161801 61.08905
660 1168 UPL 10.8179265 0.0000608 -0.0052043 -0.0231527 8.987043 -0.2128789 10.642368 35.63286
661 1171 UPL 9.6172377 0.0002480 0.0075446 0.3166283 2.565787 0.2308200 4.387607 53.56741
662 1172 UPL 13.0258961 0.0000106 -0.0013250 -0.0274439 4.577498 -0.1108998 6.041663 71.54881
663 1173 UPL 48.2184274 0.0019116 -0.0024805 0.0312456 4.296639 0.1265714 4.183794 46.76287
664 1175 UPL 30.6119474 0.0000753 -0.0033450 -0.0228484 6.202801 -0.0801914 6.727592 61.37441
665 1176 UPL 37.4600549 0.0009726 -0.0061758 -0.0441134 3.854062 -0.0782129 4.120447 48.14862
666 1178 UPL 9.7205833 0.0000222 0.0026076 0.0626477 3.041689 0.1487836 4.806628 56.57505
667 1179 UPL 8.4130971 0.0000389 0.0015827 0.0626739 4.520779 0.1808738 6.437748 30.02854
668 1180 UPL 29.2522598 0.0002282 -0.0019061 -0.0289246 3.088755 -0.0312393 3.668632 48.52389
669 1181 UPL 11.1032275 0.0000446 -0.0016108 -0.0843379 3.440476 -0.2244658 5.077250 53.31511
670 1182 UPL 14.0980995 0.0001314 0.0064636 0.1247210 3.564496 0.0808822 4.950965 64.72900
671 1183 UPL 19.0379132 0.0000163 0.0021160 0.0351814 3.594476 0.0969172 4.659575 59.16910
672 1185 UPL 38.0041387 0.0012831 0.0143934 0.1721912 2.260183 0.2397525 2.507004 29.22778
673 1186 UPL 21.4917527 0.0000675 -0.0037712 -0.0278350 5.854883 -0.1305758 6.787516 51.84333
674 1187 UPL 22.4833553 0.0000320 -0.0026400 -0.0217207 4.220725 -0.1768478 5.103093 64.22941
675 1188 UPL 27.4032091 0.0003137 0.0066576 0.0650036 2.741919 0.2541422 3.399191 60.49728
676 1189 UPL 23.8040385 0.0000883 0.0039449 0.0465062 2.764559 0.1845518 3.583323 91.42914
677 1190 UPL 51.6168590 0.0017559 0.0092566 0.0930240 2.869106 0.0463319 2.633958 60.90587
678 1191 UPL 29.0420797 0.0005485 -0.0091618 -0.0863900 6.816101 -0.2240494 7.404666 59.92812
679 1192 UPL 23.4748972 0.0001053 0.0021714 0.1951030 1.736401 0.1483478 2.570755 54.06336
680 1194 WET 6.3987934 0.0000795 0.0059547 0.2150042 2.117509 0.4317382 4.309919 76.11903
681 1195 UPL 35.3121396 0.0000657 -0.0024031 -0.0350593 4.701381 -0.0574919 5.046220 56.51925
682 1196 UPL 10.9072901 0.0000282 0.0002779 0.0277612 3.701084 0.0560277 5.349120 68.19300
683 1202 UPL 31.4366542 0.0002736 0.0016947 0.0136256 5.311203 0.0211988 5.804004 69.60403
684 1203 UPL 15.0100539 0.0000048 0.0004345 0.0020502 5.652671 0.0264874 6.969899 78.07914
685 1204 UPL 8.5031660 0.0000358 0.0015102 0.0641570 3.168023 0.1491755 5.069585 72.41079
686 1205 UPL 34.3319049 0.0002561 0.0064793 0.0553294 4.294324 0.0828363 4.677022 60.79418
687 1208 UPL 39.0999271 0.0035834 -0.0222815 -0.1860864 5.184783 -0.2912782 5.393255 51.09053
688 1209 UPL 31.5749705 0.0000746 -0.0022087 -0.0637907 4.454609 -0.0901930 4.941916 57.42365
689 1210 UPL 30.9635670 0.0027213 0.0161850 0.2629878 1.851312 0.3219375 2.364828 40.83955
690 1211 UPL 33.3006193 0.0002201 -0.0021661 -0.0392988 4.412318 -0.0951033 4.833636 57.21724
691 1212 UPL 18.0668983 0.0015443 0.0197042 0.4021277 2.068218 0.6004010 3.195408 47.91987
692 1213 UPL 18.0762828 0.0000607 0.0031565 0.0855279 3.034309 0.0381480 4.154307 24.90208
693 1214 UPL 46.4395239 0.0048236 0.0191741 0.1192335 2.940227 0.1896232 2.889864 45.22272
694 1215 UPL 37.2710042 0.0007832 -0.0119690 -0.1121811 4.729420 -0.2621025 5.005430 63.18016
695 1216 UPL 33.1260129 0.0001099 0.0027425 0.0205164 3.445787 0.0186463 3.872897 66.73528
696 1218 UPL 44.1821653 0.0003386 -0.0052227 -0.0388560 3.693172 -0.0291323 3.721731 47.69490
697 1220 UPL 53.6344184 0.0072663 -0.0124293 -0.0566514 4.131875 -0.2473829 3.825618 56.98475
698 1221 UPL 20.7403787 0.0000278 0.0009428 0.0436815 5.173705 0.0394304 6.145311 58.92845
700 1225 UPL 23.8738547 0.0002396 -0.0083397 -0.0499689 5.189627 -0.1629910 6.006924 47.68606
701 1226 UPL 20.5759754 0.0002488 -0.0057044 -0.1230276 4.812602 -0.0958928 5.792718 57.11339
702 1227 UPL 7.9854415 0.0000084 -0.0010589 -0.0667962 7.049718 -0.1209026 9.016638 25.67386
703 1228 UPL 16.6129655 0.0004200 -0.0062316 -0.1676312 3.278038 -0.1268940 4.509337 73.84599
704 1230 UPL 40.9606131 0.0000517 0.0017518 -0.0092179 5.461444 0.0833286 5.602931 89.01246
705 1231 UPL 32.0116360 0.0000940 0.0006104 0.0164228 4.194171 0.0114983 4.664344 54.34391
706 1234 UPL 10.2401691 0.0007796 -0.0139656 -0.6898018 7.128733 -1.9241546 8.849063 52.44314
707 1235 UPL 22.7803279 0.0000339 -0.0024505 -0.0164887 5.377113 -0.0135058 6.245421 41.22219
708 1236 UPL 37.0893426 0.0001554 -0.0025616 -0.0060017 5.480962 -0.0761571 5.760862 64.22446
709 1238 UPL 17.5334234 0.0001047 -0.0005681 -0.0320183 6.395261 -0.0254016 7.547539 61.43545
710 1239 UPL 24.7792370 0.0007789 0.0018993 0.0175266 2.636426 0.0124191 3.416653 59.26646
711 1240 UPL 16.4304212 0.0001493 -0.0030933 -0.0447998 4.987263 -0.1206919 6.211475 42.62020
712 1242 UPL 54.1312118 0.0014530 0.0064041 0.0463102 3.396791 0.1327858 3.072349 71.65421
713 1246 UPL 31.9744660 0.0001440 -0.0044703 -0.0478701 4.496256 -0.1565683 4.968189 52.75816
714 1247 UPL 9.4734243 0.0002462 -0.0073900 -0.1821483 3.878777 -0.3914795 5.691295 82.48049
715 1248 UPL 17.5539985 0.0000723 -0.0035569 -0.0335411 5.233539 -0.1168443 6.384687 70.09887
716 1249 UPL 25.4063035 0.0002868 -0.0060329 -0.0519078 6.614801 -0.1141533 7.359466 64.48382
717 1250 UPL 29.8190320 0.0001042 -0.0030004 -0.0289548 5.723742 -0.0556057 6.280861 64.87900
718 1251 UPL 21.6520411 0.0002109 0.0028271 0.0838018 4.568015 0.0436597 5.497497 37.87949
719 1252 UPL 39.6775365 0.0012339 0.0125039 0.1008723 3.083581 0.2396712 3.270750 83.37094
720 1255 UPL 2.2929736 0.0001384 -0.0013784 0.0142140 1.609438 0.1195496 4.963508 81.34474
721 1258 UPL 17.0450676 0.0003782 0.0079686 0.1553194 2.265673 0.2828066 3.448872 37.75769
722 1259 UPL 32.2826632 0.0000886 -0.0038113 -0.0459244 5.161583 -0.1247939 5.621321 49.36535
723 1260 UPL 39.3978354 0.0008591 -0.0096933 -0.0349215 5.510067 -0.1878877 5.707082 52.28427
724 1262 UPL 14.2946958 0.0002266 0.0072320 0.0974306 3.414998 0.1797376 4.789260 60.28226
725 1263 UPL 25.2846190 0.0005878 -0.0067031 -0.0931729 5.632337 0.0342871 6.387214 50.92569
726 1264 UPL 11.2880196 0.0002692 -0.0040822 -0.6049717 3.837376 -0.3262733 5.511059 48.02759
727 1265 UPL 32.9173392 0.0000466 0.0022020 0.0051349 4.173685 -0.0177458 4.608547 59.73837
728 1266 UPL 29.6029912 0.0011602 0.0034711 0.1472515 4.124576 0.1104328 4.705188 57.84199
729 1267 UPL 38.5558535 0.0002645 -0.0002560 -0.0114908 4.234891 0.0360430 4.461865 45.46865
730 1268 UPL 33.2783936 0.0009753 0.0096825 0.1407411 3.835565 0.2954822 4.258353 51.58882
731 1269 UPL 33.7694640 0.0014236 0.0111673 0.0669916 2.466138 0.4135802 2.869039 72.18121
732 1270 UPL 55.9593290 0.0005375 0.0005673 0.0060594 3.616296 -0.1235638 3.222812 66.26935
733 1271 UPL 1.3825949 0.0000696 -0.0053673 -0.4140417 4.616420 -0.6640312 8.364793 32.22075
734 1274 UPL 22.6337239 0.0001357 -0.0049501 0.0221531 4.087395 -0.0973539 4.963265 57.29818
735 1275 UPL 21.8892419 0.0002450 0.0024518 0.1139467 2.078433 0.0476747 2.993758 51.81412
736 1277 UPL 40.3019430 0.0006066 -0.0021874 -0.0403370 3.666948 0.0436694 3.831805 72.61139
737 1278 UPL 43.4752745 0.0005566 0.0044068 0.0509266 3.271306 0.2177490 3.324610 41.20236
738 1280 UPL 14.5766992 0.0000789 0.0014880 0.0459028 3.045807 0.1203305 4.394107 73.20837
739 1281 UPL 19.2314607 0.0003413 0.0063904 0.0406951 5.269882 0.1792718 6.324332 65.22179
740 1282 UPL 36.1808461 0.0013113 -0.0134317 -0.1147805 5.241834 -0.1396442 5.554860 68.12875
741 1283 UPL 44.3703222 0.0006753 0.0046955 0.0493516 3.619387 0.1598302 3.641503 75.80797
742 1284 UPL 33.7741967 0.0005582 -0.0052870 -0.0908869 4.177458 -0.1517319 4.581938 74.94985
743 1285 UPL 23.4419944 0.0001165 -0.0026694 0.0183021 4.844549 0.0046661 5.681513 50.76092
744 1286 UPL 23.6658156 0.0000446 0.0003051 -0.0097856 3.496204 0.0360845 4.322017 61.50436
745 1289 UPL 2.3684481 0.0000388 -0.0015124 -0.2602904 7.546509 -0.3831961 10.765396 42.24314
