This is my third thread in StackOverflow.
I think I already learnt a LOT by reading threads here and clearing my doubts.
I'm trying to transform an excel table, in my own python script. I've done so much, and now that I'm almost finishing the script, I'm getting an Err message that I can't really understand. Here is my code: (I tried to give as much information as possible!)
def _sensitivity_analysis(datasource):
#datasource is a list with data that may be used for HBV_model() function;
datasource_length = len(datasource) #returns tha size of the data time series
sense_param = parameter_vector #collects the parameter data from the global vector (parameter_vector);
sense_index = np.linspace(0, 11, 12) #Vector that reflects the indexes of parameters that must be analyzed (0 - 11)
sense_factor = np.linspace(0.5, 2, 31) #Vecor with the variance factors that multiply the original parameter value;
ns_sense = [] #list that will be filled with Nasch-Sutcliff values (those numbers will be data for sensitivity analysis)
for i in range(sense_factor.shape[0]): #start column loop
ns_sense.append([]) #create column in ns_sense matrix
for j in range(sense_index.shape[0]): #start row loop
aux = sense_factor[i]*sense_param[j] #Multiplies the param[j] value by the factor[i] value
print(i,j,aux) #debug purposes
sense_param[j] = aux #substitutes the original parameter value by the modified one
hbv = _HBV_model(datasource, sense_param) #run the model calculations (works awesomely!)
sqrdiff = _square_diff() #does square-difference calculations for Nasch-Sutcliff;
average = _qcalc_qmed() #does square-difference calculations for Nasch-Sutcliff [2];
nasch = _nasch_sutcliff(sqrdiff, average) #Returns the Nasch-Sutcliff calculation value
ns_sense[i].insert(j, nasch) #insert the value into ns_sense(i, j) for further uses;
sense_param = np.array([np.float64(catchment_area), np.float64(thresh_temp),
np.float64(degreeday_factor), np.float64(field_capacity),
np.float64(shape_coeficient), np.float64(model_paramC),
np.float64(surfaceflow_param), np.float64(thresh_surface_level),
np.float64(interflow_param), np.float64(baseflow_param),
np.float64(percolation_param), np.float64(soilmoist_param)]) #restores sense_param to original values
for i in range(len(datasource)): #HBV_model() transforms original data (index = 5) in a fully calculated data (index 17)
for j in range(12): #in order to return it to original state before a new loop
datasource[i].pop() #data is popped out;
print(ns_sense) #debug purposes
So, when I run _sensitivity_analysis(datasource) I receive this message:
File "<ipython-input-47-c9748eaba818>", line 4, in <module>
aux = sense_factor[i]*sense_param[j]
IndexError: index 3652 is out of bounds for axis 0 with size 31;
I'm totally aware that it is talking about a index that is not accessible as it does not exists.
Explaining my situation, datasource is a list with index [3652]. But I can't see how the console is trying to access index 3652, as I'm not asking it to do so. The only point I'm trying to access such value is in the final loop:
for i in range(len(datasource)):
I'm really lost. I'd really apreciate if you could help me guys! If you need more info, I can give you.
Guess: sense_factor = np.linspace(0.5, 2, 31) has 31 elements - you ask for element 3652 and it naturally blows. i takes this value in final loop. Rewrite final loop as:
for k in range(len(datasource))
for m in range(12):
datasource[k].pop()
However your code has many issues - you should have been not using indexes
at all - instead use for loops directly on the arrays
You reused your variable names, here:
for i in range(sense_factor.shape[0]):
...
for j in range(sense_index.shape[0]):
and then here:
for i in range(len(datasource)):
for j in range(12):
so in aux = sense_factor[i]*sense_param[j], you're using the wrong value of i, and it's basically a fluke that you're not using the wrong value of j too.
Don't reuse variable names in the same scope.
Related
So my question is how I should save a large amount of simulation data to a file using Python (or update new data rows to the existing file).
Lets say I have NN=1000 particles, and I want to save the position and velocity data of each particle (x y z, vx vy vz). The data is in format [x1,y1,z1,vx1,vy1,vz1, x2,y2,z2,vx2,vy2,vz2, ...] and so on.
