I performed a CFA using the lavaan package
require('lavaan');
HS.model <- 'external_regulation_soc =~ JOBMOTIVATIE_extsoc1 +
JOBMOTIVATIE_extsoc2 + JOBMOTIVATIE_extsoc3
external_regulation_mat =~ JOBMOTIVATIE_extmat1 +
JOBMOTIVATIE_extmat2 + JOBMOTIVATIE_extmat3
introjected_regulation =~ JOBMOTIVATIE_introj1 +
JOBMOTIVATIE_introj2 + JOBMOTIVATIE_introj3 +
JOBMOTIVATIE_introj4
identified_regulation =~ JOBMOTIVATIE_ident1 +
JOBMOTIVATIE_ident2 + JOBMOTIVATIE_ident3
intrinsic_motivation =~ JOBMOTIVATIE_intrin1 +
JOBMOTIVATIE_intrin2 + JOBMOTIVATIE_intrin3'
fit <- cfa(HS.model, data = dataset, scores="regression")
summary(fit, fit.measures=TRUE, standardized=TRUE)
I managed to get the values for the five factors in a seperate dataset using
data_factor <- predict(fit)
But I need these 5 factors (as columns, as variables), added to my original dataset. How can I achieve this?
I tried cbind, but got an error:
factorERS <- select(dataset, JOBMOTIVATIE_extsoc1 +
JOBMOTIVATIE_extsoc2 + JOBMOTIVATIE_extsoc3)
data_CFA <- cbind(dataset, fit$scores)
Error in fit$scores : $ operator not defined for this S4 class
Thanks for helping me out!
Related
I'm trying to implement to RK45 for a two body problem with the earth and sun but keep getting a division by zero that I don't understand. It seems to be in the norme from the accelerations function that the division occurs but I don't see how that can be or how to fix it. Here is code:
from scipy import optimize
from numpy import linalg as LA
import matplotlib.pyplot as plt
from scipy.optimize import fsolve
import numpy as np
AU=1.5e11
a=AU
e=0.5
mss=2E30
ms = 2E30
me = 5.98E24
mv=4.867E24
yr=3.15e7
h=100
mu1=ms*me/(ms+me)
mu2=ms*me/(ms+me)
G=6.67E11
step=24
vi=np.sqrt(G*ms*(2/(a*(1-e))-1/a))
#sun=sphere(pos=vec(0,0,0),radius=0.1*AU,color=color.yellow)
#earth=sphere(pos=vec(1*AU,0,0),radius=0.1*AU)
sunpos=np.array([-903482.12391302, -6896293.6960525, 0. ])
earthpos=np.array([a*(1-e),0,0])
earthv=np.array([0,vi,0])
sunv=np.array([0,0,0])
def accelerations(t,earthposs, sunposs):
norme=sum( (earthposs-sunposs)**2 )**0.5
gravit = G*(earthposs-sunposs)/norme**3
sunaa = me*gravit
earthaa = -ms*gravit
return earthaa, sunaa
def ode45(f,t,y,h):
"""Calculate next step of an initial value problem (IVP) of an ODE with a RHS described
by the RHS function with an order 4 approx. and an order 5 approx.
Parameters:
t: float. Current time.
y: float. Current step (position).
h: float. Step-length.
Returns:
q: float. Order 2 approx.
w: float. Order 3 approx.
