prediction to actual label and export result to csv

prediction to actual label and export result to csv - python

import pandas as pd
import os
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
predictions = model.predict_generator(Br_test_generator, steps=test_steps_per_epoch)
predicted_classes = np.argmax(predictions, axis=1)
predicted_classes
output= array([3, 1, 0, 3, 5, 0, 0, 0, 6, 0, 0, 3, 6, 0, 1, 0, 0, 2, 2, 2, 2, 2,
1, 1, 0, 2, 2, 6, 0, 0, 0, 1, 1, 0, 0, 2, 0, 1, 1, 1, 1, 1, 1, 1,
6, 0, 5, 1, 3, 1, 0, 2, 2, 1, 1, 1, 1, 2, 2, 2, 4, 1, 5, 1, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 2, 2, 5, 2, 5, 5, 5, 2, 2, 2, 2,
1, 3, 5, 5, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 1, 2, 1, 5,
2, 2, 2, 5, 3, 1, 3, 3, 1, 3, 3, 3, 1, 1, 0, 1, 5, 0, 2, 5, 5, 4,
4, 4, 4, 4, 6, 4, 4, 4, 5, 0, 4, 4, 4, 4, 4, 5, 6, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 2, 2, 5, 5, 6, 5,
5, 6, 1, 6, 4, 5, 4, 1, 4, 5, 0, 2, 5, 5, 5, 2, 2, 2, 6, 6, 5, 6,
6, 6, 6, 6, 4, 6, 2, 6, 6, 2, 0, 2, 5, 6, 6, 6, 4, 4, 0, 6])
true_classes = Bre_test_generator.classes
class_labels = list(Bre_test_generator.class_indices.keys())
class_labels
output=['1B', '2B', '3B', 'CA', 'FB', 'MB', 'NB']
I want my predicted_classes to match the corresponding class_labels and I also want to output the result in csv.
I want my csv to have two columns: the image ID and the predicted classs_labels

Related

How to define a constant function defined in intervals in python?

I want to define a simple function which assumes different constant value (y=[1,4,2,3]) for defined intervals.
I implement it in this way:
import numpy as np
def f(x):
if (x>=0 and x<=1900):
return 1
if (x>1900 and x<=3600):
return 4
if (x>3600 and x<=5400):
return 2
if (x>5400 and x<=7200):
return 3
x=np.linspace(0,7200,1000)
y=f(x)
However, when I run the script, an error appears:
"ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
Do you know how to fix this?

The reason is that your function is only applicable to a single element rather than vectorization. np.vectorize is a general solution, but its performance is poor. For the example here, you can use np.searchsorted to vectorize:
>>> np.array([1, 1, 4, 2, 3])[np.searchsorted([0, 1900, 3600, 5400, 7200], x)]
array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3])

x is not what you think it is.
Try print(x) and see what it is actually looking like. It is first up a list and inside the list contains
[ 0. 7.20720721 14.41441441 21.62162162 28.82882883 .... 7192.79279279 7200. ]
I am unsure what you are trying to achieve but x is an array and not a single value, therefor you either need to loop over it or point to the exact index you want to test.

Naturally sorting pandas data rasies error

I have a pandas data from with the following indices
print(df.index)
MultiIndex(levels=[[u'Day 3', u'Day 4', u'Day 5', u'Day 7', u'Day 9'], [u'D1', u'D10', u'D11', u'D12', u'D2', u'D3', u'D4', u'D5', u'D6', u'D7', u'D8', u'D9'], [1.0, 2.0, 3.0]],
labels=[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [0, 0, 0, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 1, 1, 1, 2, 2, 2, 3, 3, 3, 0, 0, 0, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 1, 1, 1, 2, 2, 2, 3, 3, 3, 0, 0, 0, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 1, 1, 1, 2, 2, 2, 3, 3, 3, 0, 0, 0, 4, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 1, 1, 1, 2, 2, 2, 3, 3, 3, 0, 0, 0, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 1, 1, 1, 2, 2, 2, 3, 3, 3], [1, 2, 0, 1, 2, 0, 0, 2, 1, 0, 1, 2, 2, 0, 1, 0, 2, 1, 1, 2, 0, 1, 0, 2, 2, 0, 1, 0, 1, 2, 2, 1, 0, 1, 2, 0, 0, 2, 1, 0, 2, 1, 2, 0, 1, 0, 2, 1, 1, 0, 2, 0, 1, 2, 0, 2, 1, 2, 0, 1, 0, 2, 1, 0, 2, 1, 2, 0, 1, 0, 2, 1, 2, 1, 0, 0, 2, 1, 1, 2, 0, 0, 2, 1, 0, 1, 2, 0, 1, 2, 2, 1, 0, 1, 0, 2, 1, 0, 2, 0, 1, 2, 2, 0, 1, 1, 0, 2, 1, 2, 0, 1, 1, 2, 0, 2, 1, 0, 1, 2, 0, 0, 1, 2, 0, 1, 2, 2, 1, 0, 1, 0, 2, 2, 0, 1, 0, 1, 2, 0, 2, 1, 2, 0, 1, 1, 2, 0, 0, 2, 1, 0, 2, 1, 0, 2, 1, 2, 1, 0, 0, 2, 1, 2, 0, 1, 2, 0, 1, 2, 1, 0, 1, 2, 0, 2, 1, 0, 1, 2, 0]],
names=[u'Interval', u'Device', u'Well'])
I am sorting with the following
df = df.reindex(index=natsorted(df.index))
With this particular df, however, it returns the follow error.
raise Exception("cannot handle a non-unique multi-index!")
Exception: cannot handle a non-unique multi-index!
Any help would be greatly appreciated.

