I am having some issues building a histogram.
Here is my code:
distribution = dict()
count = 0
name = input("Enter file:")
handle = open(name)
for line in handle:
line = line.rstrip()
if not line.startswith("From "):
continue
count = count + 1
firstSplit = line.split() # This gets me the line of text
time = firstSplit[5] # This gets me time - ex: 09:11:38
# print(firstSplit[5])
timeSplit = time.split(':')
hr = timeSplit[1] # This gets me hrs - ex: 09
# Gets me the histogram
if hr not in distribution:
distribution[hr[1]] = 1
else:
distribution[hr[1]] = distribution[hr[1]] + 1
print(distribution)
# print(firstSplit[5])
I read the text in, and I split it to get the lines, done by firstSplit. This line of text includes a time stamp. I do a second split to get the time, done by timeSplit.
From here, I try to build the histogram by trying to see if the hour is in the dictionary, if it is, add one, if not, add the hour. But this is where it goes wrong. My output looks like:
Example of Output
Any advise or suggestions would be great!
Seán
You are using an incorrect method to check if the hour is a key in the histogram. Here is the correct way to check:
if not (hr in list(distribution.keys()):
Also, you should be checking if a value is a key, then using the same value as the key that you create / add to. Therefore, the above will now be:
if not (hr[1] in list(distribution.keys()):
These two changes should fix your code and build you a great histogram!
Note: Code is untested
Related
This is my first time working with such a dataset.
I have a .csv file containing pixel values (48x48 = 2304 columns) of images, with their labels in the first column and the pixels in the subsequent ones, as below:
A glimpse of the dataset
I want to convert these pixels into their images, and store them into different directories corresponding to their respective labels. Now I have tried the solution posted here but it doesn't seem to work for me.
Here's what I've tried to do:
labels = ['Fear', 'Happy', 'Sad']
with open('dataset.csv') as csv_file:
csv_reader = csv.reader(csv_file)
fear = 0
happy = 0
sad = 0
# skip headers
next(csv_reader)
for row in csv_reader:
pixels = row[1:] # without label
pixels = np.array(pixels, dtype='uint8')
pixels = pixels.reshape((48, 48))
image = Image.fromarray(pixels)
if csv_file['emotion'][row] == 'Fear':
image.save('C:\\Users\\name\\data\\fear\\im'+str(fear)+'.jpg')
fear += 1
elif csv_file['emotion'][row] == 'Happy':
image.save('C:\\Users\\name\\data\\happy\\im'+str(happy)+'.jpg')
happy += 1
elif csv_file['emotion'][row] == 'Sad':
image.save('C:\\Users\\name\\data\\sad\\im'+str(sad)+'.jpg')
sad += 1
However, upon running the above block of code, the following is the error message I get:
Traceback (most recent call last):
File "<ipython-input-11-aa928099f061>", line 18, in <module>
if csv_file['emotion'][row] == 'Fear':
TypeError: '_io.TextIOWrapper' object is not subscriptable
I referred to a bunch of posts that solved the above error (like this one), but I found that the people were trying their hand at a relatively different problem than mine, and others I couldn't understand.
This may well be a very trivial question, but as I mentioned earlier, this is my first time working with such a dataset. Kindly tell me what am I doing wrong and how I can fix my code.
Try -
if str(row[0]) == 'Fear':
And in a similar way for the other conditions:
elif str(row[0]) == 'Happy':
elif str(row[0]) == 'Sad':
(a good practice is to just save the first value of the array as a variable)
The first problem that arose was that the first row was just the column names. In order to take care of this, I used the skiprows parameter like so:
raw = pd.read_csv('dataset.csv', skiprows = 1)
Secondly, I moved the labels column to the end due to it being in the first column. For my own convenience.
Thirdly, after all the preparations were done, the dataset won't iterate over the whole row, and instead just took in the value of the first row and first column, which gave an issue in resizing. So I instead used the df.itertuples() like so:
for row in data.itertuples(index = False, name = 'Pandas'):
Lastly, thanks to #HadarM 's suggestions, I was able to get it to work.