"""
s1 = f(t, y[0],y[1])
s2 = f(t + h/4.0, y[0] + h*s1[0]/4.0,y[1] + h*s1[1]/4.0)
s3 = f(t + 3.0*h/8.0, y[0] + 3.0*h*s1[0]/32.0 + 9.0*h*s2[0]/32.0,y[1] + 3.0*h*s1[1]/32.0 + 9.0*h*s2[1]/32.0)
s4 = f(t + 12.0*h/13.0, y[0] + 1932.0*h*s1[0]/2197.0 - 7200.0*h*s2[0]/2197.0 + 7296.0*h*s3[0]/2197.0,y[1] + 1932.0*h*s1[1]/2197.0 - 7200.0*h*s2[1]/2197.0 + 7296.0*h*s3[1]/2197.0)
s5 = f(t + h, y[0] + 439.0*h*s1[0]/216.0 - 8.0*h*s2[0] + 3680.0*h*s3[0]/513.0 - 845.0*h*s4[0]/4104.0,y[1] + 439.0*h*s1[1]/216.0 - 8.0*h*s2[1] + 3680.0*h*s3[1]/513.0 - 845.0*h*s4[1]/4104.0)
s6 = f(t + h/2.0, y[0] - 8.0*h*s1[0]/27.0 + 2*h*s2[0] - 3544.0*h*s3[0]/2565 + 1859.0*h*s4[0]/4104.0 - 11.0*h*s5[0]/40.0,y[1] - 8.0*h*s1[1]/27.0 + 2*h*s2[1] - 3544.0*h*s3[1]/2565 + 1859.0*h*s4[1]/4104.0 - 11.0*h*s5[1]/40.0)
w1 = y[0] + h*(25.0*s1[0]/216.0 + 1408.0*s3[0]/2565.0 + 2197.0*s4[0]/4104.0 - s5[0]/5.0)
w2 = y[1] + h*(25.0*s1[1]/216.0 + 1408.0*s3[1]/2565.0 + 2197.0*s4[1]/4104.0 - s5[1]/5.0)
q1 = y[0] + h*(16.0*s1[0]/135.0 + 6656.0*s3[0]/12825.0 + 28561.0*s4[0]/56430.0 - 9.0*s5[0]/50.0 + 2.0*s6[0]/55.0)
q2 = y[1] + h*(16.0*s1[1]/135.0 + 6656.0*s3[1]/12825.0 + 28561.0*s4[1]/56430.0 - 9.0*s5[1]/50.0 + 2.0*s6[1]/55.0)
return w1,w2, q1,q2
t=0
T=10**5
xarray=[]
yarray=[]
while t<T:
ode45(accelerations,t,[earthpos,sunpos],h)
earthpos=ode45(accelerations,t,[earthpos,sunpos],h)[1]
sunpos=ode45(accelerations,t,[earthpos,sunpos],h)[3]
xarray.append(ode45(accelerations,t,[earthpos,sunpos],h)[0][0])
yarray.append(ode45(accelerations,t,[earthpos,sunpos],h)[0][1])
print(ode45(accelerations,t,[earthpos,sunpos],h)[0][0],ode45(accelerations,t,[earthpos,sunpos],h)[0][1])
t=t+h
plt.plot(xarray,yarray)
plt.savefig('orbit.png')
plt.show()
After the second iteration the code comes back with only nan values for the earthpos.
Numerical integration methods usually integrate first order systems, y'=f(t,y). You want to integrate a second order ODE system y''=f(t,y) which you first need to turn into a first order system.
Why do you not use the vector class of numpy?
Why do you perform the same computation with the same arguments multiple times instead of catching all return values once and then distributing them to the lists?
You could also use scipy.integrate.solve_ivp with the "RK45" method instead of programming it yourself.
There was an earlier question on this, but the asker was just overwriting their output and solved their own problem.
I'm using a subprocess.popen to read video information and write the output to a json. It works fine on MOST videos, but on others is returning an empty string on others - even though it runs fine from the command line. I tried it several times and am getting the data fine through the command line.
Here's the relevant part of the script:
out_prj.write('[')
for m, i in enumerate(files):
print i
out_prj.write('{"$type":"BatchProcessor.Job, BatchProcessor","Id":0,"Ver":1.02,"CurrentTask":0,"IsSelected":true,"TaskList":[')
f_name = os.path.basename(i[0])
f_json = out_folder + os.sep + "06_Output" + os.sep + os.path.basename(i[0]).split(".")[0] + ".json"
trans_f = out_folder + os.sep + "04_Video" + os.sep + os.path.basename(i[0]).split(".")[0] + "-tr.ts"
trans_f_out = out_folder + os.sep + "06_Output" + os.sep + os.path.basename(i[0]).split(".")[0] + "-tr-out.ts"
ffprobe = 'ffprobe.exe'
command = [ffprobe, '-v', 'quiet', '-print_format', 'json', '-show_format', '-show_streams', i[0]]
p = sp.Popen(command, stdout=sp.PIPE, stderr=sp.PIPE, shell=True)
out, err = p.communicate()
io = cStringIO.StringIO(out)
info = json.load(io)
print info
filea = open(f_json, 'w')
filea.write(json.dumps(info))
filea.close()
f = open(f_json)
b = json.load(f)
print b
#########################
###################
f_format = str(b['streams'][0]['codec_long_name'])
Your code ignores error messages (err variable). print err or don't redirect stderr to see them.