746 1291 UPL 35.7543444 0.0003944 -0.0044028 -0.0636710 4.397244 0.0745094 4.726191 66.32997
747 1294 UPL 41.4666182 0.0007589 0.0064379 0.0929592 3.741834 0.2087822 3.865897 66.72307
748 1295 UPL 27.0073732 0.0000813 0.0032936 0.0587210 5.263532 0.0202338 5.937764 52.63182
749 1297 UPL 21.5201259 0.0000540 0.0031173 0.0442428 4.075059 0.1646282 5.006186 64.59211
750 1299 UPL 52.4201019 0.0065255 -0.0054392 -0.0348592 2.995249 0.1665071 2.731651 41.67505
751 1301 UPL 12.8249516 0.0002407 0.0074447 0.1366329 3.247233 0.3991677 4.729500 73.11363
752 1302 UPL 16.2760644 0.0000743 0.0007904 0.0313766 4.881619 0.0162694 6.113235 30.54223
753 1303 UPL 24.9652540 0.0001390 -0.0047641 -0.1345673 6.029412 -0.1072328 6.795368 44.67272
754 1306 UPL 41.0931151 0.0003489 -0.0027698 -0.0147508 3.408232 0.0043100 3.545140 74.45640
755 1307 UPL 34.3204888 0.0007421 -0.0125325 -0.1161560 4.689261 -0.2113267 5.071628 55.54310
756 1308 UPL 21.4374385 0.0000285 -0.0000139 -0.0035321 4.007124 -0.0183988 4.942150 47.96935
757 1309 UPL 33.3376926 0.0001316 0.0040222 0.0564870 4.085882 0.1214126 4.505196 52.96937
758 1310 UPL 14.5080653 0.0000182 -0.0015193 -0.0655854 5.824503 -0.0477991 7.177030 29.31772
759 1311 UPL 39.6078916 0.0000022 0.0006072 0.0021556 3.747314 0.0497268 3.936655 45.23311
760 1313 UPL 24.8253177 0.0001059 0.0052441 0.0274880 3.815772 0.0627702 4.586698 52.18624
761 1314 UPL 24.1927935 0.0000029 0.0005956 0.0080126 3.813687 0.0528252 4.613829 67.54223
762 1316 UPL 30.5964772 0.0000735 0.0014701 -0.0047851 4.724169 0.0574175 5.249709 49.40923
763 1317 UPL 34.5642978 0.0003411 0.0085170 0.0683334 3.258934 0.1556765 3.631594 64.28166
764 1318 UPL 8.0595547 0.0000395 -0.0019883 -0.0437147 3.459133 -0.0272354 5.415683 52.92242
765 1319 UPL 4.4909909 0.0000323 0.0023939 0.4425876 5.015934 0.0477708 7.589673 26.44676
766 1320 UPL 22.4346104 0.0001281 0.0036889 0.0255416 3.846554 0.0700478 4.731546 60.47833
767 1321 UPL 5.3043154 0.0000802 0.0011242 -0.0426947 4.273745 0.1802420 6.659509 48.21579
768 1322 UPL 44.6637593 0.0018114 0.0142367 0.1120729 2.727154 0.2479429 2.739090 59.79444
769 1323 UPL 10.9606535 0.0014226 0.0130378 0.4425687 1.947124 0.4856988 3.778902 57.25611
770 1324 UPL 27.5943187 0.0013075 0.0071562 0.1008868 4.855548 -0.6606545 5.515365 68.13171
771 1325 UPL 24.4936192 0.0003616 -0.0084257 -0.1463979 4.361775 -0.1563891 5.151919 63.50710
772 1326 UPL 38.1195894 0.0000502 0.0025492 0.0106022 4.325594 0.0152732 4.568248 41.16322
773 1327 UPL 9.1743320 0.0001297 0.0047181 0.2694815 1.627761 0.1498726 3.461862 30.52128
774 1329 UPL 44.7563839 0.0051357 0.0109146 0.0632167 4.254699 0.3024377 4.263465 66.70629
775 1330 UPL 32.7158360 0.0020818 -0.0192500 -0.0856429 5.675407 -0.2852791 6.118250 57.18911
777 1333 UPL 28.8504974 0.0000574 -0.0035747 -0.0291038 5.715718 -0.0663637 6.312190 63.01296
778 1334 UPL 41.6149237 0.0002088 0.0002749 -0.0009635 3.713485 -0.0598389 3.831934 56.16230
779 1336 UPL 29.3249249 0.0001148 0.0044188 0.0222317 4.469112 0.0242715 5.046252 57.57700
780 1337 UPL 41.0466280 0.0011705 0.0048556 -0.0005798 2.933935 0.0572816 3.073163 60.60839
781 1338 WET 3.5017280 0.0003361 0.0045413 0.1323592 1.820264 0.4920046 4.650801 72.06095
782 1339 UPL 20.6180625 0.0001216 0.0059678 0.0521600 5.209429 0.1586222 6.187323 54.10823
783 1340 UPL 41.2331371 0.0041031 0.0252429 0.2789105 1.981269 0.4104402 2.113784 61.59055
784 1342 UPL 22.4587110 0.0005589 0.0071065 0.1569484 2.862921 0.4661176 3.747411 64.14805
785 1343 UPL 32.3312933 0.0001063 0.0017436 -0.0247005 3.279265 0.0703262 3.737177 58.41980
786 1344 UPL 31.6429825 0.0027849 -0.0243248 -0.2844582 4.591850 -0.7674246 5.080949 74.93907
787 1347 UPL 27.8360734 0.0003041 -0.0070000 -0.1023865 4.200372 -0.1355654 4.839710 53.40828
788 1348 UPL 10.0585394 0.0000357 0.0033251 0.0710463 2.331024 0.5873426 4.067875 58.73638
789 1349 UPL 7.6502269 0.0003062 -0.0069206 -0.1268845 7.751703 -0.6963809 9.807297 38.52848
790 1350 UPL 17.6602823 0.0000278 -0.0019884 -0.0253644 6.165625 -0.0532315 7.310381 53.59127
791 1351 UPL 32.0955444 0.0000581 0.0014469 0.0393395 3.850798 -0.0264133 4.318004 76.93313
792 1353 UPL 10.1801945 0.0000139 -0.0014631 -0.0716972 5.417653 -0.0163551 7.138244 48.89078
793 1357 UPL 14.6156259 0.0000430 -0.0009992 0.0544340 5.805638 -0.1258940 7.150398 58.93301
794 1358 UPL 13.8987880 0.0003284 0.0079809 -0.1173291 3.334183 0.3438078 4.762210 58.30660
795 1359 UPL 50.5874455 0.0118293 -0.0334250 -0.1602516 4.405997 -0.2420845 4.209385 52.52884
796 1361 UPL 25.3168457 0.0003251 -0.0054727 -0.0292595 4.613981 -0.0897217 5.362630 60.18822
797 1362 UPL 21.0863362 0.0001331 -0.0063127 -0.1060111 5.530885 -0.0980977 6.488200 51.22677
798 1363 WET 6.0115092 0.0000150 -0.0018308 -0.0024491 5.362706 -0.3568484 7.616304 59.05879
799 1364 UPL 10.8836667 0.0000137 -0.0004324 -0.0315452 3.862049 -0.0698323 5.515210 56.06949
800 1365 UPL 33.7188902 0.0000766 -0.0025376 -0.0129621 3.982375 -0.0182244 4.386858 63.34867
801 1367 UPL 25.5710127 0.0000804 -0.0026328 0.0167239 4.927567 -0.0406031 5.664870 51.52203
802 1368 UPL 33.0191713 0.0002872 0.0045603 0.0342392 4.387353 0.1189028 4.819951 53.76402
803 1370 UPL 22.4636143 0.0005312 -0.0070728 -0.1232511 5.448056 -0.0647651 6.332132 68.64608
804 1372 UPL 7.0423011 0.0001491 -0.0051151 -0.2061519 6.494299 -0.3024525 8.587779 70.75560
805 1374 UPL 39.9271594 0.0006492 0.0060533 0.0285645 4.145059 0.1506127 4.323364 54.94741
806 1375 UPL 26.9965885 0.0004028 -0.0060676 -0.0873060 4.140675 -0.1692235 4.820290 60.80954
807 1376 UPL 25.4833964 0.0007157 -0.0074374 -0.0468424 3.381054 -0.0486680 4.125012 54.92965
808 1377 UPL 27.9536464 0.0007179 0.0105003 0.1558478 3.690410 0.2973720 4.329557 48.45616
809 1378 UPL 20.7791487 0.0001041 0.0030827 0.0339721 3.234618 0.0720669 4.204015 51.37773
810 1379 UPL 18.7671872 0.0002022 -0.0061984 -0.1303132 4.238373 -0.2818861 5.321833 78.78697
811 1383 UPL 25.5844355 0.0002209 0.0047638 0.0648271 2.998429 0.0458822 3.736399 37.22420
812 1384 UPL 33.9738256 0.0008446 0.0093033 0.1599036 5.327607 0.2137178 5.724450 47.13636
813 1386 UPL 21.3185555 0.0001104 -0.0015330 -0.0199772 5.588910 -0.1442202 6.530268 73.14037
814 1387 UPL 17.9692703 0.0000922 -0.0026059 -0.1109442 4.840788 -0.1040588 5.970413 57.01658
815 1389 UPL 42.6564567 0.0001703 0.0028496 0.0419416 3.269369 0.0951935 3.351272 50.77003
816 1391 UPL 14.7892884 0.0001684 -0.0063525 -0.0941672 5.572607 -0.2738471 6.909265 72.42109
817 1392 UPL 29.0557729 0.0001086 0.0015153 0.0350952 3.894144 0.0543537 4.482216 65.90615
818 1393 UPL 36.8659023 0.0011367 -0.0133415 -0.0817296 6.933717 -0.3108281 7.222118 42.30543
819 1394 UPL 15.2604991 0.0001391 -0.0046968 -0.1035103 3.836400 -0.2927764 5.136387 45.65348
820 1395 UPL 24.2798799 0.0001623 -0.0007910 -0.0434466 3.060912 -0.0268421 3.863153 35.81020
821 1398 UPL 33.7567099 0.0005667 0.0073319 0.0532735 5.284857 0.0010820 5.688161 70.00578
822 1399 UPL 24.9064992 0.0001351 -0.0010649 -0.0776345 4.152686 -0.0861304 4.920711 38.99943
823 1400 UPL 10.3173226 0.0009145 -0.0079369 -0.4168854 6.498836 -0.5270387 8.227446 46.77861
824 1401 UPL 13.9933106 0.0000320 0.0027341 0.1213230 3.338019 0.1936235 4.732211 49.92639
825 1402 UPL 19.2900757 0.0003052 0.0053444 0.0905050 4.207730 0.1298964 5.261553 70.02301
826 1403 UPL 14.4803883 0.0000704 -0.0005129 -0.0104570 3.715844 -0.1537379 5.071409 50.29515
827 1405 UPL 32.5721538 0.0091995 -0.0464887 -0.2346247 3.721969 -0.7447366 4.173827 60.65185
828 1406 UPL 62.7190796 0.0009601 0.0027719 -0.0124824 3.511760 0.0427943 2.848353 50.75386
829 1407 UPL 39.2451332 0.0001518 -0.0010633 -0.0058905 4.854945 0.0933847 5.057417 55.64355
830 1408 UPL 27.4581053 0.0002012 0.0037258 0.0407531 4.240003 -0.0303206 4.895006 63.44255
831 1413 UPL 4.2611539 0.0000713 0.0056242 0.1718612 2.383819 0.3344687 4.981741 19.15745