Modified code of the above code snippet that was the problematic block:
for row in data.itertuples(index = False, name = 'Pandas'):
pixels = row[:-1] # without label
pixels = np.array(pixels, dtype='uint8')
pixels = pixels.reshape((48, 48))
image = Image.fromarray(pixels)
if str(row[-1]) == 'Fear':
image.save('C:\\Users\\name\\data\\fear\\im'+str(fear)+'.jpg')
fear += 1
elif str(row[-1]) == 'Happy':
image.save('C:\\Users\\name\\data\\happy\\im'+str(happy)+'.jpg')
happy += 1
elif str(row[-1]) == 'Sad':
image.save('C:\\Users\\name\\data\\sad\\im'+str(sad)+'.jpg')
sad += 1
print('done')
I have 24 more attempts to submit this task. I spent hours and my brain does not work anymore. I am a beginner with Python can you please help to figure out what is wrong? I would love to see the correct code if possible.
Here is the task itself and the code I wrote below.
Note that you can have access to all standard modules/packages/libraries of your language. But there is no access to additional libraries (numpy in python, boost in c++, etc).
You are given a content of CSV-file with information about set of trades. It contains the following columns:
TIME - Timestamp of a trade in format Hour:Minute:Second.Millisecond
PRICE - Price of one share
SIZE - Count of shares executed in this trade
EXCHANGE - The exchange that executed this trade
For each exchange find the one minute-window during which the largest number of trades took place on this exchange.
Note that:
You need to send source code of your program.
You have only 25 attempts to submit a solutions for this task.
You have access to all standart modules/packages/libraries of your language. But there is no access to additional libraries (numpy in python, boost in c++, etc).
Input format
Input contains several lines. You can read it from standart input or file “trades.csv”
Each line contains information about one trade: TIME, PRICE, SIZE and EXCHANGE. Numbers are separated by comma.
Lines are listed in ascending order of timestamps. Several lines can contain the same timestamp.
Size of input file does not exceed 5 MB.
See the example below to understand the exact input format.
Output format
If input contains information about k exchanges, print k lines to standart output.
Each line should contain the only number — maximum number of trades during one minute-window.
You should print answers for exchanges in lexicographical order of their names.
Sample
Input Output
09:30:01.034,36.99,100,V
09:30:55.000,37.08,205,V
09:30:55.554,36.90,54,V
09:30:55.556,36.91,99,D
09:31:01.033,36.94,100,D
09:31:01.034,36.95,900,V
2
3
Notes
In the example four trades were executed on exchange “V” and two trades were executed on exchange “D”. Not all of the “V”-trades fit in one minute-window, so the answer for “V” is three.
X = []
with open('trades.csv', 'r') as tr:
for line in tr:
line = line.strip('\xef\xbb\xbf\r\n ')
X.append(line.split(','))
dex = {}
for item in X:
dex[item[3]] = []
for item in X:
dex[item[3]].append(float(item[0][:2])*60.+float(item[0][3:5])+float(item[0][6:8])/60.+float(item[0][9:])/60000.)
for item in dex:
count = 1
ccount = 1
if dex[item][len(dex[item])-1]-dex[item][0] <1:
count = len(dex[item])
else:
for t in range(len(dex[item])-1):
for tt in range(len(dex[item])-t-1):
if dex[item][tt+t+1]-dex[item][t] <1:
ccount += 1
else: break
if ccount>count:
count=ccount
ccount=1
print(count)
First of all it is not necessary to use datetime and csv modules for such a simple case (like in Ed-Ward's example).
If we remove colon and dot signs from the time strings it could be converted to int() directly - easier way than you tried in your example.
CSV features like dialect and special formatting not used so i suggest to use simple split(",")
Now about efficiency. Efficiency means time complexity.
The more times you go through your array with dates from the beginning to the end, the more complicated the algorithm becomes.
So our goal is to minimize cycles count, best to make only one pass by all rows and especially avoid nested loops and passing through collections from beginning to the end.