Unrelated: the json handling in your code is insane: most operations are redundant.
To save output of the subprocess to a file:
import os
from subprocess import check_call
f_json = os.path.join(out_folder, "06_Output",
os.path.splitext(f_name)[0] + ".json")
with open(f_json, 'wb', 0) as file:
check_call(command, stdout=file)
Note: shell=True is not necessary here. If subprocess can't find ffprobe.exe then specify the full path e.g. (use the path appropriate for your system):
ffprobe = r'C:\Program Files\Real\RealPlayer\RPDS\Tools\ffmpeg\ffprobe.exe'
Note: r'' -- a raw string literal is used to avoid doubling the backslashes.
I have a very big polygon shapefile with hundreds of features, often overlapping each other. Each of these features has a value stored in the attribute table. I simply need to calculate the average values in the areas where they overlap.
I can imagine that this task requires several intricate steps: I was wondering if there is a straightforward methodology.
I’m open to every kind of suggestion, I can use ArcMap, QGis, arcpy scripts, PostGis, GDAL… I just need ideas. Thanks!
You should use the Union tool from ArcGIS. It will create new polygons where the polygons overlap. In order to keep the attributes from both polygons, add your polygon shapefile twice as input and use ALL as join_attributes parameter.This creates also polygons intersecting with themselves, you can select and delete them easily as they have the same FIDs. Then just add a new field to the attribute table and calculate it based on the two original value fields from the input polygons.
This can be done in a script or directly with the toolbox's tools.
After few attempts, I found a solution by rasterising all the features singularly and then performing cell statistics in order to calculate the average.
See below the script I wrote, please do not hesitate to comment and improve it!
Thanks!
#This script processes a shapefile of snow persistence (area of interest: Afghanistan).
#the input shapefile represents a month of snow cover and contains several features.
#each feature represents a particular day and a particular snow persistence (low,medium,high,nodata)
#these features are polygons multiparts, often overlapping.
#a feature of a particular day can overlap a feature of another one, but features of the same day and with
#different snow persistence can not overlap each other.
#(potentially, each shapefile contains 31*4 feature).
#the script takes the features singularly and exports each feature in a temporary shapefile
#which contains only one feature.
#Then, each feature is converted to raster, and after
#a logical conditional expression gives a value to the pixel according the intensity (high=3,medium=2,low=1,nodata=skipped).
#Finally, all these rasters are summed and divided by the number of days, in order to
#calculate an average value.
#The result is a raster with the average snow persistence in a particular month.
#This output raster ranges from 0 (no snow) to 3 (persistent snow for the whole month)
#and values outside this range should be considered as small errors in pixel overlapping.
#This script needs a particular folder structure. The folder C:\TEMP\Afgh_snow_cover contains 3 subfolders
#input, temp and outputs. The script takes care automatically of the cleaning of temporary data
import arcpy, numpy, os
from arcpy.sa import *
from arcpy import env
#function for finding unique values of a field in a FC
def unique_values_in_table(table, field):
data = arcpy.da.TableToNumPyArray(table, [field])
return numpy.unique(data[field])
#check extensions
try:
if arcpy.CheckExtension("Spatial") == "Available":
arcpy.CheckOutExtension("Spatial")
else:
# Raise a custom exception
#
raise LicenseError
except LicenseError:
print "spatial Analyst license is unavailable"
except:
print arcpy.GetMessages(2)
finally:
# Check in the 3D Analyst extension
#
arcpy.CheckInExtension("Spatial")
# parameters and environment
temp_folder = r"C:\TEMP\Afgh_snow_cover\temp_rasters"
output_folder = r"C:\TEMP\Afgh_snow_cover\output_rasters"
env.workspace = temp_folder
unique_field = "FID"
field_Date = "DATE"
field_Type = "Type"
