In one folder I have 13 txt files which have 3 columns of data t, x, y. I use for path in glob to set multiple arrays with the data values. My question is if I want to create a separate array from each of the main arrays (t, x, y)
for path in glob("F:\Thermal Motion\*.txt"):
t, x, y = np.loadtxt(path, unpack=True)
for i in range(len(x)):
D = ((x[i] - x[0])**2 + (y[i] - y[0])**2)**0.5
So when I print D, I don't get an array I get a list. I want to sort D into arrays according to the original 13 files. So the data from the first file going through D is one array, and so on...
D looks like a float to me, not an array or list. If I understand your need correctly, you need a list of "D-values" for each file that you're reading in. Let's say you want those list of values associated with the name of the file that produced them for simplicity; obviously you can reorder them however you want later. Use a list comprehension to store the values, and a dict to map file names to lists of D values. Something like this:
D_values = {}
for path in glob("F:\Thermal Motion\*.txt"):
t, x, y = np.loadtxt(path, unpack=True)
# Add a list of computed D values to the D_values dictionary.
D_list = []
for i in range(len(x)):
D = ((x[i] - x[0])**2 + (y[i] - y[0])**2)**0.5
D_list.append(D)
D_values[path] = D_list
The example above is extra verbose for clarity. If I were writing this code for real, I'd use a list comprehension rather than the explicit inner loop:
D_values = {}
for path in glob("F:\Thermal Motion\*.txt"):
t, x, y = np.loadtxt(path, unpack=True)
D_values[path] = [((x[i] - x[0])**2 + (y[i] - y[0])**2)**0.5
for i in range(len(x))]
Related
I have a list like below and need to firs add items in each list and then multiply all results 2+4 = 6 , 3+ (-2)=1, 2+3+2=7, -7+1=-6 then 6*1*7*(-6) = -252 I know how to do it by accessing indexes and it works (as below) but I also need to do it in a way that it will work no matter how many sublist there is
nested_lst = [[2,4], [3,-2],[2,3,2], [-7,1]]
a= nested_lst[0][0] + nested_lst[0][1]
b= nested_lst[1][0] + nested_lst[1][1]
c= nested_lst[2][0] + nested_lst[2][1] + nested_lst[2][2]
d= nested_lst[3][0] + nested_lst[3][1]
def sum_then_product(list):
multip= a*b*c*d
return multip
print sum_then_product(nested_lst)
I have tried with for loop which gives me addition but I don't know how to perform here multiplication. I am new to it. Please, help
nested_lst = [[2,4], [3,-2],[2,3,2], [-7,1]]
for i in nested_lst:
print sum(i)
Is this what you are looking for?
nested_lst = [[2,4], [3,-2],[2,3,2], [-7,1]] # your list
output = 1 # this will generate your eventual output
for sublist in nested_lst:
sublst_out = 0
for x in sublist:
sublst_out += x # your addition of the sublist elements
output *= sublst_out # multiply the sublist-addition with the other sublists
print(output)
It is difficult for me to describe the problem in the title, so excuse any misleading description.
The easiest way to describe what I need is with an example. I have a table like:
A B C
1 x
2 x x
3 x x
Now what I want is the formula in a cell for every single column and row with each of the column or row name for every x that is placed. In the example like:
A B C
1,2 2,3 3
1 A x
2 A, B x x
3 B, C x x
The column and row names are not equivalent to the excel designation. It works with an easy WHEN statement for single cells (=WHEN(C3="x";C1)), but not for a bunch of them (=WHEN(C3:E3="x";C1:E1)). How should/can such a formula look like?