832 1415 UPL 17.9581557 0.0000800 -0.0039291 0.0088515 3.695184 -0.1936627 4.821951 68.78108
833 1416 UPL 21.7614200 0.0010729 0.0026276 -0.0237435 2.042937 0.4937145 2.962832 64.95345
834 1417 UPL 13.3017409 0.0000873 -0.0057392 -0.0747170 6.962919 -0.3043306 8.409320 67.59296
835 1419 UPL 17.6639123 0.0002268 -0.0012737 0.0291168 3.668246 -0.0324037 4.814453 76.10533
836 1421 UPL 13.2524367 0.0001008 0.0033248 0.0401099 6.251835 0.1343374 7.701486 60.10705
837 1422 UPL 20.7533361 0.0000940 0.0008093 -0.0147605 5.387530 -0.0254114 6.360772 43.34699
838 1423 UPL 19.1843482 0.0000463 -0.0010675 0.0291919 4.295344 -0.0640325 5.353032 35.15084
839 1424 UPL 39.6324897 0.0002935 0.0055051 0.0757294 4.027786 0.0894277 4.216300 67.74633
840 1426 UPL 26.4599980 0.0003167 0.0051999 0.0858013 3.739709 0.2250709 4.438025 60.18259
841 1427 UPL 38.8665581 0.0007332 0.0057141 0.1287399 2.970898 0.0412448 3.186779 38.97989
842 1429 UPL 38.4593764 0.0005008 0.0021699 -0.0089668 3.659574 -0.1436892 3.890155 58.12029
843 1430 UPL 26.1035499 0.0004717 0.0072285 0.0642103 4.696205 0.2560278 5.410772 73.76357
844 1431 UPL 26.7040717 0.0001832 -0.0059457 -0.0704471 7.202125 -0.1339185 7.889704 37.29530
845 1433 UPL 23.3890758 0.0007832 0.0102845 0.1007437 2.249692 0.4187253 3.089305 49.43021
846 1434 UPL 41.0384850 0.0014499 -0.0161676 -0.1941725 3.559180 -0.1965217 3.699470 45.64104
847 1435 UPL 35.7353327 0.0005187 -0.0044660 -0.0001604 2.361625 0.0450044 2.690867 37.30574
848 1436 UPL 12.5289887 0.0000132 0.0003409 0.1311913 2.622294 0.0572038 4.130278 49.06334
849 1438 UPL 24.2044667 0.0000531 0.0021252 0.0008842 4.439712 -0.0144047 5.239986 52.34000
850 1439 UPL 15.6818697 0.0000235 -0.0026796 -0.0546791 5.206475 -0.1238902 6.476908 59.85433
851 1440 UPL 40.0998139 0.0005500 -0.0024095 -0.0414689 3.557290 -0.0735124 3.729255 62.30635
852 1441 UPL 36.9603907 0.0001217 0.0027807 0.0351326 4.335110 0.0813706 4.619562 30.72034
853 1443 UPL 21.4470824 0.0003316 0.0101653 0.2328856 2.793062 0.3537591 3.728604 47.29077
854 1445 UPL 13.1313967 0.0001380 0.0012254 -0.0650247 3.316407 0.1692513 4.786473 56.69169
855 1446 UPL 23.9336037 0.0002973 0.0077421 0.1203574 2.747930 0.3440717 3.561867 77.70025
856 1449 UPL 12.5119593 0.0000208 0.0014988 0.0380356 3.806470 0.0106370 5.313943 57.54470
857 1450 UPL 18.5588615 0.0001489 0.0056911 0.1009580 3.674596 0.2497188 4.766251 53.29535
858 1451 UPL 32.6579562 0.0003515 -0.0032977 0.0141720 5.645753 -0.0720874 6.091009 56.40401
859 1452 UPL 21.7437157 0.0004906 -0.0014408 -0.0913928 2.771195 -0.1766320 3.726885 68.00330
860 1455 UPL 32.7984167 0.0001837 -0.0002534 0.0086574 3.371725 -0.0958765 3.811424 50.46098
861 1457 UPL 63.6493922 0.0069542 0.0107817 0.0438680 3.647910 0.0159620 2.945243 47.29676
862 1458 UPL 50.5918656 0.0011259 -0.0015082 -0.0765783 4.179415 0.0568780 3.982691 62.45746
863 1460 UPL 31.1087765 0.0000164 0.0018267 0.0205493 4.009633 0.0521420 4.514853 37.96605
864 1461 UPL 31.2493613 0.0006260 0.0044450 0.0103435 3.203616 0.2290019 3.704349 53.71809
865 1462 UPL 19.8794541 0.0000390 -0.0016326 -0.0386855 4.643888 -0.0448317 5.661306 72.45784
866 1463 UPL 16.4092317 0.0005727 0.0143641 0.3416803 3.892144 0.4153979 5.148100 65.23682
867 1464 UPL 32.9654046 0.0000820 0.0031126 0.0157262 4.766053 0.0376839 5.199095 59.04907
868 1465 UPL 31.8741382 0.0001230 -0.0029719 -0.0266126 4.827686 -0.0992789 5.302937 59.52873
869 1467 UPL 19.5007421 0.0000213 -0.0006928 -0.0033247 4.674060 0.0297331 5.712254 39.26256
870 1468 UPL 19.9340691 0.0000837 0.0026970 0.0584130 5.314326 -0.0412511 6.331443 74.62803
871 1469 UPL 31.0550035 0.0000917 -0.0040600 -0.0392748 5.776744 -0.1240442 6.284101 40.11923
872 1470 UPL 29.7068876 0.0001070 0.0012602 0.0044144 4.869693 0.0721004 5.431731 54.94829
873 1471 UPL 26.1628140 0.0018216 0.0208924 0.2177919 3.445662 0.4246653 4.162446 66.98824
874 1473 UPL 19.7887868 0.0000212 0.0008241 0.0133758 5.314250 0.0105976 6.336904 67.06104
875 1474 UPL 18.7637959 0.0001910 0.0027782 0.0025750 4.783263 0.0909307 5.863001 42.36975
876 1475 UPL 12.5622511 0.0001660 0.0051976 0.1133867 2.959623 0.5513029 4.464556 66.52947
877 1476 UPL 18.2930570 0.0000359 -0.0024718 -0.0088341 6.214954 -0.0035820 7.322667 45.96908
878 1477 UPL 3.6829661 0.0000442 0.0040906 0.2976096 1.761771 0.2091737 4.513082 25.78424
879 1482 UPL 7.9375855 0.0003698 0.0104222 0.3292718 2.971615 0.5482177 4.990357 70.72132
880 1484 UPL 16.5017825 0.0000235 0.0013085 0.0234400 4.839083 0.0560720 6.056573 71.30510
881 1486 UPL 32.2720159 0.0000610 -0.0023265 0.0069148 6.128224 -0.0531365 6.588041 51.49736
882 1487 UPL 7.5066504 0.0000769 0.0031571 0.0727302 3.666371 0.0405667 5.697570 99.36952
883 1488 UPL 31.7942808 0.0056612 0.0367627 0.2813011 1.791978 0.6941396 2.276544 66.83223
884 1489 UPL 21.6925277 0.0001550 -0.0021597 0.0258114 6.583355 -0.0489933 7.506571 75.49358
885 1490 UPL 46.4514908 0.0009780 -0.0041426 -0.0352124 4.514964 -0.0036094 4.464066 63.95880
886 1491 UPL 22.8922077 0.0001007 0.0013299 -0.0138508 5.974776 0.0027736 6.837607 68.20921
887 1494 UPL 25.2465805 0.0001267 0.0046834 0.0626632 4.338437 0.1272476 5.090910 69.60912
888 1495 UPL 18.1174848 0.0000235 0.0007284 0.0273737 5.601847 0.0670500 6.719658 37.53345
889 1496 UPL 27.8825350 0.0001183 -0.0052437 -0.0283211 4.893056 -0.1784500 5.529765 73.62831
890 1497 UPL 8.1432976 0.0000470 -0.0026975 -0.0396909 3.601672 -0.3340086 5.551375 19.20983
891 1498 UPL 15.2025453 0.0000951 -0.0032844 -0.0793780 6.621220 -0.2034318 7.926358 54.91839
892 1499 UPL 38.8173933 0.0008348 -0.0106907 -0.0340010 3.843003 -0.2066291 4.060586 50.62405
893 1500 UPL 19.9350043 0.0017873 -0.0167740 -0.1194066 3.731003 -0.3247958 4.751696 69.74889
894 1501 UPL 22.0469022 0.0000862 -0.0035233 -0.0557035 4.657444 -0.1867895 5.562347 69.05598
895 1503 UPL 27.6406192 0.0001318 -0.0044241 -0.0243392 3.955633 -0.0810205 4.603169 59.60408
896 1505 UPL 12.9962366 0.0000292 -0.0028261 -0.0417647 3.953457 -0.1471330 5.421766 65.80589
897 1506 UPL 30.4190581 0.0005805 0.0044579 0.0331025 3.614020 0.1240507 4.146773 48.88785
898 1507 UPL 41.1782195 0.0017999 0.0011969 0.0148913 4.473848 0.0540165 4.608446 65.28970
899 1508 UPL 25.5697862 0.0003271 0.0008514 -0.0304398 6.557343 -0.0062530 7.303092 44.28602
900 1510 UPL 18.0495603 0.0002682 -0.0075465 -0.1035982 5.815801 -0.3819636 6.937894 75.64623
901 1511 UPL 40.2877241 0.0012873 0.0127014 0.1295709 2.283943 0.3631282 2.450135 66.27758
902 1512 UPL 40.7581891 0.0007167 0.0072374 0.0616469 3.235890 0.1597114 3.384524 52.93033
903 1513 UPL 39.6067892 0.0000658 -0.0008187 -0.0176882 5.554528 -0.0189076 5.743938 31.86001
904 1516 UPL 12.7854908 0.0000389 -0.0012437 -0.0570693 5.946341 -0.0837424 7.430443 86.25070
905 1517 UPL 14.9597011 0.0001170 0.0022089 -0.0074897 5.304746 -0.0272929 6.624770 48.98572
906 1518 UPL 47.8412837 0.0005901 0.0058544 0.0890557 3.192231 0.0761061 3.091903 60.50416
907 1520 UPL 30.1555456 0.0001038 0.0041811 0.0417592 4.556793 0.0791055 5.099936 41.55925
908 1523 UPL 32.2189040 0.0018011 0.0202958 0.1619311 2.418054 0.5884613 2.889006 91.23739
909 1525 UPL 7.9263388 0.0000237 0.0003634 0.0058474 4.733467 0.0368542 6.709628 30.63893
910 1526 UPL 13.4287529 0.0003471 -0.0016703 0.0092036 2.239440 0.1356871 3.686637 42.64803
911 1527 UPL 10.0519578 0.0001001 -0.0044644 -0.0903838 5.692781 -0.1606474 7.430971 41.73098
912 1528 UPL 16.6115963 0.0003788 -0.0040594 -0.0470842 4.733032 -0.0702175 5.943137 49.59285
913 1529 UPL 23.4883259 0.0001187 0.0045843 0.0516366 3.061353 0.1743049 3.894969 48.80630
914 1530 UPL 34.1670314 0.0003888 0.0076225 0.0858995 3.416409 0.1808633 3.805119 84.35488
915 1531 UPL 26.8769254 0.0000874 -0.0043957 -0.0493149 6.019383 -0.0442825 6.700173 46.74969
916 1534 UPL 21.8422123 0.0000238 -0.0020154 -0.0046309 4.693261 -0.1268304 5.607566 46.71913
917 1535 UPL 28.9835759 0.0000562 0.0023214 0.0004578 2.994278 0.1239399 3.585302 45.31193