I made a minimal example and could reproduce your error. It seems it happens, because of the same levels tuple Day 3, D1 and 1.0 in arrays. If you remove one of them it works fine.
import pandas as pd
import numpy as np
from natsort import natsorted
arrays = [[u'Day 3', u'Day 3', u'Day 4', u'Day 5', u'Day 7', u'Day 9', u'Day 3', u'Day 4', u'Day 5', u'Day 7', u'Day 9'],
[u'D1', u'D1', u'D10', u'D11', u'D12', u'D2', u'D3', u'D4', u'D5', u'D6', u'D7'],
[1.0, 1.0, 2.0, 3.0, 1.0, 2.0, 1.0, 2.0, 3.0, 1.0, 2.0]]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=[u'Interval', u'Device', u'Well'])
df = pd.Series(np.random.randn(len(arrays[0])), index=index)
print df.index
df = df.reindex(index=natsorted(df.index))
As you mentioned you use several excel files, this may be helpful: Merging multiple dataframes with non unique indexes

Find index of non-equal neighbors (adjacent elements) of the list

I would like to create a list that:
iterates through each value of List1
populates List2 with the index number where the value differs from the previous index number
print List1 yields:
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,11, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
Here is the code I have so far:
List2 = []
ArraySize = len(List1)
index=0
for c in range (1,ArraySize):
if ArraySize[c] != ArraySize[c-1]:
c=List2[index]
index=index+1
This provides the error:
if ArraySize[c] != ArraySize[c-1]:
TypeError: 'int' object has no attribute '__getitem__'
I don't want to alter List_1 at all, because I use it to produce a graph later in the script.
List2 for these values in List1 should end up looking like:
[93,307,526,692,1106,1856,2043]
Any help is greatly appreciated! Thank you!

As the comment on your question says, you are using ArraySize in the wrong place, and assigning c in a way that doesn't do anything useful, you aren't updating List2.
And there's no need to keep a counter for the index when you can use enumerate() (also: What does enumerate mean? ) to do that for you, e.g.
List1 = [0, 0, ...]
List2 = []
for index, value in enumerate(List1):
if index > 0 and List1[index-1] != value:
List2.append(index)
Output is
> List2
=> [93, 218, 219, 307, 526, 692, 1106, 1856, 2043]
Try online at http://repl.it

In order to achieve this, you may use zip() and enumerate() along with conditional list comprehension expression as:
>>> [n for n, (i, j) in enumerate(zip(my_list, my_list[1:]), 1) if i!=j]
[93, 218, 219, 307, 526, 692, 1106, 1856, 2043]
Issue with you code:
You are assigning ArraySize = len(List1) which will hold int value. Then later you are doing ArraySize[c] i.e accessing it like a list. But since it hold integer value, you are getting the error:
TypeError: 'int' object has no attribute '__getitem__'
# Function called by list when you ^
# try to access the items based on index

reshape numpy 3D array to 2D

I have a very big array with the shape = (32, 3, 1e6)
I need to reshape it to this shape = (3, 32e6)
On a snippet, how to go from this::
>>> m3_3_5
array([[[8, 4, 1, 0, 0],
[6, 8, 5, 5, 2],
[1, 1, 1, 1, 1]],
[[8, 7, 1, 0, 3],
[2, 8, 5, 5, 2],
[1, 1, 1, 1, 1]],
[[2, 4, 0, 2, 3],
[2, 5, 5, 3, 2],
[1, 1, 1, 1, 1]]])
to this::
>>> res3_15
array([[8, 4, 1, 0, 0, 8, 7, 1, 0, 3, 2, 4, 0, 2, 3],
[6, 8, 5, 5, 2, 2, 8, 5, 5, 2, 2, 5, 5, 3, 2],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
I did try various combinations with reshape with no success::
>>> dd.T.reshape(3, 15)
array([[8, 8, 2, 6, 2, 2, 1, 1, 1, 4, 7, 4, 8, 8, 5],
[1, 1, 1, 1, 1, 0, 5, 5, 5, 1, 1, 1, 0, 0, 2],
[5, 5, 3, 1, 1, 1, 0, 3, 3, 2, 2, 2, 1, 1, 1]])
>>> dd.reshape(15, 3).T.reshape(3, 15)
array([[8, 0, 8, 2, 1, 8, 0, 8, 2, 1, 2, 2, 5, 2, 1],
[4, 0, 5, 1, 1, 7, 3, 5, 1, 1, 4, 3, 5, 1, 1],
[1, 6, 5, 1, 1, 1, 2, 5, 1, 1, 0, 2, 3, 1, 1]])