Simulation is working well, but I believe the methods I use for saving and keeping these information saved is not really optimal for me.
Pseudo code similar to my code
T_max = 1000 # for example
dt = 0.1 # time step
T = 0 # current time
iterations = int(T_max/dt) # number of iterations we are doing
NN = 1000 # Number of particles
ZZ = np.zeros( (iterations, 2+NN*6 ) ) # Here I generate whole data matrix at the beginning.
# ^ might not be the best idea as the system needs to keep everything in memory for the whole time
# So I guess saving could be done in chunks?
ZZ[0][0], ZZ[0][1] = T , dt
# ZZ[0][2:] = initialize_system(NN=NN) # so lets initialize the system.
# However, for this post I do this differently due to simplicity. See below
ZZ[0][2:] = np.random.uniform(-100,100,NN*6)
i = 0
while i < iteration:
T += dt
Z[i+1][0], Z[i+1][1] = T, dt
#Z[i+1][2:] = rk4(EOM_function, posvel=Z[i][2:])
# ^ Using this I would calculate new positions based on previous ones.
Z[i+1][2:] = np.random.uniform(-100,100,NN*6) #This is just for example here.
i += 1
# Now the simulation data is basically done, so one would need to save
# This one feels slow, as it takes 181s to save and is size of 1046246KB
np.savetxt('test1.txt', ZZ)
#other method with a bit less accuracy as I don't need to have all decimals saved
np.savetxt('test2.txt', ZZ, fmt='%1.6f') # Takes 125s and size is 426698KB
# Both of the above are kinda slow so I also tried to save to npy format
np.save('test.npy', ZZ) # It took 8.9s and size 164118KB
so this np.save() method seems to be fast, but I read somewhere that I can not append data to it. So this would not work if I keep saving the data in parts while calculating new positions.
So back to my question. How should/could I save the data efficiently (fast and memory friendly). I keep having some memory issues when NN and T_max gets larger because with this method I keep this whole ZZ all the time in memory.
So I guess I should calculate ZZ in parts, i.e. iterations/10 parts but then I should append this data to an existing file, and tests I have made felt slow. Any suggestions?
EDIT: feel free to ask more specifying questions as I feel like I forgot to explain something.
That highly depends on what you intend to use the output for. If it's stored for further calculations, .npy or some other binary format is always the way to go as it is faster, takes less space, and doesn't lose precision between loads and saves, instead of serializing it into a human readable format. If you need it to be readable, you might as well just output row by row to a csv file or something.
If you want to do it with binary, h5py allows you to extend a dataset after saving and append more stuff to it.
import numpy as np
import h5py
T_max = 10**4 # for example
dt = 0.1 # time step
T = 0 # current time
iterations = int(T_max/dt) # number of iterations we are doing
NN = 1000 # Number of particles
chunk_size = 10**3
ZZ = np.zeros( (chunk_size, 2+NN*6 ) )
ZZ[0][0], ZZ[0][1] = T , dt
# ZZ[0][2:] = initialize_system(NN=NN) # so lets initialize the system.
# However, for this post I do this differently due to simplicity. See below
ZZ[0][2:] = np.random.uniform(-100,100,NN*6)
with h5py.File("test.h5", "a") as f:
dset = f.create_dataset('ZZ', (0,2+NN*6), maxshape=(None,2+NN*6), dtype='float64', chunks=(chunk_size,2+NN+6))
for chunk in range(0, iterations, chunk_size):
for i in range(0, chunk_size - 1):
T += dt
ZZ[i + 1][0], ZZ[i + 1][1] = T, dt
#Z[i+1][2:] = rk4(EOM_function, posvel=Z[i][2:])
# ^ Using this I would calculate new positions based on previous ones.
ZZ[i + 1][2:] = np.random.uniform(-100,100,NN*6) #This is just for example here.