918 1536 UPL 29.2946307 0.0002961 0.0060852 0.0881258 2.204208 0.0372509 2.784724 49.36789
919 1540 UPL 52.0501425 0.0044034 -0.0016032 -0.0199200 3.907722 0.1415561 3.658589 51.14397
920 1541 UPL 16.6755258 0.0000122 -0.0004164 0.0206130 4.521486 -0.0460556 5.727200 32.10251
921 1542 UPL 26.8227230 0.0003758 0.0085056 0.1253602 3.160646 0.3141418 3.845026 82.68730
922 1543 UPL 33.2834599 0.0002929 -0.0079011 -0.0833121 6.059197 -0.1683193 6.481018 57.75108
923 1544 UPL 42.7131074 0.0017545 0.0062310 0.0897699 4.949721 0.0857866 5.030551 54.84415
924 1545 UPL 31.7151813 0.0001111 -0.0046706 -0.0481482 4.595231 -0.1135481 5.076616 44.74601
925 1546 UPL 24.3238804 0.0000891 -0.0037264 -0.0289277 3.414049 -0.1424881 4.208614 48.89509
926 1547 UPL 12.4279203 0.0000067 -0.0009062 -0.0364436 3.130340 -0.0060356 4.643404 79.66906
927 1548 UPL 15.9535066 0.0000569 -0.0031589 -0.0568242 5.836520 -0.1479622 7.090527 62.21320
928 1549 UPL 31.2912152 0.0000583 0.0014760 0.0064086 4.905535 0.0621403 5.403506 49.67022
929 1551 UPL 18.3399188 0.0000617 0.0004049 -0.0447072 6.218544 -0.0094352 7.323105 69.66261
930 1554 UPL 18.0340436 0.0000194 -0.0003018 0.0380298 4.825725 -0.0385158 5.949209 76.64846
931 1555 UPL 11.6503326 0.0001014 -0.0023086 -0.0108513 4.484486 -0.0236519 6.070089 51.64981
932 1556 UPL 16.9365865 0.0000045 -0.0002530 0.0205148 6.640760 0.0291414 7.831390 33.74232
933 1557 UPL 22.2396500 0.0000506 0.0009677 0.0034189 3.715397 -0.0073932 4.609690 73.86768
934 1558 UPL 21.8946728 0.0000088 0.0003519 0.0553024 3.163092 0.0483836 4.074684 54.34241
935 1559 UPL 16.1869472 0.0000562 0.0026942 -0.0235756 4.602121 0.0470249 5.840024 57.37088
936 1560 UPL 4.7355685 0.0003938 0.0104890 0.2257755 4.215680 0.4897421 6.741020 43.52997
937 1561 UPL 15.7589428 0.0002994 0.0054591 0.1383129 3.396608 0.0767593 4.679455 40.78252
938 1565 UPL 31.5753684 0.0001761 -0.0020503 -0.0107280 3.966386 -0.0898504 4.454142 68.95087
939 1567 UPL 5.5489116 0.0001049 -0.0041445 0.0831432 1.789274 0.0386465 4.137929 68.00521
940 1569 UPL 12.3774952 0.0011446 0.0012208 -0.1040761 3.034178 0.5574495 4.579985 59.82804
941 1570 UPL 18.7395010 0.0014123 0.0115588 0.0649170 4.658924 0.4323984 5.753169 76.41661
942 1573 UPL 46.5916206 0.0011127 0.0103336 0.0720464 4.037627 0.1703806 3.982000 27.63319
943 1575 UPL 38.1112312 0.0001990 0.0023391 0.0445333 3.847118 -0.0349079 4.090406 45.10225
944 1577 UPL 5.5133833 0.0000159 0.0006933 -0.0405470 9.015671 -0.0416872 11.354940 103.62875
945 1579 UPL 29.2145444 0.0001348 -0.0031710 -0.0267664 5.192734 -0.0724176 5.774152 74.66905
946 1580 UPL 24.3102220 0.0001729 -0.0047509 -0.0805993 5.270529 -0.2047631 6.066352 56.42050
947 1581 UPL 13.4407622 0.0001638 -0.0017574 -0.0700458 3.496591 0.0047223 4.944413 41.04722
948 1582 UPL 38.9897805 0.0012758 -0.0051594 -0.1030265 6.056344 -0.0999538 6.267958 52.00760
949 1585 UPL 30.2351354 0.0001132 -0.0005330 -0.0304417 4.308139 0.0769461 4.848325 42.21387
950 1586 UPL 14.9454392 0.0001459 -0.0063212 -0.1211126 5.055677 -0.3337634 6.376998 67.70359
951 1587 UPL 31.2306451 0.0010629 -0.0123700 -0.1194601 4.896047 -0.1879499 5.398138 54.89276
952 1590 UPL 22.7808092 0.0001202 -0.0021386 -0.0003432 3.566153 -0.2129743 4.433906 46.02185
953 1592 UPL 23.1835773 0.0006844 -0.0098888 -0.1548312 4.994221 -0.3669526 5.844796 47.50730
954 1593 UPL 53.3532301 0.0032143 0.0121698 0.1401316 2.534435 0.2863652 2.235849 40.84504
955 1597 WET 8.0096727 0.0000384 -0.0009821 -0.0229399 5.152570 -0.1276312 7.114584 67.78045
956 1600 UPL 45.8509700 0.0025260 -0.0124692 -0.0650949 5.930202 -0.1608086 5.900431 22.41461
957 1601 UPL 26.1415337 0.0000613 0.0014964 0.0183689 3.504328 0.0158778 4.216193 68.33777
958 1602 UPL 23.6016720 0.0016411 -0.0134946 -0.1220083 3.620619 -0.4343785 4.453586 52.12987
959 1604 UPL 23.4857645 0.0000331 -0.0013341 -0.0076319 5.782017 -0.0252570 6.616400 69.64800
960 1606 UPL 33.4760114 0.0014708 0.0003301 0.0099485 5.187746 0.1246043 5.602106 72.95232
961 1607 UPL 14.9130505 0.0000795 -0.0035450 -0.0443516 3.062662 -0.1390750 4.386037 47.85594
962 1608 UPL 23.5204966 0.0120363 0.0577304 0.7056949 1.695150 0.9192314 2.553428 60.57570
963 1610 UPL 36.3549827 0.0000756 -0.0024499 -0.0418728 4.383486 -0.0218705 4.690078 33.69228
964 1612 UPL 17.0133671 0.0000294 -0.0008792 -0.0806144 6.207019 -0.0693957 7.394436 29.58876
965 1613 UPL 10.8347133 0.0005258 0.0082690 0.0859610 2.600869 0.2961175 4.287922 52.29050
966 1614 UPL 19.0782061 0.0000665 -0.0042444 -0.0706664 6.936063 -0.4658990 7.999378 54.64584
967 1615 UPL 41.3133488 0.0001288 0.0043426 0.0285981 3.617882 -0.0305526 3.747006 61.28395
968 1617 UPL 31.9465151 0.0002943 0.0047962 0.0640223 4.368374 0.2506244 4.841321 30.29444
969 1620 UPL 54.3329943 0.0004853 -0.0017881 -0.0819942 3.615409 -0.0453300 3.283563 44.33232
970 1621 UPL 16.5330335 0.0000083 -0.0014795 -0.0504480 6.238534 -0.1295340 7.453304 55.38574
971 1622 UPL 29.5171941 0.0006611 0.0127258 0.1760147 2.186536 0.5361809 2.756919 59.00798
972 1623 UPL 31.8132305 0.0025685 0.0226170 0.1566684 2.394851 0.4411777 2.873036 48.41185
973 1624 UPL 30.1204679 0.0024126 -0.0206228 -0.1843355 5.100394 -0.5290902 5.653217 59.37327
974 1626 UPL 18.8500609 0.0003528 0.0004975 -0.0181021 4.446839 0.0174457 5.524247 49.27604
975 1628 UPL 31.1851257 0.0004574 0.0060466 0.0371545 2.924050 0.1080094 3.426173 64.91720
976 1630 UPL 29.5393994 0.0000949 0.0022374 0.0331288 3.499643 0.0976174 4.067811 67.68307
977 1631 UPL 21.5318132 0.0000439 -0.0024570 -0.0384294 4.441737 -0.1192614 5.373118 45.28593
978 1633 UPL 3.1953378 0.0000253 -0.0021858 -0.1570620 6.682072 -0.1735365 9.568825 84.30637
979 1634 UPL 29.5804458 0.0006507 0.0121952 0.0728489 2.705456 0.3701993 3.272448 63.07125
980 1635 UPL 23.7807209 0.0000734 -0.0000535 0.0345874 3.862350 0.0837453 4.682982 60.58194
981 1636 UPL 37.8794847 0.0002962 0.0051761 0.0438088 3.714787 0.0903765 3.966016 44.81808
982 1640 UPL 40.2271010 0.0034243 -0.0070466 -0.4464118 2.962981 -0.3074795 3.135597 54.99332
983 1641 UPL 38.9244418 0.0002476 -0.0049019 -0.0366026 5.564600 -0.0330859 5.778373 82.67925
984 1642 UPL 24.4469651 0.0000371 0.0012916 0.0169266 4.122269 -0.0229265 4.911190 72.74721
985 1643 UPL 3.1744002 0.0000553 -0.0036541 -0.2618777 4.497707 -0.6140707 7.439395 37.97788
986 1644 UPL 19.8734305 0.0002476 -0.0061390 0.0212040 2.838179 -0.0724371 3.857327 67.25197
987 1645 UPL 14.7236028 0.0000072 0.0000760 0.0000961 5.635879 -0.0440337 6.972719 65.94238
988 1646 UPL 34.0148555 0.0001642 -0.0057279 -0.0607833 5.070858 -0.1244949 5.464082 57.73146
989 1649 UPL 12.2594514 0.0003620 -0.0115761 -0.2715162 7.776112 -0.5036747 9.302917 61.93921
990 1650 UPL 2.9886911 0.0000173 0.0018097 0.2210660 1.881827 -0.0167911 4.901313 31.77841
991 1651 UPL 26.1394015 0.0001847 0.0000889 -0.0338601 4.766728 0.0128970 5.479539 44.82847
992 1652 UPL 33.9272012 0.0001903 -0.0017602 -0.0014728 3.401802 0.0420257 3.798804 61.53361
993 1653 UPL 33.4047037 0.0006234 -0.0097431 -0.0860854 4.725328 -0.2154227 5.145553 51.37059
994 1654 UPL 11.6916724 0.0000722 -0.0004215 -0.0113914 7.599371 -0.1420686 9.175091 69.45676
995 1655 UPL 20.9032808 0.0000065 -0.0008557 -0.0049208 3.724208 -0.1362180 4.686760 84.03125
996 1657 WET 6.6282442 0.0001353 -0.0065111 -0.2012374 5.487617 -0.3656889 7.677626 73.34669
997 1658 UPL 41.3252395 0.0006039 0.0073397 0.0502081 3.058064 0.0807761 3.186826 67.23232
998 1661 UPL 26.1560377 0.0011259 0.0156418 0.2886075 2.562312 0.6152270 3.282323 63.44406
999 1662 UPL 21.3306191 0.0000574 -0.0029844 -0.0700752 4.074192 -0.0537172 5.014908 69.05472
1000 1663 UPL 14.8081183 0.0001146 0.0005060 0.0134991 9.066260 -0.1842733 10.396973 66.49541
1001 1664 UPL 26.6584737 0.0000543 -0.0005091 -0.0403885 4.846572 -0.0786125 5.535704 44.58940
1002 1666 UPL 21.7644485 0.0002713 -0.0009596 0.0053229 5.768283 -0.0369952 6.690332 28.08215