cellSize = 0.02
fc = r"C:\TEMP\Afgh_snow_cover\input_shapefiles\snow_cover_Dec2007.shp"
stat_output_name = fc[-11:-4] + ".tif"
#print stat_output_name
arcpy.env.extent = "MAXOF"
#find all the uniquesID of the FC
uniqueIDs = unique_values_in_table(fc, "FID")
#make layer for selecting
arcpy.MakeFeatureLayer_management (fc, "lyr")
#uniqueIDs = uniqueIDs[-5:]
totFeatures = len(uniqueIDs)
#for each feature, get the date and the type of snow persistence(type can be high, medium, low and nodata)
for i in uniqueIDs:
SC = arcpy.SearchCursor(fc)
for row in SC:
if row.getValue(unique_field) == i:
datestring = row.getValue(field_Date)
typestring = row.getValue(field_Type)
month = str(datestring.month)
day = str(datestring.day)
year = str(datestring.year)
#format month and year string
if len(month) == 1:
month = '0' + month
if len(day) == 1:
day = '0' + day
#convert snow persistence to numerical value
if typestring == 'high':
typestring2 = 3
if typestring == 'medium':
typestring2 = 2
if typestring == 'low':
typestring2 = 1
if typestring == 'nodata':
typestring2 = 0
#skip the NoData features, and repeat the following for each feature (a feature is a day and a persistence value)
if typestring2 > 0:
#create expression for selecting the feature
expression = ' "FID" = ' + str(i) + ' '
#select the feature
arcpy.SelectLayerByAttribute_management("lyr", "NEW_SELECTION", expression)
#create
#outFeatureClass = os.path.join(temp_folder, ("M_Y_" + str(i)))
#create faeture class name, writing the snow persistence value at the end of the name
outFeatureClass = "Afg_" + str(year) + str(month) + str(day) + "_" + str(typestring2) + '.shp'
#export the feature
arcpy.FeatureClassToFeatureClass_conversion("lyr", temp_folder, outFeatureClass)
print "exported FID " + str(i) + " \ " + str(totFeatures)
#create name of the raster and convert the newly created feature to raster
outRaster = outFeatureClass[4:-4] + ".tif"
arcpy.FeatureToRaster_conversion(outFeatureClass, field_Type, outRaster, cellSize)
#remove the temporary fc
arcpy.Delete_management(outFeatureClass)
del SC, row
#now many rasters are created, representing the snow persistence types of each day.
#list all the rasters created
rasterList = arcpy.ListRasters("*", "All")
print rasterList
#now the rasters have values 1 and 0. the following loop will
#perform CON expressions in order to assign the value of snow persistence
for i in rasterList:
print i + ":"
inRaster = Raster(i)
#set the value of snow persistence, stored in the raster name
value_to_set = i[-5]
inTrueRaster = int(value_to_set)
inFalseConstant = 0
whereClause = "Value > 0"
# Check out the ArcGIS Spatial Analyst extension license
arcpy.CheckOutExtension("Spatial")
print 'Executing CON expression and deleting input'
# Execute Con , in order to assign to each pixel the value of snow persistence
print str(inTrueRaster)
try:
outCon = Con(inRaster, inTrueRaster, inFalseConstant, whereClause)
except:
print 'CON expression failed (probably empty raster!)'
nameoutput = i[:-4] + "_c.tif"
outCon.save(nameoutput)
#delete the temp rasters with values 0 and 1
arcpy.Delete_management(i)
#list the raster with values of snow persistence
rasterList = arcpy.ListRasters("*_c.tif", "All")
#sum the rasters
print "Caclulating SUM"
outCellStats = CellStatistics(rasterList, "SUM", "DATA")
#calculate the number of days (num of rasters/3)
print "Calculating day ratio"
num_of_rasters = len(rasterList)
print 'Num of rasters : ' + str(num_of_rasters)
num_of_days = num_of_rasters / 3
print 'Num of days : ' + str(num_of_days)
#in order to store decimal values, multiplicate the raster by 1000 before dividing
outCellStats = outCellStats * 1000 / num_of_days
#save the output raster
print "saving output " + stat_output_name
stat_output_name = os.path.join(output_folder,stat_output_name)
outCellStats.save(stat_output_name)
#delete the remaining temporary rasters
print "deleting CON rasters"
for i in rasterList:
print "deleting " + i
arcpy.Delete_management(i)
arcpy.Delete_management("lyr")
Could you rasterize your polygons into multiple layers, each pixel could contain your attribute value. Then merge the layers by averaging the attribute values?