So I found the answer to my problem. Excel provides the normal CONCATENATE function. What is needed is something like a CONCATENATEIF (in German = verkettenwenn) function. By adding a module in VBA based on a thread from ransi from 2011 on the ms-office-forum.net the function verkettenwenn can be used. The code for the German module looks like:
Option Explicit
Public Function verkettenwenn(Bereich_Kriterium, Kriterium, Bereich_Verketten)
Dim mydic As Object
Dim L As Long
Set mydic = CreateObject("Scripting.Dictionary")
For L = 1 To Bereich_Kriterium.Count
If Bereich_Kriterium(L) = Kriterium Then
mydic(L) = Bereich_Verketten(L)
End If
Next
verkettenwenn = Join(mydic.items, ", ")
End Function
With that module in place one of the formula for the mentioned example looks like: =verkettenwenn(C3:E3;"x";$C$1:$K$1)
The English code for a CONCATENATEIF function should probably be:
Option Explicit
Public Function CONCATENATEIF(Criteria_Area, Criterion, Concate_Area)
Dim mydic As Object
Dim L As Long
Set mydic = CreateObject("Scripting.Dictionary")
For L = 1 To Criteria_Area.Count
If Criteria_Area(L) = Criterion Then
mydic(L) = Concate_Area(L)
End If
Next
CONCATENATEIF = Join(mydic.items, ", ")
End Function
I'm using a nested list to hold data in a Cartesian coordinate type system.
The data is a list of categories which could be 0,1,2,3,4,5,255 (just 7 categories).
The data is held in a list formatted thus:
stack = [[0,1,0,0],
[2,1,0,0],
[1,1,1,3]]
Each list represents a row and each element of a row represents a data point.
I'm keen to hang on to this format because I am using it to generate images and thus far it has been extremely easy to use.
However, I have run into problems running the following code:
for j in range(len(stack)):
stack[j].append(255)
stack[j].insert(0, 255)
This is intended to iterate through each row adding a single element 255 to the start and end of each row. Unfortunately it adds 12 instances of 255 to both the start and end!
This makes no sense to me. Presumably I am missing something very trivial but I can't see what it might be. As far as I can tell it is related to the loop: if I write stack[0].append(255) outside of the loop it behaves normally.
The code is obviously part of a much larger script. The script runs multiple For loops, a couple of which are range(12) but which should have closed by the time this loop is called.
So - am I missing something trivial or is it more nefarious than that?
Edit: full code
step_size = 12, the code above is the part that inserts "right and left borders"
def classify(target_file, output_file):
import numpy
import cifar10_eval # want to hijack functions from the evaluation script
target_folder = "Binaries/" # finds target file in "Binaries"
destination_folder = "Binaries/Maps/" # destination for output file
# open the meta file to retrieve x,y dimensions
file = open(target_folder + target_file + "_meta" + ".txt", "r")
new_x = int(file.readline())
new_y = int(file.readline())
orig_x = int(file.readline())
orig_y = int(file.readline())
segment_dimension = int(file.readline())
step_size = int(file.readline())
file.close()
# run cifar10_eval and create predictions vector (formatted as a list)
predictions = cifar10_eval.map_interface(new_x * new_y)
del predictions[(new_x * new_y):] # get rid of excess predictions (that are an artefact of the fixed batch size)
print("# of predictions: " + str(len(predictions)))
# check that we are mapping the whole picture! (evaluation functions don't necessarily use the full data set)
if len(predictions) != new_x * new_y:
print("Error: number of predictions from cifar10_eval does not match metadata for this file")
return
# copy predictions to a nested list to make extraction of x/y data easy
# also eliminates need to keep metadata - x/y dimensions are stored via the shape of the output vector
stack = []
for j in range(new_y):
stack.append([])
for i in range(new_x):
stack[j].append(predictions[j*new_x + i])
predictions = None # clear the variable to free up memory
# iterate through map list and explode each category to cover more pixels
# assigns a step_size x step_size area to each classification input to achieve correspondance with original image
new_stack = []
for j in range(len(stack)):
row = stack[j]
new_row = []
for i in range(len(row)):
for a in range(step_size):
new_row.append(row[i])
for b in range(step_size):
new_stack.append(new_row)
stack = new_stack
new_stack = None
new_row = None # clear the variables to free up memory
