bhh | Bounding Halfspace Hierarchy | Widget library
kandi X-RAY | bhh Summary
kandi X-RAY | bhh Summary
Above we have an array of 15 triangle structures [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14] where each structure contains the three values {minX, minY, -(maxX+maxY)}. In 2D any 3 or more axes will do*, but we chose these for convenience. It is possible to sort the entire array by increasing value of minX. Then, we can consider the median triangle (#7) to be done. Next, we sort each of the two subarrays [0,1,2,3,4,5,6] and [8,9,10,11,12,13,14] by minY. Then, we can consider their median triangles (#3 and #11) to be done. Next, we can sort each of the four subarrays [0,1,2] and [4,5,6] and [8,9,10] and [12,13,14] by -(maxX+maxY). Then we can consider their median triangles (#1 and #5 and #9 and #13) to be done. If we try to sort the arrays on either side of the newly-done medians, we find that each contains only one item, and so there is no more sorting to do. But if there were sorting left to do, we would cycle through the same three half-spaces, over and over again.
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QUESTION
I am simply trying to merge/join to dataframes based on their latitudes. Whenever I run the below code it will only return a dataframe in which some of the latitudes have matched up. I have tried converting the "Lat" column from number to character and that did not have any effect. I have also tried using the join function in plyr without any luck. I am not sure where to go from here. Thank you.
...ANSWER
Answered 2020-May-02 at 15:40If you merge by latitude and longitude after rounding the numbers to 5 digits, you'll obtain a complete set of matches for df1
.
QUESTION
I am trying to run through an old coworkers script and I am hoping someone can help inform me of what exactly he did during this code segment. Earlier in the script we calculated the intake rate for several prey species and now it appears that we are grouping them based on unique locations. The section of code after this requires that there be 41 rows ( 1 row for each unique location in the complete dataset). I believe that the code subsets the data based on latitude and then adds an 'alpha' column. The main issue that I am having is what is this line calculating: x= x + d$Intakerate_kjday[j]*d$alpha[j]
. For locations that had several prey items (profit.fall.38.4.959) is this code summing up "intakerate_kjday" and "alpha" and then multiplying them together? When the code is performed I receive the error Error in
ANSWER
Answered 2020-Apr-23 at 15:37The code in question apparently computes (very inefficiently and inaccurately)
QUESTION
This is for anyone if they are looking for a solution of such kind of a problem. I will try to explain the problem here and put the answer after that.
Question
I have two csv
files (file1.csv
and file2.csv
): in which cookieid
column is common in both. When I load it in dataframe it looks like this for ex:
ANSWER
Answered 2018-Mar-23 at 09:10import pandas as pd
import numpy as np
import string
DATA_FILE1 = 'file1.csv'
## encodings vary from file to file
df1 = pd.read_csv(DATA_FILE1,sep=',',encoding='ISO-8859-1')
DATA_FILE2 = 'file2.csv'
df2 = pd.read_csv(DATA_FILE2,sep=',',encoding='ISO-8859-1')
#creating a list of unique cookieids from both the files
cookielist =[]
uniqcookie1 = df1["CookieID"].unique()
uniqcookie2 = df2["CookieID"].unique()
for i in uniqcookie1:
cookielist.append(i)
for j in uniqcookie2:
cookielist.append(j)
for i in cookielist:
dfout1 = df1.loc[df1['CookieID'] == i]
dfout2 = df2.loc[df2['CookieID'] == i]
bigdata = pd.concat([dfout1, dfout2], axis=1)
k = bigdata.loc[:, bigdata.columns != 'CookieID']
k.to_csv(str(i)+".txt",header=None, index=None, sep=' ', mode='a')
QUESTION
My dataframe looks like this:
...ANSWER
Answered 2018-Mar-10 at 19:06I think need:
QUESTION
My dataframe looks like this:
...ANSWER
Answered 2018-Mar-08 at 13:45I'd do it this way:
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