a.transpose([1,0,2]).reshape(3,15) will do what you want. (I am basically following comments by #hpaulj).
In [14]: a = np.array([[[8, 4, 1, 0, 0],
[6, 8, 5, 5, 2],
[1, 1, 1, 1, 1]],
[[8, 7, 1, 0, 3],
[2, 8, 5, 5, 2],
[1, 1, 1, 1, 1]],
[[2, 4, 0, 2, 3],
[2, 5, 5, 3, 2],
[1, 1, 1, 1, 1]]])
In [15]: a.transpose([1,0,2]).reshape(3,15)
Out[15]:
array([[8, 4, 1, 0, 0, 8, 7, 1, 0, 3, 2, 4, 0, 2, 3],
[6, 8, 5, 5, 2, 2, 8, 5, 5, 2, 2, 5, 5, 3, 2],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])

You can get the desired behavior with np.hstack
# g is your (3,3,5) array from above
reshaped = np.hstack(g[i,:,:] for i in range(3)) #uses a generator exp
reshaped_simpler = np.hstack(g) # this produces equivalent output to the above statmement
print reshaped # (3,30)
Output
array([[8, 4, 1, 0, 0, 8, 7, 1, 0, 3, 2, 4, 0, 2, 3],
[6, 8, 5, 5, 2, 2, 8, 5, 5, 2, 2, 5, 5, 3, 2],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])

convert matrix to image

How would I go about going converting a list of lists of ints into a matrix plot in Python?
The example data set is:
[[3, 5, 3, 5, 2, 3, 2, 4, 3, 0, 5, 0, 3, 2],
[5, 2, 2, 0, 0, 3, 2, 1, 0, 5, 3, 5, 0, 0],
[2, 5, 3, 1, 1, 3, 3, 0, 0, 5, 4, 4, 3, 3],
[4, 1, 4, 2, 1, 4, 5, 1, 2, 2, 0, 1, 2, 3],
[5, 1, 1, 1, 5, 2, 5, 0, 4, 0, 2, 4, 4, 5],
[5, 1, 0, 4, 5, 5, 4, 1, 3, 3, 1, 1, 0, 1],
[3, 2, 2, 4, 3, 1, 5, 5, 0, 4, 3, 2, 4, 1],
[4, 0, 1, 3, 2, 1, 2, 1, 0, 1, 5, 4, 2, 0],
[2, 0, 4, 0, 4, 5, 1, 2, 1, 0, 3, 4, 3, 1],
[2, 3, 4, 5, 4, 5, 0, 3, 3, 0, 2, 4, 4, 5],
[5, 2, 4, 3, 3, 0, 5, 4, 0, 3, 4, 3, 2, 1],
[3, 0, 4, 4, 4, 1, 4, 1, 3, 5, 1, 2, 1, 1],
[3, 4, 2, 5, 2, 5, 1, 3, 5, 1, 4, 3, 4, 1],
[0, 1, 1, 2, 3, 1, 2, 0, 1, 2, 4, 4, 2, 1]]
To give you an idea of what I'm looking for, the function MatrixPlot in Mathematica gives me this image for this data set:
Thanks!

You may try
from pylab import *
A = rand(5,5)
figure(1)
imshow(A, interpolation='nearest')
grid(True)
source

Perhaps matshow() from matplotlib is what you need.

You can also use pyplot from matplotlib, follows the code:
from matplotlib import pyplot as plt
plt.imshow(
[[3, 5, 3, 5, 2, 3, 2, 4, 3, 0, 5, 0, 3, 2],
[5, 2, 2, 0, 0, 3, 2, 1, 0, 5, 3, 5, 0, 0],
[2, 5, 3, 1, 1, 3, 3, 0, 0, 5, 4, 4, 3, 3],
[4, 1, 4, 2, 1, 4, 5, 1, 2, 2, 0, 1, 2, 3],
[5, 1, 1, 1, 5, 2, 5, 0, 4, 0, 2, 4, 4, 5],
[5, 1, 0, 4, 5, 5, 4, 1, 3, 3, 1, 1, 0, 1],
[3, 2, 2, 4, 3, 1, 5, 5, 0, 4, 3, 2, 4, 1],
[4, 0, 1, 3, 2, 1, 2, 1, 0, 1, 5, 4, 2, 0],
[2, 0, 4, 0, 4, 5, 1, 2, 1, 0, 3, 4, 3, 1],
[2, 3, 4, 5, 4, 5, 0, 3, 3, 0, 2, 4, 4, 5],
[5, 2, 4, 3, 3, 0, 5, 4, 0, 3, 4, 3, 2, 1],
[3, 0, 4, 4, 4, 1, 4, 1, 3, 5, 1, 2, 1, 1],
[3, 4, 2, 5, 2, 5, 1, 3, 5, 1, 4, 3, 4, 1],
[0, 1, 1, 2, 3, 1, 2, 0, 1, 2, 4, 4, 2, 1]], interpolation='nearest')
plt.show()
The output would be:

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