1003 1667 UPL 23.0793286 0.0008822 0.0153726 0.2646932 2.236674 0.4218827 3.100386 58.49269
1004 1669 UPL 44.3801579 0.0033718 -0.0174389 -0.1363767 5.701711 -0.1849829 5.723462 59.05137
1005 1671 UPL 32.1660884 0.0000987 -0.0045347 -0.0429551 5.600178 -0.1187240 6.064151 36.71326
1006 1672 UPL 13.6762876 0.0000037 0.0006533 0.0352474 8.094342 -0.0362072 9.507671 39.14267
1007 1673 UPL 17.8427036 0.0001969 -0.0052041 -0.0894079 7.202966 -0.1977073 8.337416 43.85158
1008 1674 UPL 44.4680707 0.0006500 -0.0006047 -0.0178714 2.811920 0.2317145 2.830689 42.97518
1009 1677 UPL 11.8866135 0.0000052 -0.0009964 -0.0682313 5.298122 -0.0427399 6.857204 43.34883
1010 1678 UPL 28.0003001 0.0000175 0.0000223 -0.0012943 4.076995 0.0177297 4.708714 72.44621
1011 1679 UPL 7.9362934 0.0002155 -0.0074295 -0.1995652 7.016788 -0.5604228 8.990165 71.40541
1012 1684 UPL 31.7820834 0.0000159 -0.0008807 0.0012632 5.333099 -0.0710760 5.811860 68.81809
1013 1685 UPL 12.5938051 0.0000256 0.0026971 0.0600894 4.105041 0.1119712 5.603962 45.18611
1014 1686 UPL 40.2755884 0.0005452 0.0040805 0.0190901 3.836244 0.0778461 4.002105 58.49965
1015 1688 UPL 51.5030268 0.0028850 0.0074912 -0.0058553 3.213437 0.0133025 2.982712 43.07093
1016 1689 UPL 7.4026358 0.0000646 0.0036883 0.1516048 2.676744 0.2987088 4.718069 76.57815
1017 1690 UPL 8.9658167 0.0000484 -0.0035551 -0.1107450 4.339513 0.0287336 6.193623 81.44705
1018 1691 UPL 42.6184345 0.0003100 0.0046335 -0.0045456 5.206849 0.0067249 5.290092 57.55709
1019 1692 UPL 38.5483430 0.0001480 -0.0022984 -0.0443175 3.929670 -0.2122028 4.156879 49.81457
1020 1694 UPL 19.3840998 0.0001947 0.0034141 0.0510634 5.457940 0.0625026 6.503747 41.06924
1021 1695 UPL 49.0045004 0.0006599 0.0029806 -0.0222172 3.295601 0.0324153 3.155204 51.92373
1022 1697 UPL 34.7677446 0.0001180 0.0015557 0.0441745 3.508139 0.0276926 3.873295 60.77580
1023 1699 UPL 28.2377037 0.0036497 0.0323186 0.5214634 1.757755 0.8850508 2.396212 50.63817
1024 1700 UPL 32.8363894 0.0004024 0.0004912 -0.0489693 3.027874 0.0796254 3.466332 63.67295
1025 1702 UPL 12.5503958 0.0000349 -0.0017757 -0.0037193 2.274261 0.2213782 3.777544 74.59420
1026 1706 UPL 29.0498789 0.0020197 0.0200381 0.2243057 2.475685 0.7560448 3.079815 68.90964
1027 1709 UPL 19.2006713 0.0001071 -0.0031284 -0.0382890 4.847655 -0.0638472 5.907270 77.57027
1028 1712 UPL 22.9306143 0.0000137 -0.0017445 -0.0181913 4.018286 -0.0561442 4.878645 52.17517
1029 1713 UPL 25.3447570 0.0024030 -0.0236269 -0.1990291 6.272006 -0.3523499 7.023002 58.45366
1030 1714 UPL 33.8604542 0.0001436 -0.0048914 -0.0642903 5.338071 -0.1620035 5.737523 74.30570
1031 1715 UPL 7.8187623 0.0001150 0.0009844 0.0310972 2.220318 0.3628841 4.206558 48.45982
1032 1717 UPL 32.7619880 0.0008945 -0.0086658 -0.0792243 6.544354 -0.2621416 6.985215 70.73739
1033 1719 UPL 21.5147813 0.0002606 0.0044836 0.0342712 3.883727 0.1018354 4.816944 61.52543
1034 1721 UPL 35.5822017 0.0003404 -0.0020239 0.0210127 4.164983 0.0243079 4.500742 74.73806
1035 1725 WET 4.9258290 0.0000944 -0.0023728 -0.2002777 3.145895 -0.1987233 5.610425 72.08225
1036 1727 UPL 14.7617840 0.0001097 -0.0048820 -0.0451121 2.934576 0.0648215 4.268494 44.35442
1037 1728 UPL 32.8437761 0.0001933 0.0038247 0.0358120 3.478286 0.1276862 3.916013 70.90505
1038 1729 UPL 17.4722791 0.0000859 -0.0057124 -0.0106970 3.184326 -0.0990010 4.340392 74.06888
1039 1730 WET 3.0921952 0.0000267 -0.0024070 -0.2436546 6.681688 -0.1963392 9.601424 83.79275
1040 1732 UPL 55.5183409 0.0022781 0.0007784 -0.0108347 2.592006 -0.1533365 2.216017 46.05525
1041 1733 UPL 37.6625876 0.0001847 -0.0024741 -0.0421401 3.988934 -0.0554509 4.248214 51.06447
1042 1734 UPL 10.0948662 0.0012290 -0.0148300 -0.4003592 3.442211 -0.5362394 5.214338 33.45297
1043 1735 UPL 46.6556657 0.0008611 -0.0030676 -0.0540246 3.922110 0.0312167 3.864218 65.36025
1044 1736 UPL 27.6550667 0.0002998 0.0042908 -0.0340315 3.516184 0.0643973 4.162553 74.90477
1045 1739 UPL 15.7369549 0.0000220 -0.0027547 -0.0846034 5.473216 -0.0554677 6.740288 47.02157
1046 1740 UPL 33.2450967 0.0016754 0.0159666 0.1320708 3.412892 0.1777162 3.837730 82.15012
1047 1741 UPL 15.7109958 0.0000239 0.0000802 -0.0017231 3.038000 0.0097268 4.307811 69.27114
1048 1742 UPL 44.2225157 0.0006880 0.0094732 0.0715796 3.940888 0.2847787 3.968064 68.52975
1049 1743 UPL 13.2236422 0.0006068 -0.0069353 -0.0795268 4.839252 -0.3418532 6.297889 54.00049
1050 1744 UPL 26.2774006 0.0000910 0.0022978 0.0211842 3.512422 0.0988448 4.218427 66.21539
1051 1745 UPL 17.9476327 0.0001841 0.0024621 0.0539066 6.847063 0.0461726 7.980906 62.86032
1052 1748 UPL 29.9008405 0.0001446 -0.0029276 -0.0479253 5.121695 -0.1708647 5.675607 58.29164
1053 1749 UPL 19.5673712 0.0000295 -0.0021338 -0.0431598 3.977784 -0.0449306 5.012270 75.37972
1054 1750 UPL 22.1760957 0.0019910 -0.0229900 -0.3600722 6.888592 -0.7739538 7.802923 64.62865
1055 1752 UPL 40.0053472 0.0002539 -0.0009995 -0.0105072 3.847109 -0.1498286 4.022388 43.74045
1056 1753 UPL 17.0913658 0.0000465 0.0023937 0.0034116 5.288145 0.1049367 6.469033 66.55052
1057 1754 UPL 25.4919630 0.0006267 0.0086107 0.0500087 5.275364 0.1912889 6.027250 59.95217
1058 1755 WET 9.9096650 0.0000363 -0.0018041 0.0332086 5.291705 -0.1192492 7.037355 77.99001
1059 1756 UPL 18.2466844 0.0001754 0.0069711 0.1161836 4.177805 0.1596901 5.295148 61.81147
1060 1757 UPL 17.7543058 0.0008077 -0.0089792 -0.1473035 5.275407 -0.2724159 6.430098 44.05800
1061 1759 UPL 19.9428495 0.0000079 -0.0010649 -0.0231039 5.545359 -0.1922990 6.559238 51.38067
1062 1760 UPL 11.8310609 0.0000207 -0.0003990 -0.0169082 2.554696 0.0764184 4.119289 51.61084
1063 1761 UPL 33.8103120 0.0002576 0.0042228 -0.0117008 2.840191 0.2426602 3.242103 62.34592
1064 1762 UPL 31.4400605 0.0000270 -0.0017552 -0.0178733 6.554944 -0.0695742 7.047079 61.16726
1065 1764 UPL 21.0261028 0.0002335 -0.0092246 -0.1684314 4.736205 -0.1641526 5.703455 57.15959
1066 1766 UPL 21.1671021 0.0006022 -0.0112423 -0.0860423 11.674444 -0.2861884 12.629120 35.47087
1067 1767 UPL 6.1000217 0.0006951 0.0120263 0.3444747 2.094637 0.6922407 4.334877 56.75650
1068 1768 UPL 10.4055324 0.0000161 0.0013434 0.0502361 4.097094 0.2367945 5.793299 60.59890
1069 1769 UPL 31.4495517 0.0000827 -0.0014553 -0.0429023 5.083916 -0.0692863 5.575799 26.60951
1070 1771 UPL 11.7520664 0.0000718 -0.0041230 -0.1141626 7.240212 -0.2599214 8.810675 80.18233
1071 1772 UPL 25.0900739 0.0005076 -0.0085155 -0.1069632 3.806219 -0.2298728 4.566187 62.62908
1072 1773 UPL 35.6529143 0.0000365 -0.0011841 -0.0053000 6.015601 -0.0668046 6.347911 59.11129
1073 1774 UPL 24.9241424 0.0001259 -0.0018887 -0.0411166 3.323416 -0.0056547 4.090604 50.01642
1074 1776 UPL 24.5314429 0.0001764 0.0036420 0.0110575 3.156744 0.1197861 3.941243 64.91010
1075 1777 UPL 8.6883896 0.0000461 0.0016905 0.0472380 2.800619 0.0536944 4.680384 80.98060
1076 1779 UPL 47.0904498 0.0020249 0.0140122 0.0877843 2.587980 0.3896669 2.514812 67.93386
1077 1780 UPL 52.7005119 0.0024283 0.0119412 0.1208218 3.505346 0.0701859 3.231033 73.79921
1078 1781 UPL 33.0193479 0.0003020 0.0047340 0.0953884 3.458263 0.0743361 3.889956 56.66863
1079 1782 UPL 13.7062605 0.0002246 -0.0067889 -0.1178348 3.404870 -0.1589170 4.830626 58.17471
1080 1783 UPL 21.6770450 0.0001631 0.0001255 0.0051947 2.913941 -0.0265636 3.837150 59.32800
1081 1784 UPL 41.9830459 0.0005975 0.0018537 0.0387427 3.154233 0.2226241 3.260367 69.50731
1082 1785 UPL 28.6798536 0.0000764 -0.0030515 -0.0427983 6.635537 -0.0991001 7.239961 55.31171
1083 1787 UPL 23.0073882 0.0000473 0.0005978 0.0121466 6.173106 -0.0746882 7.029758 68.47998
1084 1788 UPL 8.3814022 0.0001160 0.0062886 0.1957978 4.508688 0.1509989 6.444150 30.69050
1085 1789 UPL 50.1354659 0.0005403 -0.0007444 -0.0227914 3.045704 0.0293106 2.865219 58.69052
1086 1790 UPL 11.3309014 0.0006597 -0.0046019 -0.0023926 4.477105 -0.1459667 6.089064 79.21859