For such a task it is better to use deque, instead of tuple or list, because you can pop() first element and append last element with complexity of O(1).
Just append every time for needed exchange to the end of the exchange's queue until difference between current and first elements becomes more than 1 minute. Then just remove first element with popleft() and continue comparison. After whole file done - length of each queue will be the max 1min window.
Example with linear time complexity O(n):
from collections import deque
ex_list = {}
s = open("trades.csv").read().replace(":", "").replace(".", "")
for line in s.splitlines():
s = line.split(",")
curr_tm = int(s[0])
curr_ex = s[3]
if curr_ex not in ex_list:
ex_list[curr_ex] = deque()
ex_list[curr_ex].append(curr_tm)
if curr_tm >= ex_list[curr_ex][0] + 100000:
ex_list[curr_ex].popleft()
print("\n".join([str(len(ex_list[k])) for k in sorted(ex_list.keys())]))
This code should work:
import csv
import datetime
diff = datetime.timedelta(minutes=1)
def date_calc(start, dates):
for i, date in enumerate(dates):
if date >= start + diff:
return i
return i + 1
exchanges = {}
with open("trades.csv") as csvfile:
reader = csv.reader(csvfile)
for row in reader:
this_exchange = row[3]
if this_exchange not in exchanges:
exchanges[this_exchange] = []
time = datetime.datetime.strptime(row[0], "%H:%M:%S.%f")
exchanges[this_exchange].append(time)
ex_max = {}
for name, dates in exchanges.items():
ex_max[name] = 0
for i, d in enumerate(dates):
x = date_calc(d, dates[i:])
if x > ex_max[name]:
ex_max[name] = x
print('\n'.join([str(ex_max[k]) for k in sorted(ex_max.keys())]))
Output:
2
3
( obviously please check it for yourself before uploading it :) )
I think the issue with your current code is that you don't put the output in lexicographical order of their names...
If you want to use your current code, then here is a (hopefully) fixed version:
X = []
with open('trades.csv', 'r') as tr:
for line in tr:
line = line.strip('\xef\xbb\xbf\r\n ')
X.append(line.split(','))
dex = {}
counts = []
for item in X:
dex[item[3]] = []
for item in X:
dex[item[3]].append(float(item[0][:2])*60.+float(item[0][3:5])+float(item[0][6:8])/60.+float(item[0][9:])/60000.)
for item in dex:
count = 1
ccount = 1
if dex[item][len(dex[item])-1]-dex[item][0] <1:
count = len(dex[item])
else:
for t in range(len(dex[item])-1):
for tt in range(len(dex[item])-t-1):
if dex[item][tt+t+1]-dex[item][t] <1:
ccount += 1
else: break
if ccount>count:
count=ccount
ccount=1
counts.append((item, count))
counts.sort(key=lambda x: x[0])
print('\n'.join([str(x[1]) for x in counts]))
Output:
2
3
I do think you can make your life easier in the future by using Python's standard library, though :)
I wrote a program to calculate the ratio of minor (under 20 of age) population in each prefecture of Japan and it keeps producing this error: list index out of range, at line 19: ratio =(agerange[1]+agerange[2]+agerange[3]+agerange[4])/population*100.0
Link to csv: https://drive.google.com/open?id=1uPSMpgHw0csRx1UgAJzRLit9p6NrztFY
f=open("population.csv","r")
header=f.readline()
header=header.rstrip("\r\n")
while True:
line=f.readline()
if line=="":
break
line=line.rstrip("\r\n")
field=line.split(sep=",")
population=0
ratio=0
agerange=[ "pref" ]
for age in range(1, len(field)):
agerange.append(int(field[age]))
population+=int(field[age])
ratio =(agerange[1]+agerange[2]+agerange[3]+agerange[4])/population*100.0
print(field[0],ratio)
On line 17, I assume you to do the following code:
ratio =(agerange[0]+agerange[1]+agerange[2]+agerange[3])/population*100.0
next time, write your error more in detail please.