# Expand the file here to allow for more data.
dset.resize(dset.shape[0] + chunk_size, axis=0)
dset[chunk: chunk + chunk_size ] = ZZ
# update and initialize next chunk. the next chunk's first row should be the last row of the previous chunk + iteration
T += dt
ZZ[0][0], ZZ[0][1] = T, dt
#Z[0][2:] = rk4(EOM_function, posvel=Z[-1][2:])
# ^ Using this I would calculate new positions based on previous ones.
ZZ[0][2:] = np.random.uniform(-100,100,NN*6) #This is just for example here.
print(dset.shape)
This takes 70 seconds on the save step on my computer, generating a 45GB file, for a dataset that is 100 times your original code.
The above code is more general in case you are streaming your data and don't know your final size. If you know it from the start, you can replace the initial create_dataset with
dset = f.create_dataset('ZZ', (iterations,2+NN*6), dtype='float64')
and remove the dset.resize(dset.shape[0] + chunk_size, axis=0)
You'll probably also want to read it back in chunks afterwards for other processing, in which case you can follow the docs here: https://docs.h5py.org/en/latest/high/dataset.html#reading-writing-data
Okay so I'm continuing my question / providing possible answer to it based on the answer of EricChen1248. EDIT: Answer provided by EricChen1248 works now and is way better than this my code part. See his code
I do not yet still understand completely how this f.create_dataset () truly works (i.e. when does it write data to file in the loop etc).
Using the code provided by Eric, it created and saved the data files fastly, but when I read the file as follows
hf = h5py.File('temp/test.h5', 'r')
ZZ = np.array(hf['ZZ'])
hf.close()
and plotted the first column (time T column, which should increase by timestep dt after each iteration) I get the following figure
plt.plot(ZZ[:,0])
time T column plotted
and as can be seen, it grows to a time of 100, and then goes to zero. This happens after the first 'chunk_size' has been passed. I started to read docs provided by Eric, and using his code as reference I managed to write something like this
import numpy as np
import h5py
T_max = 10**4
dt = 0.1
T = 0
NN = 1000
iterations = int(T_max/dt)
chunk_size = 10**3
with h5py.File('temp/data12.h5', 'a') as hf:
dset = hf.create_dataset("ZZ", (chunk_size, 2+NN*6),maxshape=(None,2+NN*6) ,chunks=(chunk_size, 2+NN*6), dtype='f8' )
# ^ first I create data set equals to one chunk_size
# Here I initialize the system. Columns ; 0=T , 1=dt, 2=arbitrary data point, 3=sin(column2)
# all the rest columns are random numbers just to fill some numbers in
dset[0,0], dset[0,1] = T, dt
#dset[0,2:] = np.random.uniform(0,1,NN*6)
dset[0,2] = 1
dset[0,3] = np.sin(dset[0,2])
dset[0,4:] = np.random.uniform(0,1,NN*6 -2)
print('starts')
# Main difference down there is that I use dataset (dset)
# as a data matrix to be filled instead of matrix ZZ as in my question.
i = 0
#for j, s in enumerate(dset.iter_chunks()):
for j, s in enumerate(range(0, iterations, chunk_size )):
print(j, s)
while i < iterations and i < chunk_size*(j+1) -1:
#for i in range(chunk_size*j, chunk_size*(j+1)-1):
T += dt
dset[i+1,0], dset[i+1,1] = T, dt
#dset[i+1,2:] = np.sin(dset[i,2:]+dt)
dset[i+1,2] = dset[i,2] + dt
dset[i+1,3] = np.sin(dset[i,2]+dt)
dset[i+1,4:] = dset[i,4:] + np.random.uniform(-1,1,NN*6-2)
i+=1
print(dset.shape)
dset.resize(dset.shape[0] + chunk_size, axis=0)
This code runs in 1min 50s , and saves a file of size 4.47GB so I am happy with the speed, and what I'm really happy is that it do not use so much memory while iterating (I used to get into problem with huge RAM usage).