1087 1793 UPL 48.5910243 0.0038375 -0.0082319 -0.1946941 3.354298 -0.2648367 3.224227 69.66153
1088 1794 UPL 42.9874313 0.0005448 0.0045906 0.0450873 2.727346 0.0532149 2.797674 77.51204
1089 1796 UPL 22.2901554 0.0001893 0.0039061 0.0610151 2.763726 0.0576528 3.655578 79.38046
1090 1797 UPL 50.6873582 0.0027856 0.0128620 0.0777176 2.720165 0.1576283 2.520266 60.70090
1091 1798 UPL 47.3583484 0.0015361 0.0086647 0.1568932 3.610938 0.2664008 3.527946 66.76836
1092 1800 UPL 18.0459663 0.0001964 -0.0010990 0.0275725 4.243476 -0.0558615 5.365443 79.14866
1093 1802 UPL 50.8875943 0.0201248 0.0336536 0.3184528 1.682082 0.2409177 1.472187 52.71731
1094 1803 UPL 8.2680807 0.0000236 0.0016491 0.0645534 2.477482 0.2394594 4.407161 64.25031
1095 1804 UPL 7.6766902 0.0002240 0.0050062 0.2515740 2.035592 0.4965490 4.115000 41.06759
1096 1806 UPL 31.8710987 0.0007465 -0.0091635 -0.1278535 5.524825 -0.3500260 6.000462 68.48458
1097 1807 UPL 36.7697068 0.0002309 0.0028278 0.0209947 4.055586 0.1271480 4.347204 50.38556
1098 1809 UPL 25.2558088 0.0005572 0.0018797 0.0087439 2.802355 0.0431832 3.558109 47.75919
1099 1810 UPL 32.2728913 0.0005599 0.0067416 0.0414854 3.141342 0.2615330 3.604852 59.95738
1100 1811 UPL 15.1511317 0.0006472 -0.0117953 -0.3145764 4.534816 -0.4311036 5.876770 54.13879
1101 1812 UPL 15.1101197 0.0000374 -0.0005040 -0.0018253 4.072145 -0.0184648 5.381815 66.70427
1102 1814 UPL 14.0460700 0.0001203 -0.0032912 -0.0642338 3.765931 -0.2183368 5.153039 75.22995
1103 1816 UPL 18.6421303 0.0002948 -0.0102310 -0.1954302 5.488864 -0.3782109 6.576060 50.55397
1104 1817 UPL 44.1094996 0.0000644 0.0011807 0.0409451 3.901646 0.0978289 3.932807 21.68728
1105 1818 UPL 42.5562979 0.0006537 -0.0085421 -0.1026013 3.903683 -0.2648407 3.989151 58.44910
1106 1819 UPL 27.6004231 0.0002227 0.0049910 0.1128482 2.709485 0.0910722 3.358980 66.05297
1107 1820 UPL 5.1614407 0.0000038 0.0009701 0.3962014 1.872468 0.1947730 4.280616 53.75801
1108 1822 WET 3.1265234 0.0000929 0.0056747 0.4187403 1.615703 0.8559100 4.530961 80.01851
1109 1823 UPL 17.5131814 0.0002836 0.0076195 0.0966274 2.893074 0.3407657 4.047841 65.57071
1110 1826 UPL 26.6349351 0.0002187 -0.0013298 -0.0353856 7.276540 -0.1228072 7.966729 71.98368
1111 1827 UPL 18.0069401 0.0019421 0.0182775 0.5860356 2.928109 0.6591981 4.101056 40.84102
1112 1828 UPL 15.6575056 0.0000189 -0.0006713 -0.0703245 3.694387 -0.0402759 4.968091 73.25827
1113 1829 UPL 28.0383599 0.0005478 0.0099743 0.1242220 4.452157 0.3639090 5.085532 75.82020
1114 1831 UPL 17.3528694 0.0003655 -0.0085585 -0.1422754 6.424501 -0.4136834 7.588335 71.52036
1115 1832 UPL 46.0526672 0.0009164 -0.0001969 -0.0065536 3.161403 -0.0489721 3.124632 72.66523
1116 1833 UPL 21.6318108 0.0031026 -0.0274210 -0.3039213 5.017955 -0.5357392 5.959271 53.60945
1117 1834 UPL 22.9121269 0.0001258 -0.0035185 -0.0302372 5.173752 -0.1602248 6.035762 76.83661
1118 1835 UPL 19.9571321 0.0004565 0.0096969 0.2359143 2.621651 0.3036073 3.654218 61.85987
1119 1836 UPL 28.4751502 0.0001477 -0.0025821 -0.0569763 4.232460 -0.0129784 4.848277 65.56699
1120 1838 UPL 20.9202682 0.0000673 -0.0017335 -0.0060277 4.399770 -0.0404645 5.361979 58.41231
1121 1839 UPL 37.4264774 0.0005171 0.0079878 0.0565367 4.848875 0.1130095 5.116600 70.91797
1122 1840 UPL 24.2051890 0.0001484 -0.0067113 -0.0659078 4.358083 -0.1719134 5.158014 59.44318
1123 1841 UPL 30.0184407 0.0001510 0.0056341 0.0679228 4.322002 0.0892522 4.870612 63.62430
1124 1842 UPL 3.7412747 0.0001086 -0.0059483 -0.5530080 5.499174 -0.7381840 8.422122 23.16425
1125 1844 UPL 40.9299367 0.0005773 -0.0063904 -0.0842435 4.056276 -0.1928457 4.200233 51.06060
1126 1845 UPL 18.3040691 0.0000601 -0.0027073 -0.0297548 5.046488 -0.0600887 6.155116 47.43752
1127 1846 UPL 14.9307655 0.0000589 0.0042397 0.0300781 5.194575 -0.0644769 6.516684 49.07622
1128 1848 UPL 20.6450075 0.0002495 0.0033555 0.1366856 2.759685 0.1331239 3.753199 52.58158
1129 1849 UPL 26.9413224 0.0020352 0.0167328 0.4800639 1.803565 0.7111779 2.515305 59.08092
1130 1850 UPL 35.0405473 0.0002260 -0.0060214 -0.0578536 6.613807 -0.1357824 6.968994 51.44525
1131 1851 UPL 15.8721118 0.0000525 0.0041466 0.1232227 4.414156 0.0953287 5.672273 51.51634
1132 1852 UPL 24.5285925 0.0004997 0.0082378 0.1590240 4.135103 0.2493943 4.926622 60.01984
1133 1853 UPL 12.9014192 0.0001818 -0.0071506 -0.1824813 5.700885 -0.1441741 7.197852 59.65738
1134 1854 UPL 3.7486247 0.0000060 -0.0003609 -0.0165577 5.803651 -0.0671622 8.529759 99.27008
1135 1856 UPL 11.7677323 0.0000354 0.0003054 0.0720965 2.897609 -0.0138396 4.471405 54.25637
1136 1858 UPL 20.0794401 0.0005281 -0.0034404 -0.0222342 3.670223 -0.1387031 4.687036 68.97750
1137 1860 UPL 56.2828283 0.0019618 0.0067163 0.0276527 2.922560 0.0548121 2.517631 55.35985
1138 1861 UPL 10.6420083 0.0003990 -0.0047250 -0.1962280 3.234204 -0.3168644 4.914222 61.44148
1139 1862 UPL 12.4650722 0.0001491 0.0018517 0.0353547 3.858364 0.1745951 5.369840 69.03698
1140 1863 UPL 39.5007880 0.0000808 0.0025755 0.0217152 4.005355 0.0856763 4.198532 56.76095
1141 1864 UPL 34.8722913 0.0005847 -0.0102289 -0.1120593 7.688354 -0.1972532 8.050154 54.84852
1142 1865 UPL 12.0075393 0.0001913 0.0063241 0.1532073 3.482564 0.2465431 5.051286 84.16091
1143 1867 UPL 27.0389879 0.0001469 0.0045810 0.0476393 3.182192 0.1163286 3.855147 80.47124
1144 1871 UPL 13.4681576 0.0000098 -0.0011820 -0.0417959 6.814814 -0.0132185 8.244208 60.59326
1145 1872 UPL 37.3410259 0.0002885 0.0041092 0.0192462 2.753987 0.1810236 3.024605 64.11248
1146 1873 UPL 39.1531756 0.0001585 -0.0025932 -0.0414007 3.816955 -0.0517759 4.022837 53.79987
1147 1874 UPL 14.9305700 0.0000169 -0.0002634 0.0130867 5.290849 0.0436182 6.613304 79.00401
1148 1875 UPL 27.9085847 0.0022290 -0.0159769 -0.1333318 4.742036 -0.3447417 5.377670 36.92803
1149 1878 UPL 28.7503175 0.0000707 0.0003344 -0.0245561 5.475733 -0.0442467 6.076514 61.67842
1150 1879 UPL 27.9052352 0.0001139 -0.0032973 -0.0588320 4.379339 -0.1425355 5.016650 63.36754
1151 1881 UPL 12.0267274 0.0000583 0.0018432 0.0196729 4.897265 0.0801383 6.447399 78.45574
1152 1882 UPL 21.8961073 0.0001056 -0.0011651 -0.0575694 4.494665 0.0198760 5.407436 69.07102
1153 1883 UPL 40.2109294 0.0014665 -0.0070116 -0.0617646 3.425730 -0.0294509 3.595473 63.39889
1154 1884 UPL 3.5259497 0.0000027 -0.0006058 -0.0683769 2.658630 -0.3124321 5.446080 14.44622
1155 1886 UPL 23.7759590 0.0000223 0.0026717 0.0366499 4.471959 0.0907084 5.291794 52.42049
1156 1888 UPL 33.5971629 0.0004451 -0.0067558 -0.0283086 4.796602 -0.1416141 5.205881 44.47276
1157 1891 UPL 30.0226592 0.0000201 0.0000765 0.0003449 3.982031 -0.0272048 4.530505 65.74931
1158 1893 UPL 26.5839349 0.0005560 -0.0063215 -0.0846119 5.361942 -0.1696777 6.057575 71.51756
1159 1894 UPL 25.3464532 0.0002522 -0.0071555 -0.1005183 5.498749 -0.2371403 6.247674 46.65756
1160 1895 WET 15.4641736 0.0000416 -0.0024538 -0.0685623 5.815078 -0.1782107 7.104596 61.25670
1161 1896 UPL 20.7337236 0.0002107 -0.0028429 0.0172786 2.723006 -0.0885793 3.696702 41.05243
1162 1897 UPL 5.4230143 0.0000159 -0.0012805 -0.0976038 2.848550 -0.0780806 5.206845 31.97300
1163 1898 UPL 50.6730469 0.0020493 0.0150610 0.1133521 2.748205 0.2606905 2.548752 80.85550
1164 1899 UPL 29.0572761 0.0001502 -0.0008899 -0.0249895 4.072029 0.0263631 4.660041 62.96746
1165 1900 UPL 21.6712887 0.0000466 0.0035033 0.0566399 3.479008 -0.0127958 4.402572 68.33033
1166 1901 UPL 34.5581838 0.0000313 -0.0014793 -0.0266263 3.982382 -0.0090672 4.355635 71.39385
1167 1902 UPL 10.4794655 0.0002546 -0.0036668 -0.1787865 4.022040 -0.4430146 5.790452 68.78680
1168 1903 UPL 17.2787899 0.0001171 -0.0057799 -0.0415583 6.869024 -0.1551742 8.038293 46.46540
1169 1904 UPL 17.1402123 0.0001518 0.0066388 0.1188320 3.302247 0.0523068 4.479550 62.68792
1170 1905 UPL 41.5314906 0.0025161 -0.0019621 -0.0744949 2.947683 -0.4961152 3.069953 41.37266