What you could do instead is get the sums of populations in the required age ranges and then perform the ratio calculation.
In Python, you can use the map function to convert the values in an iterable to ints, and make that into a list.
Once you have the list, you can use the sum function on it, or a part of it.
So, I came up with:
f = open("population.csv","r")
header = f.readline()
header = header.rstrip("\r\n")
while True:
line = f.readline()
if line == "":
break
line = line.rstrip("\r\n")
field = line.split(sep=",")
popData = list(map(int, field[1:]))
youngPop = sum(popData[:4])
oldPop = sum(popData[4:])
ratio = youngPop / (youngPop + oldPop)
print(field[0].ljust(12), ratio)
f.close()
Which outputs (just showing a portion here):
Hokkaido 0.1544532130777903
Aomori 0.1564945226917058
Iwate 0.16108452950558214
Miyagi 0.16831683168316833
Akita 0.14357429718875503
Yamagata 0.16515426497277677
Fukushima 0.16586921850079744
(I don't really know Python, so there could be some "better" or more conventional way.)
I am trying to have Python read through an excel file and find the maximum and minimum temperature for a set of days. The issue I'm having is that the first for loop functions correctly and all the ones after do not.
`weather_data = open("WeatherDataWindows.csv", 'r')
max_temp = 41
for next_line in weather_data:
next_line = next_line.split(',')
if next_line[1].isdigit():
if int(next_line[1]) > max_temp:
max_temp = int(next_line[1])
print("The max temperature is", max_temp, "degrees")
min_temp = 100
for find_min_temp in weather_data:
find_min_temp = find_min_temp.split(',')
if find_min_temp[3].isdigit():
if int(find_min_temp[3]) < min_temp:
min_temp = int(find_min_temp[3])
print("The min temperature is", min_temp, "degrees")`
When I run this code, the maximum temperature is displayed correctly, however, the minimum temperature simply displays '100'. If I delete the max_temp code from the program, the minimum temperature will display correctly. Why is this happening and what can do to fix it?
This is because the position of the read/write pointer within the file points to the end of the file after first for loop.
You can simply write
weather_data.seek(0)
Before second loop
I have data from eye-tracking (.edf file - from Eyelink by SR-research). I want to analyse it and get various measures such as fixation, saccade, duration, etc.
Is there an existing package to analyse Eye-Tracking data?
Thanks!
At least for importing the .edf-file into a pandas DF, you can use the following package by Niklas Wilming: https://github.com/nwilming/pyedfread/tree/master/pyedfread
This should already take care of saccades and fixations - have a look at the readme. Once they're in the data frame, you can apply whatever analysis you want to it.
pyeparse seems to be another (yet currently unmaintained as it seems) library that can be used for eyelink data analysis.
Here is a short excerpt from their example:
import numpy as np
import matplotlib.pyplot as plt
import pyeparse as pp
fname = '../pyeparse/tests/data/test_raw.edf'
raw = pp.read_raw(fname)
# visualize initial calibration
raw.plot_calibration(title='5-Point Calibration')
# create heatmap
raw.plot_heatmap(start=3., stop=60.)
EDIT: After I posted my answer I found a nice list compiling lots of potential tools for eyelink edf data analysis: https://github.com/davebraze/FDBeye/wiki/Researcher-Contributed-Eye-Tracking-Tools
Hey the question seems rather old but maybe I can reactivate it, because I am currently facing the same situation.
To start I recommend to convert your .edf to an .asc file. In this way it is easier to read it to get a first impression.
For this there exist many tools, but I used the SR-Research Eyelink Developers Kit (here).
I don't know your setup but the Eyelink 1000 itself detects saccades and fixation. I my case in the .asc file it looks like that:
SFIX L 10350642
10350642 864.3 542.7 2317.0
...
...
10350962 863.2 540.4 2354.0
EFIX L 10350642 10350962 322 863.1 541.2 2339
SSACC L 10350964
10350964 863.4 539.8 2359.0
...