When I read the data file provided by my code (similarly as above) I get following image for time Time T column plotted, my code version and it grows nicely to T=10e4 as should be. It still generated one more chunk_size block to the end of dataset which is full of zeros. That I need to get rid of. One more proof that the code works and saves data without weird problems is this sinusoidal plot plt.plot(ZZ[500:1500,0] , ZZ[500:1500,3]). Sinusoidal image proof Note that the plot is limited for T ~ [50,150] so one could still see something there (if plotted the whole thing, one could not see lines well).
I believe this is not the best way to write this code, but it is the way I got this working. So if someone sees improvements, please let me know. Also, I am curious to know why the code provided by Eric did not work, at least for me.
EDIT : fixed typos
I am facing an 'List Index out of range' error when trying to iterate a for-loop over a table I've created from a CSV extract, but cannot figure out why - even after trying many different methods.
Here is the step by step description of how the error happens :
I'm removing the first line of an imported CSV file, as this
line contains the columns' names but no data. The CSV has the following structure.
columnName1, columnName2, columnName3, columnName4
This, is, some, data
I, have, in, this
very, interesting, CSV, file
After storing the CSV in a first array called oldArray, I want to populate a newArray that will get all values from oldArray but not the first line, which is the column name line, as previously
mentioned. My newArray should then look like this.
This, is, some, data
I, have, in, this
very, interesting, CSV, file
To create this newArray, I'm using the following code with the append() function.
tempList = []
newArray = []
for i in range(len(oldArray)):
if i > 0: #my ugly way of skipping line 0...
for j in range(len(oldArray[0])):
tempList.append(oldArray[i][j])
newArray.append(tempList)
tempList = []
I also stored the columns in their own separate list.
i = 0
for i in range(len(oldArray[0])):
my_columnList[i] = oldArray[0][i]
And the error comes up next : I now want to populate a treeview table from this newArray, using a for-loop and insert (in a function). But I always get the 'Index List out of range error' and I cannot figure out why.
def populateTable(my_tree, newArray, my_columnList):
i = 0
for i in range(len(newArray)):
my_tree.insert('','end', text=newArray[i][0], values = (newArray[i][1:len(newArray[0]))
#(im using the text option to bypass treeview's column 0 problem)
return my_tree
Error message --> " File "(...my working directory...)", line 301, in populateTable
my_tree.insert(parent='', index='end', text=data[i][0], values=(data[i][1:len(data[0])]))
IndexError: list index out of range "
Using that same function with different datasets and columns worked fine, but not for this here newArray.
I'm fairy certain that the error comes strictly from this 'newArray' and is not linked to another parameter.
I've tested the validity of the columns list, of the CSV import in oldArray through some print() functions, and everything seems normal - values, row dimension, column dimension.
This is a great mystery to me...
Thank you all very much for your help and time.
You can find a problem from your error message: File "(...my working directory...)", line 301, in populateTable my_tree.insert(parent='', index='end', text=data[i][0], values=(data[i][1:len(data[0])])) IndexError: list index out of range
It means there is an index out of range in line 301: data[i][0] or data[i][1:len(data[0])]
(i is over len(data)) or (0 or 1 is over len(data[0]))
My guess is there is some empty list in data(maybe data[-1]?).
if data[i] is [] or [some_one_item], then data[i][1:len(data[0])] try to access to second item which not exists.
there is no problem in your "ugly" way to skip line 0 but I recommend having a look on this way
new_array = old_array.copy()
new_array.remove(new_array[0])
now for fixing your issue
looks like you have a problem in the indexing
when you use a for loop using the range of the length of an array you use normal indexing which starts from one while you identify your i variable to be zero
to make it simple
len(oldArray[0])
this is equal to 4 so when you use it in the for loop it's just like saying
for i in range(4):
to fix this you can either subtract 1 from the length of the old array or just identify the i variable to be 1 at the first
i = 1
for i in range(len(oldArray[0])):
my_columnList[i] = oldArray[0][i]
or
i = 0
for i in range(len(oldArray[0])-1):
my_columnList[i] = oldArray[0][i]
this mistake is also repeated in your populateTree function
so in the same way your code would be
def populateTree(my_tree, newArray, my_columnList):
i = 0
for i in range(len(newArray)-1):
my_tree.insert('','end', text=newArray[i][0], values = (newArray[i][1:len(newArray[0]))
#(im using the text option to bypass treeview's column 0 problem)
return my_tree
I'm trying to replicate this Stata loop in Pandas:
forvalues i = 1/6 {
gen int codeL`i' = L`i'.location_level_2
gen int codeF`i' = F`i'.location_level_2
}
As you can see, I want to create these new columns: codeL1 code L2...and so on, until I get codeL6, based on the lags and leads of the variable location_level_2
It is kind of easy in Stata, but as I'm just starting in Pandas, I have no clue.
