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Ryan Hill ryan-hill

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@gakuba
gakuba / metrics.py
Last active July 16, 2018 23:32
module to display roc_au of multilabel classifier
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import roc_curve, auc
from scipy import interp
from itertools import cycle
def roc_auc(y_test, y_score, n_classes):
"""Plots ROC curve for micro and macro averaging."""
# Compute ROC curve and ROC area for each class
@gakuba
gakuba / MultiLabelClassification-with-nltk.ipynb
Last active April 1, 2020 16:11
In this Tutorial, I explain how to do Multi label classification of texts using nltk
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@debboutr
debboutr / walk_up.py
Last active October 19, 2017 16:50
recursive function to walk up flow table. NOTE: this will not handle braided flowlines!
from StreamCat_functions import dbf2DF
pre = 'D:/NHDPlusV21/NHDPlusGL/NHDPlus04'
fline = dbf2DF('D%s/NHDSnapshot/Hydrography/NHDFlowline.dbf' % pre)
flow = dbf2DF('%s/NHDPlusAttributes/PlusFlow.dbf' pre)[['TOCOMID','FROMCOMID']]
def recurs(val, ups):
print val
ups = ups + flow.ix[flow.TOCOMID == val].FROMCOMID.tolist()
if 0 in ups:
@mhweber
mhweber / SpatialLinesEndPoints.R
Created February 1, 2016 21:23
Function to build a spatial points data frame of end nodes using a spatial lines data frame - function is a modification of SpatialLinesMidPoints in the R maptools package.
SpatialLinesEndPoints = function(sldf){
Lns <- slot(sldf, "lines")
hash_lns <- sapply(Lns, function(x) length(slot(x, "Lines")))
N <- sum(hash_lns)
endpoints <- matrix(NA, ncol = 2, nrow = N)
Ind <- integer(length = N)
ii <- 1
for (i in 1:length(Lns)) {
Lnsi <- slot(Lns[[i]], "Lines")
for (j in 1:hash_lns[i]) {
@ryan-hill
ryan-hill / float2int.py
Created October 14, 2015 00:35 — forked from bryanluman/float2int.py
Converting a large DEM with rasterio
import numpy as np
import rasterio
"""
2014-02-13
Bryan Luman
Use it however you like at your own risk
Problem:
I have a huge DEM converted from LiDAR LAS points. I'd like to make it slightly
@mhweber
mhweber / Lookup.R
Last active October 1, 2015 19:10
This code takes a lookup table and applies it to a data frame, updating only values for records that occur in the lookup table using indexing and match
# Create data frame 1
x = c("ID1","ID2","ID3","ID4","ID5")
y = c("C1","C2","C3","C4","C5")
d1 = data.frame("SiteID" = x, "Value" = y)
d1
# Create lookup table
x = c("ID2","ID5")
y = c("C5","C2")
lookup = data.frame("SiteID" = x, "Value" = y)
lookup
@bryanluman
bryanluman / float2int.py
Last active December 2, 2020 03:57
Converting a large DEM with rasterio
import numpy as np
import rasterio
"""
2014-02-13
Bryan Luman
Use it however you like at your own risk
Problem:
I have a huge DEM converted from LiDAR LAS points. I'd like to make it slightly