1171 1906 UPL 13.8200899 0.0001576 0.0042291 0.0513728 2.863551 0.2657432 4.271840 67.69411
1172 1907 UPL 29.0882603 0.0000397 0.0003604 0.0084115 4.078141 -0.0155969 4.664648 46.08252
1173 1909 UPL 3.7346186 0.0000141 0.0014055 0.1612664 3.672516 0.1699363 6.405351 114.62955
1174 1910 UPL 21.1961199 0.0019221 0.0254226 0.3809952 2.193527 0.5942032 3.160688 75.43028
1176 1913 UPL 20.5889857 0.0000077 0.0010351 0.0073606 3.597934 -0.0551400 4.578184 71.65678
1177 1914 UPL 34.1796310 0.0000220 -0.0009877 0.0017195 5.757819 -0.0698690 6.144990 49.44857
1178 1915 UPL 22.0902819 0.0005450 -0.0081674 -0.1090809 8.295931 -0.2972120 9.200071 65.83336
1179 1916 UPL 23.7860865 0.0001257 -0.0063551 -0.0815678 3.612062 -0.1782895 4.431424 70.92864
1180 1917 UPL 47.5178309 0.0008652 0.0097389 0.0854657 2.740389 0.1203632 2.652273 60.43786
1181 1918 UPL 23.1130445 0.0021654 0.0204732 0.2206713 4.116796 0.5478005 4.978120 41.78046
1182 1919 UPL 40.6562617 0.0058755 0.0261664 0.2450623 1.754258 0.4554529 1.907091 76.39840
1183 1921 UPL 36.1243329 0.0003732 -0.0044171 -0.0374788 3.188979 -0.0875634 3.503905 36.65324
1184 1922 UPL 36.6556444 0.0014169 -0.0058221 -0.0303987 2.429187 0.1361314 2.724791 52.38155
1185 1923 UPL 14.5631877 0.0000039 0.0004123 -0.0015284 6.757154 -0.0301612 8.105070 66.33555
1186 1924 UPL 12.9647425 0.0001661 0.0056909 0.0998693 5.325974 0.2362902 6.798186 88.12828
1187 1925 UPL 39.4034598 0.0001382 -0.0028018 -0.0084028 6.284846 0.0134602 6.481577 25.77153
1188 1928 UPL 27.1611148 0.0003469 -0.0094105 -0.1509306 5.947009 -0.2604585 6.616315 41.63465
1189 1930 UPL 8.9923006 0.0000175 -0.0016975 -0.0612222 5.520765 -0.0937873 7.367405 62.99124
1190 1932 UPL 35.7033859 0.0012924 -0.0047048 -0.1189162 3.394752 -0.0855618 3.730504 62.32669
1191 1933 UPL 16.6319799 0.0002494 -0.0067943 -0.1334498 7.360303 -0.2223947 8.568734 52.64027
1192 1934 UPL 12.8987361 0.0000079 0.0002801 -0.0331649 5.464661 -0.0991693 6.939447 65.50835
1193 1935 UPL 15.3651479 0.0002582 -0.0056747 -0.0686077 4.386849 -0.2668441 5.680134 68.89399
1194 1937 UPL 33.4181810 0.0000020 -0.0001917 -0.0009299 5.029868 0.0724073 5.445636 69.89028
1195 1938 UPL 17.1201673 0.0002190 -0.0043991 0.0355730 5.504315 -0.0582251 6.682659 47.55511
1196 1939 UPL 14.8096987 0.0000462 -0.0016410 0.0328593 5.031204 -0.1352158 6.361548 64.36585
1197 1940 UPL 49.0898683 0.0120550 -0.0148473 -0.1225505 3.090889 -0.6906660 2.946587 50.62579
1198 1941 UPL 15.0879898 0.0000147 -0.0005670 0.0462751 5.186994 -0.0561673 6.497869 66.36893
1199 1942 UPL 25.9941755 0.0001700 0.0032636 0.0413656 2.744036 0.0819593 3.462386 27.68598
1200 1945 UPL 31.1299547 0.0001971 0.0027478 0.0075219 6.466517 0.0053123 6.970969 53.57719
1201 1946 UPL 26.1118338 0.0004986 0.0032603 -0.1131066 3.394222 -0.0573838 4.114000 60.88448
1202 1948 UPL 11.6994245 0.0003602 0.0119585 0.2958970 2.201577 0.4763553 3.808930 50.68047
1203 1949 UPL 39.0181040 0.0001844 -0.0010421 0.0052531 3.240939 -0.0246319 3.451413 61.59461
1204 1950 UPL 12.0802107 0.0005172 0.0129935 0.1999477 2.272478 0.6988240 3.816763 70.79832
1205 1951 UPL 29.6596871 0.0002549 0.0064675 0.0085784 2.899155 0.1204645 3.462259 64.87662
1206 1954 UPL 29.1522889 0.0000288 0.0015954 0.0249173 4.301462 -0.0184504 4.885427 42.14378
1207 1955 UPL 22.7578567 0.0000826 -0.0035072 -0.0255531 7.170815 0.0302938 8.039520 62.98900
1208 1958 UPL 19.5649154 0.0019345 0.0107018 0.0951537 2.433536 0.1255030 3.491310 54.92009
1209 1959 UPL 15.6247107 0.0000157 0.0001966 0.0442914 5.871941 -0.0564267 7.149328 93.62708
1210 1961 UPL 27.9351165 0.0002403 0.0063644 0.0851351 3.680879 0.1417428 4.315715 122.91132
1211 1963 UPL 18.7140785 0.0000732 0.0013326 0.0533482 5.680498 0.0512066 6.764176 32.64852
1212 1964 UPL 12.3918381 0.0000913 -0.0039232 -0.0385392 5.265008 -0.0512059 6.780488 49.66694
1213 1965 UPL 38.7785374 0.0014060 0.0052732 0.0077451 2.983099 0.2492662 3.203324 50.02744
1214 1966 UPL 13.8288296 0.0002394 -0.0075062 -0.1187832 6.124822 -0.3311282 7.529903 44.24569
1215 1967 UPL 16.8787927 0.0000606 -0.0008960 -0.0091166 5.404721 -0.0880495 6.597474 45.27995
1216 1968 UPL 14.4923996 0.0000785 -0.0031246 -0.0225825 4.578582 -0.1302106 5.932831 50.56490
1217 1970 UPL 13.1480717 0.0001272 0.0027429 0.0588625 3.986543 0.0655270 5.449978 61.77018
1218 1971 UPL 14.4677069 0.0000349 0.0031102 0.0535086 4.396764 0.0442557 5.760271 69.86634
1219 1972 UPL 11.3000979 0.0000695 0.0035173 0.0966149 3.677821 0.2587555 5.290693 42.78425
1220 1973 UPL 20.4301098 0.0001357 0.0046761 0.1587809 5.912402 0.0337135 6.914885 70.89792
1221 1975 UPL 51.9953110 0.0057119 -0.0089887 -0.2763397 3.960043 -0.3717182 3.712530 47.50550
1222 1977 UPL 10.9891092 0.0001685 0.0070492 0.2052502 2.824832 0.5271627 4.484540 70.31704
1223 1979 UPL 20.5217297 0.0001554 0.0028873 0.0466506 3.902033 0.0688166 4.885275 63.38346
1224 1981 UPL 49.9939731 0.0015418 -0.0117159 -0.1607346 4.442914 -0.0824787 4.266803 41.72985
1225 1982 UPL 40.5876617 0.0002253 0.0010794 0.0649405 3.120779 0.1073731 3.276744 49.23244
1226 1983 UPL 14.4988473 0.0000413 -0.0017447 0.0061025 3.741198 0.0043552 5.093852 80.14283
1227 2346 PUBH WET 7.7856887 0.0000734 0.0055874 0.1699672 2.871140 0.3425894 4.865627 73.03005
1228 2347 PUBH WET 8.5747558 0.0000713 -0.0023696 -0.0183654 6.864444 -0.2410798 8.758274 79.32412
1229 2348 PUBH WET 16.6571378 0.0005393 -0.0006999 -0.1723487 3.876908 -0.2334413 5.090547 102.39413
1230 2349 R5UBH WET 8.0502400 0.0000044 0.0012851 0.0310040 3.588364 0.0899888 5.544756 106.60732
1231 2350 R5UBH WET 5.4905578 0.0001040 0.0059632 0.1992185 1.850419 0.3074145 4.201063 79.64236
1232 2351 PEM1/SS1B WET 9.0089679 0.0000080 0.0013379 0.0546845 3.387223 0.0458459 5.229740 103.28795
1233 2352 PUBH WET 4.9124354 0.0000020 0.0002752 0.0185076 3.727203 0.0119549 6.181463 73.30175
1234 2353 PUBH WET 3.0124558 0.0000014 0.0005023 0.0107791 3.534688 0.2594111 6.479995 80.87714
1235 2354 PUBH WET 4.2454964 0.0000040 -0.0000718 -0.0634016 2.801563 0.2775791 5.419025 69.32370
1236 2355 PUBH WET 4.0917455 0.0000007 0.0004341 0.0189123 4.082494 0.0677401 6.720071 78.54405
1237 2356 WET 6.1564175 0.0000085 -0.0010450 -0.0445663 3.417946 -0.0933736 5.645060 94.71874
1238 2357 WET 2.4510467 0.0000213 0.0005864 0.1846342 1.977895 0.0462602 5.244996 53.94723
1239 2358 WET 1.3512364 0.0000137 0.0016301 0.3295233 1.935102 0.7828743 5.764578 81.58350
1240 2359 WET 4.4039536 0.0000351 0.0017718 0.1193891 2.329766 0.2863357 4.898329 113.12352
1241 2360 WET 7.2075415 0.0000333 0.0007161 0.0309292 2.717183 0.2569110 4.785846 113.71586
1242 2361 WET 0.1407653 0.0000001 -0.0000909 -0.2108148 4.084918 -0.9255051 10.209278 49.67929
1243 2362 WET 0.0715102 0.0000000 -0.0000427 -0.2505813 2.472944 -0.0635346 9.277394 55.34324
1244 2363 WET 3.5782681 0.0001042 -0.0017612 -0.1178425 4.483142 -0.1569896 7.318974 91.81788
1245 2364 WET 6.9379792 0.0001217 0.0035209 0.0686421 2.933369 0.1887096 5.045170 71.03002
1246 2365 WET 7.2219033 0.0003024 -0.0052136 -0.0041923 3.165516 -0.2408050 5.251446 70.63783
1247 2366 WET 9.4081582 0.0000731 0.0002757 -0.0029041 5.250232 -0.0043228 7.051845 72.66846
1248 2367 WET 7.7209431 0.0000198 -0.0015221 -0.1007244 4.747291 -0.1115434 6.746962 68.76700
1249 2368 WET 12.0919969 0.0000265 0.0024467 0.0471528 5.578155 0.0963258 7.118949 75.19821
1250 2369 WET 14.3300523 0.0000686 0.0030748 0.1276939 5.130532 0.1931650 6.495521 73.17254
1251 2370 WET 13.1773015 0.0000373 -0.0023989 -0.0305308 5.289203 -0.1553920 6.741943 71.91042
1252 2371 WET 10.3278086 0.0000165 -0.0008863 -0.0461060 4.735849 0.0187108 6.440794 70.77794
1253 2372 WET 13.7329578 0.0001438 -0.0066707 -0.1571521 5.192664 -0.2336556 6.606123 72.08819
1254 2373 WET 15.6009573 0.0000985 0.0013808 0.0648012 4.381144 0.0449892 5.657935 63.38163
1255 2374 WET 8.6885077 0.0000084 0.0009880 0.0456518 3.682645 0.0943842 5.561469 104.12556