This would be my attempt:
for i in range(1,7):
df[codeLi] = df[location_level_2].shift(i)
for i in range(-1,-7):
df[codeLi] = df[location_level_2].shift(i)
You are very close to a working solution! Here's one that follows the suggestion in the comment by #ALollz:
for i in range(1, 7):
df[f'codeL{i}'] = df['location_level_2'].shift(i)
df[f'codeF{i}'] = df['location_level_2'].shift(-i)
Note that the structure f'codeL{i} formats the integer from the range automatically. If you wanted to create the variable separately for some reason you might do new_var = 'codeL' + str(i).
Also note that there is no reason to do a second loop with a range of negative numbers, just pass the negative integer to pd.shift.
This will probably help you:
df=pd.DataFrame([[2,2,2],[1,2,3]])
for i in range(1,6):
df['codeL'+str(i)]=df.iloc[:,i]
I'm writing a code to analyze a (8477960, 1) column vector. I am not sure if the while loops in my code are running infinitely, or if the way I've written things is just really slow.
This is a section of my code up to the first while loop, which I cannot get to run to completion.
import numpy as np
import pandas as pd
data = pd.read_csv(r'C:\Users\willo\Desktop\TF_60nm_2_2.txt')
def recursive_low_pass(rawsignal, startcoeff, endcoeff, filtercoeff):
# The current signal length
ni = len(rawsignal) # signal size
rougheventlocations = np.zeros(shape=(100000, 3))
# The algorithm parameters
# filter coefficient
a = filtercoeff
raw = np.array(rawsignal).astype(np.float)
# thresholds
s = startcoeff
e = endcoeff # for event start and end thresholds
# The recursive algorithm
# loop init
ml = np.zeros(ni)
vl = np.zeros(ni)
s = np.zeros(ni)
ml[0] = np.mean(raw) # local mean init
vl[0] = np.var(raw) # local variance init
i = 0 # sample counter
numberofevents = 0 # number of detected events
# main loop
while i < (ni - 1):
i = i + 1
# local mean low pass filtering
ml[i] = a * ml[i - 1] + (1 - a) * raw[i]
# local variance low pass filtering
vl[i] = a * vl[i - 1] + (1 - a) * np.power([raw[i] - ml[i]],2)
# local threshold to detect event start
sl = ml[i] - s * np.sqrt(vl[i])
I'm not getting any error messages, but I've let the program run for more than 10 minutes without any results, so I assume I'm doing something incorrectly.
You should try to vectorize this process rather than accessing/processing indexes (otherwise why use numpy).
The other thing is that you seem to be doing unnecessary work (unless we're not seeing the whole function).
the line:
sl = ml[i] - s * np.sqrt(vl[i])
assigns the variable sl which you're not using inside the loop (or anywhere else). This assignment performs a whole vector multiplication by s which is all zeroes. If you do need the sl variable, you should calculate it outside of the loop using the last encountered values of ml[i] and vl[i] which you can store in temporary variables instead of computing on every loop.
If ni is in the millions, this unnecessary vector multiplication (of millions of zeros) is going to be very costly.
You probably didn't mean to override the value of s = startcoeff with s = np.zeros(ni) in the first place.
In order to vectorize these calculations you will need to use np.acumulate with some customized functions.