# add a border to the image to indicate that some information has been lost
# border also ensures that map has 1-1 correspondance with original image which makes processing easier
# calculate border dimensions
top_and_left_thickness = int((segment_dimension - step_size) / 2)
right_thickness = int(top_and_left_thickness + (orig_x - (top_and_left_thickness * 2 + step_size * new_x)))
bottom_thickness = int(top_and_left_thickness + (orig_y - (top_and_left_thickness * 2 + step_size * new_y)))
print(top_and_left_thickness)
print(right_thickness)
print(bottom_thickness)
print(len(stack[0]))
# add the right then left borders
for j in range(len(stack)):
for b in range(right_thickness):
stack[j].append(255)
for b in range(top_and_left_thickness):
stack[j].insert(0, 255)
print(stack[0])
print(len(stack[0]))
# add the top and bottom borders
row = []
for i in range(len(stack[0])):
row.append(255) # create a blank row
for b in range(top_and_left_thickness):
stack.insert(0, row) # append the blank row to the top x many times
for b in range(bottom_thickness):
stack.append(row) # append the blank row to the bottom of the map
# we have our final output
# repackage this as a numpy array and save for later use
output = numpy.asarray(stack,numpy.uint8)
numpy.save(destination_folder + output_file + ".npy", output)
print("Category mapping complete, map saved as numpy pickle: " + output_file + ".npy")
I am working with a list of points in python 2.7 and running some interpolations on the data. My list has over 5000 points and I have some repeating "x" values within my list. These repeating "x" values have different corresponding "y" values. I want to get rid of these repeating points so that my interpolation function will work, because if there are repeating "x" values with different "y" values it runs an error because it does not satisfy the criteria of a function. Here is a simple example of what I am trying to do:
Input:
x = [1,1,3,4,5]
y = [10,20,30,40,50]
Output:
xy = [(1,10),(3,30),(4,40),(5,50)]
The interpolation function I am using is InterpolatedUnivariateSpline(x, y)
have a variable where you store the previous X value, if it is the same as the current value then skip the current value.
For example (pseudo code, you do the python),
int previousX = -1
foreach X
{
if(x == previousX)
{/*skip*/}
else
{
InterpolatedUnivariateSpline(x, y)
previousX = x /*store the x value that will be "previous" in next iteration
}
}
i am assuming you are already iterating so you dont need the actualy python code.
A bit late but if anyone is interested, here's a solution with numpy and pandas:
import pandas as pd
import numpy as np
x = [1,1,3,4,5]
y = [10,20,30,40,50]
#convert list into numpy arrays:
array_x, array_y = np.array(x), np.array(y)
# sort x and y by x value
order = np.argsort(array_x)
xsort, ysort = array_x[order], array_y[order]
#create a dataframe and add 2 columns for your x and y data:
df = pd.DataFrame()
df['xsort'] = xsort
df['ysort'] = ysort
#create new dataframe (mean) with no duplicate x values and corresponding mean values in all other cols:
mean = df.groupby('xsort').mean()
df_x = mean.index
df_y = mean['ysort']
# poly1d to create a polynomial line from coefficient inputs:
trend = np.polyfit(df_x, df_y, 14)
trendpoly = np.poly1d(trend)
# plot polyfit line:
plt.plot(df_x, trendpoly(df_x), linestyle=':', dashes=(6, 5), linewidth='0.8',
color=colour, zorder=9, figure=[name of figure])
Also, if you just use argsort() on the values in order of x, the interpolation should work even without the having to delete the duplicate x values. Trying on my own dataset:
polyfit on its own
sorting data in order of x first, then polyfit
sorting data, delete duplicates, then polyfit
... I get the same result twice
1.Three lists a, b and c. If a[index] is in b[index] then get the element in list c corresponding to list b[index]. That is if a[0]=b[1],get c[1]:
a = ['ASAP','WTHK']
b = ['ABCD','ASAP','EFGH','HIJK']
c = ['1','2','3','4','5']
I hope this is what you were looking for. You can add the b and the corresponding c value to the dictionary in a loop if the a array contains the b value. After that you can get the c value by a value as key like in the code below.
a = ['ASAP','WTHK']
# b c
dictionary_trans = {'ASAP' : '1'}
dictionary_trans = {'WTHK' : '1337'}
# etc. put all b values existing in a to the dict
# with thier coresponding c values.
key = a[0]
c_value = dictionary_trans.get(key)
print c_value
My python skills are very limited, but I think I would try to solve the problem this way.
This solution could crash if you use an a value which is not contained in the dictionary, so you need to implement some logic to handle missing relations between a and c, like insert dummy entries to the dictionary or so.