Finally, we can set up our Random Forest like we meant to with those libraries. Because Random Forest is random we are going to use set.seed to specify a specific kind of random so we can get repeatable results

Next we use the randomForest function. Be sure to check out ?randomForest. We need to set our parameters within the function correctly

  1. y: our dependent variable, which is class or wetland/upland

    • Note, I used pts_extract[,3] here to subset thepts_extract` dataframe. This code says [take all rows, but only column 3]
    • I also specified it as a factor to make sure we do a classification

  2. x: our predictors/covariates/independent variables, which is our lidar metrics

    • This code is similar to y but says [take all rows, but only columns 4 to 10] for 7 predictor variables

  3. ntree the number of trees to grow in our forest

  4. importance for assessing the predictors/covariates/independent variables

  5. confusion for a confusion matrix related to the classification evaluation

set.seed(42)

rf_model <- randomForest(y = as.factor(pts_extract[,3]), x = pts_extract[,4:10], ntree = 300, importance = TRUE, confusion = TRUE)

rf_model
## 
## Call:
##  randomForest(x = pts_extract[, 4:10], y = as.factor(pts_extract[,      3]), ntree = 300, importance = TRUE, confusion = TRUE) 
##                Type of random forest: classification
##                      Number of trees: 300
## No. of variables tried at each split: 2
## 
##         OOB estimate of  error rate: 14.41%
## Confusion matrix:
##     UPL WET class.error
## UPL 882  75  0.07836991
## WET 105 187  0.35958904

Wooh! Ok so what did we get?

Looking at the output above, we have a Random Forest classification model with an OOB error of 14.41% WHat?

We can see how the OOB error decreases as we add more trees to the Random Forest

plot(rf_model)

But besides the OOB error what else is there. Well we can see a confusion matrix

rf_model$confusion
##     UPL WET class.error
## UPL 882  75  0.07836991
## WET 105 187  0.35958904

This shows the error by class. Although we had a low overall error, we can see that we had a high wetland error of 35% or 65% accuracy, not the best… It looks like \(\frac{105}{(105+187)}\) wetland points were classified as uplands. But we did pretty well on uplands ~93% accuracy.

We can also check out our variable importance plot

varImpPlot(rf_model)

Looks like we relied on slope a lot for this model for accurate predictions

QUESTION 10: Why do you think our wetland accuracy was low? Plot the NWI_training_pts_prj with some of the layers you generated. Do you see any metrics that are helpful or hurt the model? You can use some examples like the code below or bring them into ArcGIS Pro. Take a screenshot of the points overlaid on top of a metric of your choice

plot(slope) #METRIC
plot(NWI_training_pts_prj, "class" , type = "classes", cex = 0.5, add = T)

mapView(sf::st_as_sf(NWI_training_pts_prj), map.types = "Esri.WorldImagery")
  • Another thing to recognize about Random Forest is there is usually a validation dataset. This is super important to know because the validation dataset is what is used to actually evaluate our model. It is a set of data held out of the Random Forest algorithm separate from our training dataset so we aren’t biasing the results. Even further is having another additional testing dataset outside of both the training and validation datasets. But our coworker spent a lot of time just making the training data so we won’t be going into those.

There are also many, many ways to evaluate Random Forest and make sure your predictions are accurate and unbiased and I would encourage you to learn about them and to take classes that focus on Machine Learning. You should do this before doing actual consulting jobs besides this lab

QUESTION 11: What is one thing about Random Forest that you learned, one thing you are still confused about, and one way you think you could apply random forest to in another hypothetical project?

What we can move onto is prediction from our Random Forest model.

Predicting our results to a map is basically taking each pixel in our stack and generating a Random Forest prediction based off the model results.

Each pixel has values that go through the decision tree and come out as either wetland or upland. While we can get just a basic classification like that. We can also get a wetland probability for each pixel which better represents the uncertainty in our model. I use the index=2 to specify the WET class.

predict(stack, rf_model, type="prob", index=2, na.rm=TRUE, progress="window", overwrite=TRUE, filename="Lab 7/Lab72023/RF_prediction.tif")
## class       : SpatRaster 
## dimensions  : 600, 750, 1  (nrow, ncol, nlyr)
## resolution  : 5, 5  (x, y)
## extent      : 526500, 530250, 6457500, 6460500  (xmin, xmax, ymin, ymax)
## coord. ref. : NAD83 / UTM zone 8N (EPSG:26908) 
## source      : RF_prediction.tif 
## name        : lyr1 
## min value   :    0 
## max value   :    1

We wrote the prediction to another raster. The result is a raster with 0-1 values in the pixels representing the probability of a Wetland

RF_prediction <- rast("Lab 7/Lab72023/RF_prediction.tif")

plot(RF_prediction, main = "Wetland Probability")

We can overlay this onto a mapview map too.

mapview(raster::raster(RF_prediction), map.types = "Esri.WorldImagery", alpha = 0.5)

QUESTION 12: Play around with your new wetland probability map and take a screenshot of it with a different color scheme than the one shown here using the col.regions argument in the mapview function. Alternatively you can take the raster into ArcGIS and examine it there if you hate R now. Zoom into 1 area you think is interesting based on the model results. Why was this place interesting?

QUESTION 13: Similar to the previous question but include a separate plot with NWI wetlands with the model results. Zoom into 1 area that differs between the NWI and the model. Why do you think this place had different predictions from the NWI? Look back at some of the plot code from here or use ArcGIS to make a map