The non-numpy equivalent would be as follows (using itertools instead):
from itertools import accumulate
ml = [np.mean(raw)]+[0]*(ni-1)
mlSums = accumulate(zip(ml,raw),lambda r,d:(a*r[0] + (1-a)*d[1],0))
ml = [v for v,_ in mlSums]
vl = [np.var(raw)]+[0]*(ni-1)
vlSums = accumulate(zip(vl,raw,ml),lambda r,d:(a*r[0] + (1-a)*(d[1]-d[2])**2,0,0))
vl = [v for v,_,_ in vlSums]
In each case, the ml / vl vectors are initialized with the base value at index zero and the rest filled with zeroes.
The accumulate(zip(... function calls go through the array and call the lambda function with the current sum in r and the paired elements in d. For the ml calculation, this corresponds to r = (ml[i-1],_) and d = (0,raw[i]).
Because accumulate ouputs the same date type as it is given as input (which are zipped tuples), the actual result is only the first value of the tuples in the mlSums/vlSums lists.
This took 9.7 seconds to process for 8,477,960 items in the lists.
I just came across the need of an incremental Numpy array in Python, and since I haven't found anything I implemented it. I'm just wondering if my way is the best way or you can come up with other ideas.
So, the problem is that I have a 2D array (the program handles nD arrays) for which the size is not known in advance and variable amount of data need to be concatenated to the array in one direction (let's say that I've to call np.vstak a lot of times). Every time I concatenate data, I need to take the array, sort it along axis 0 and do other stuff, so I cannot construct a long list of arrays and then np.vstak the list at once.
Since memory allocation is expensive, I turned to incremental arrays, where I increment the size of the array of a quantity bigger than the size I need (I use 50% increments), so that I minimize the number of allocations.
I coded this up and you can see it in the following code:
class ExpandingArray:
__DEFAULT_ALLOC_INIT_DIM = 10 # default initial dimension for all the axis is nothing is given by the user
__DEFAULT_MAX_INCREMENT = 10 # default value in order to limit the increment of memory allocation
__MAX_INCREMENT = [] # Max increment
__ALLOC_DIMS = [] # Dimensions of the allocated np.array
__DIMS = [] # Dimensions of the view with data on the allocated np.array (__DIMS <= __ALLOC_DIMS)
__ARRAY = [] # Allocated array
def __init__(self,initData,allocInitDim=None,dtype=np.float64,maxIncrement=None):
self.__DIMS = np.array(initData.shape)
self.__MAX_INCREMENT = maxIncrement
if self.__MAX_INCREMENT == None:
self.__MAX_INCREMENT = self.__DEFAULT_MAX_INCREMENT
# Compute the allocation dimensions based on user's input
if allocInitDim == None:
allocInitDim = self.__DIMS.copy()
while np.any( allocInitDim < self.__DIMS ) or np.any(allocInitDim == 0):
for i in range(len(self.__DIMS)):
if allocInitDim[i] == 0:
allocInitDim[i] = self.__DEFAULT_ALLOC_INIT_DIM
if allocInitDim[i] < self.__DIMS[i]:
allocInitDim[i] += min(allocInitDim[i]/2, self.__MAX_INCREMENT)
# Allocate memory
self.__ALLOC_DIMS = allocInitDim
self.__ARRAY = np.zeros(self.__ALLOC_DIMS,dtype=dtype)
# Set initData
sliceIdxs = [slice(self.__DIMS[i]) for i in range(len(self.__DIMS))]
self.__ARRAY[sliceIdxs] = initData
def shape(self):
return tuple(self.__DIMS)
def getAllocArray(self):
return self.__ARRAY
def getDataArray(self):
"""
Get the view of the array with data
"""
sliceIdxs = [slice(self.__DIMS[i]) for i in range(len(self.__DIMS))]
return self.__ARRAY[sliceIdxs]
def concatenate(self,X,axis=0):
if axis > len(self.__DIMS):
print "Error: axis number exceed the number of dimensions"
return
# Check dimensions for remaining axis
for i in range(len(self.__DIMS)):
if i != axis:
if X.shape[i] != self.shape()[i]:
print "Error: Dimensions of the input array are not consistent in the axis %d" % i
return
# Check whether allocated memory is enough
needAlloc = False