Can anyone point me in the direction of a plugin or otherwise for formatting comments?
I'm using coffeescript, commenting is the same as python (#line, ### block ###), although javascript commenting also passes straight through the compiler.
OK, turns out that Edit -> Wrap keeps comments intact - complete newby.
There is also https://github.com/spadgos/sublime-jsdocs
I don't have a plug-in but I can offer a python script that I wrote. Enter your comment as a string at the 'user entered comment' (uec) variable, save and run. It will format a block quote surrounded by pound signs 80 characters across. If you need your block quote shorter or longer then just change the 80, the 78, and the 76.
def first2lines(): return str(('#' * 80) + '\n' + '#' + (' ' * 78) + '#') # first two lines
def leftSide(): return '# ' # left side of border
def rightSide(): return ' #' # right side of border
def last2lines(): return str('#' + (' ' * 78) + '#' + '\n' + ('#' * 80)) # last two lines
# user entered comment
uec = "This program will neatly format a programming comment block so that it's surrounded by pound signs (#). It does this by splitting the comment into a list and then concatenating strings each no longer than 76 characters long including the correct amount of right side space padding. "
if len(uec) > 0:
eosm = '<<<EOSM>>>' # end of string marker
comment = uec + ' ' + eosm
wordList = comment.split() # load the comment into a list
tmpString = '' # temporarily holds loaded elements
loadComment = '' # holds the elements that will be printed
counter = 0 # keeps track of the number of elements/words processed
space = 0 # holds right side space padding
last = wordList.index(wordList[-1]) # numerical position of last element
print first2lines()
for word in wordList:
tmpString += word + ' ' # load the string until length is greater than 76
# processes and prints all comment lines except the last one
if len(tmpString.rstrip()) > 76:
tmpList = tmpString.split()
tmpString = tmpList[-1] + ' ' # before popping last element load it for the beginning of the next cycle
tmpList.pop()
for tmp in tmpList:
loadComment += tmp + ' '
loadComment = loadComment.rstrip()
space = 76 - len(loadComment)
print leftSide() + loadComment + (space * ' ') + rightSide()
loadComment = ''
# processes and prints the last comment line
elif len(tmpString.rstrip()) <= 76 and counter == last:
tmpList = tmpString.split()
tmpList.pop()
for tmp in tmpList:
loadComment += tmp + ' '
loadComment = loadComment.rstrip()
space = 76 - len(loadComment)
print leftSide() + loadComment + (space * ' ') + rightSide()
counter += 1
print last2lines()
else:
print first2lines()
print leftSide() + "The length of your comment is zero, it must be at least one character long. " + rightSide()
print last2lines()
You can use netbeans, it has autoFormatting Alt+Mayus+F
Question: I have a program that solves a quadratic equation. The program gives real solutions only. How do I perform the quality testing of the program? Do you need to ask me for some extra input parameters?
Create test cases, and check the result of your program against the expected result (which is calculated externally) in the test case.
The test cases can cover several ordinary cases, together with special cases, such as when the coefficient is 0, or the discriminant is < 0, = 0, near 0. When you compare the result, make sure you handle the comparison properly (since the result is floating point numbers).
# "quadratic-rb.rb" Code by RRB, dated April 2014. email ab_z#yahoo.com
class Quadratic
def input
print "Enter the value of a: "
$a = gets.to_f
print "Enter the value of b: "
$b = gets.to_f
print "Enter the value of c: "
$c = gets.to_f
end
def announcement #Method to display Equation
puts "The formula is " + $a.to_s + "x^2 + " + $b.to_s + "x + " + $c.to_s + "=0"
end
def result #Method to solve the equation and display answer
if ($b**2-4*$a*$c)>0
x1=(((Math.sqrt($b**2-4*$a*$c))-($b))/(2*$a))
x2=(-(((Math.sqrt($b**2-4*$a*$c))-($b))/(2*$a)))
puts "The values of x1 and x2 are " +x1.to_s + " and " + x2.to_s
else
puts "x1 and x2 are imaginary numbers"
end
end
Quadratic_solver = Quadratic.new
Quadratic_solver.input
Quadratic_solver.announcement
Quadratic_solver.result
end