while self.__ALLOC_DIMS[axis] < self.__DIMS[axis] + X.shape[axis]:
needAlloc = True
# Increase the __ALLOC_DIMS
self.__ALLOC_DIMS[axis] += min(self.__ALLOC_DIMS[axis]/2,self.__MAX_INCREMENT)
# Reallocate memory and copy old data
if needAlloc:
# Allocate
newArray = np.zeros(self.__ALLOC_DIMS)
# Copy
sliceIdxs = [slice(self.__DIMS[i]) for i in range(len(self.__DIMS))]
newArray[sliceIdxs] = self.__ARRAY[sliceIdxs]
self.__ARRAY = newArray
# Concatenate new data
sliceIdxs = []
for i in range(len(self.__DIMS)):
if i != axis:
sliceIdxs.append(slice(self.__DIMS[i]))
else:
sliceIdxs.append(slice(self.__DIMS[i],self.__DIMS[i]+X.shape[i]))
self.__ARRAY[sliceIdxs] = X
self.__DIMS[axis] += X.shape[axis]
The code shows considerably better performances than vstack/hstack several random sized concatenations.
What I'm wondering about is: is it the best way? Is there anything that do this already in numpy?
Further it would be nice to be able to overload the slice assignment operator of np.array, so that as soon as the user assign anything outside the actual dimensions, an ExpandingArray.concatenate() is performed. How to do such overloading?
Testing code: I post here also some code I used to make comparison between vstack and my method. I add up random chunk of data of maximum length 100.
import time
N = 10000
def performEA(N):
EA = ExpandingArray(np.zeros((0,2)),maxIncrement=1000)
for i in range(N):
nNew = np.random.random_integers(low=1,high=100,size=1)
X = np.random.rand(nNew,2)
EA.concatenate(X,axis=0)
# Perform operations on EA.getDataArray()
return EA
def performVStack(N):
A = np.zeros((0,2))
for i in range(N):
nNew = np.random.random_integers(low=1,high=100,size=1)
X = np.random.rand(nNew,2)
A = np.vstack((A,X))
# Perform operations on A
return A
start_EA = time.clock()
EA = performEA(N)
stop_EA = time.clock()
start_VS = time.clock()
VS = performVStack(N)
stop_VS = time.clock()
print "Elapsed Time EA: %.2f" % (stop_EA-start_EA)
print "Elapsed Time VS: %.2f" % (stop_VS-start_VS)
I think the most common design pattern for these things is to just use a list for the small arrays. Sure you could do things like dynamic resizing (if you want to do crazy things, you can try to use the resize array method too). I think a typical method is to always double the size, when you really don't know how large things will be. Of course if you know how large the array will grow to, just allocating the full thing up front is simplest.
def performVStack_fromlist(N):
l = []
for i in range(N):
nNew = np.random.random_integers(low=1,high=100,size=1)
X = np.random.rand(nNew,2)
l.append(X)
return np.vstack(l)
I am sure there are some use cases where an expanding array could be useful (for example when the appending arrays are all very small), but this loop seems better handled with the above pattern. The optimization is mostly about how often you need to copy everything around, and doing a list like this (other then the list itself) this is exactly once here. So it is much faster normally.
When I faced a similar problem, I used ndarray.resize() (http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.resize.html#numpy.ndarray.resize). Most of the time, it will avoid reallocation+copying altogether. I can't guarantee it would prove to be faster (it probably would), but it's so much simpler.
As for your second question, I think overriding slice assignment for extending purposes is not a good idea. That operator is meant for assigning to existing items/slices. If you want to change that, it's not immediately clear how you'd want it to behave in some cases, e.g.:
a = MyExtendableArray(np.arange(100))
a[200] = 6 # resize to 200? pad [100:200] with what?
a[90:110] = 7 # assign to existing items AND automagically-allocated items?
a[::-1][200] = 6 # ...
My suggestion is that slice-assignment and data appending should remain separate.