...
10351004 683.4 511.2 2363.0
ESACC L 10350964 10351004 42 863.4 539.8 683.4 511.2 5.79 221
The first number corresponds to the timestamp, the second and third to x-y coordinates and the last is your pupil diameter (what the last numbers after ESACC are, I don't know).
SFIX -> start fixation
EFIX -> end fixation
SSACC -> start saccade
ESACC -> end saccade
You can also check out PyGaze, I haven't worked with it, but searching for a toolbox, this one always popped up.
EDIT
I found this toolbox here. It looks cool and works fine with the example data, but sadly does not work with mine
EDIT No 2
Revisiting this question after working on my own Eyetracking data I thought I might share a function wrote, to work with my data:
def eyedata2pandasframe(directory):
'''
This function takes a directory from which it tries to read in ASCII files containing eyetracking data
It returns eye_data: A pandas dataframe containing data from fixations AND saccades fix_data: A pandas dataframe containing only data from fixations
sac_data: pandas dataframe containing only data from saccades
fixation: numpy array containing information about fixation onsets and offsets
saccades: numpy array containing information about saccade onsets and offsets
blinks: numpy array containing information about blink onsets and offsets
trials: numpy array containing information about trial onsets
'''
eye_data= []
fix_data = []
sac_data = []
data_header = {0: 'TimeStamp',1: 'X_Coord',2: 'Y_Coord',3: 'Diameter'}
event_header = {0: 'Start', 1: 'End'}
start_reading = False
in_blink = False
in_saccade = False
fix_timestamps = []
sac_timestamps = []
blink_timestamps = []
trials = []
sample_rate_info = []
sample_rate = 0
# read the file and store, depending on the messages the data
# we have the following structure:
# a header -- every line starts with a '**'
# a bunch of messages containing information about callibration/validation and so on all starting with 'MSG'
# followed by:
# START 10350638 LEFT SAMPLES EVENTS
# PRESCALER 1
# VPRESCALER 1
# PUPIL AREA
# EVENTS GAZE LEFT RATE 500.00 TRACKING CR FILTER 2
# SAMPLES GAZE LEFT RATE 500.00 TRACKING CR FILTER 2
# followed by the actual data:
# normal data --> [TIMESTAMP]\t [X-Coords]\t [Y-Coords]\t [Diameter]
# Start of EVENTS [BLINKS FIXATION SACCADES] --> S[EVENTNAME] [EYE] [TIMESTAMP]
# End of EVENTS --> E[EVENT] [EYE] [TIMESTAMP_START]\t [TIMESTAMP_END]\t [TIME OF EVENT]\t [X-Coords start]\t [Y-Coords start]\t [X_Coords end]\t [Y-Coords end]\t [?]\t [?]
# Trial messages --> MSG timestamp\t TRIAL [TRIALNUMBER]
try:
with open(directory) as f:
csv_reader = csv.reader(f, delimiter ='\t')
for i, row in enumerate (csv_reader):
if any ('RATE' in item for item in row):
sample_rate_info = row
if any('SYNCTIME' in item for item in row): # only start reading after this message
start_reading = True
elif any('SFIX' in item for item in row): pass
#fix_timestamps[0].append (row)
elif any('EFIX' in item for item in row):
fix_timestamps.append ([row[0].split(' ')[4],row[1]])
#fix_timestamps[1].append (row)
elif any('SSACC' in item for item in row):
#sac_timestamps[0].append (row)
in_saccade = True
elif any('ESACC' in item for item in row):
sac_timestamps.append ([row[0].split(' ')[3],row[1]])
in_saccade = False
elif any('SBLINK' in item for item in row): # stop reading here because the blinks contain NaN
# blink_timestamps[0].append (row)
in_blink = True
elif any('EBLINK' in item for item in row): # start reading again. the blink ended
blink_timestamps.append ([row[0].split(' ')[2],row[1]])
in_blink = False
elif any('TRIAL' in item for item in row):
# the first element is 'MSG', we don't need it, then we split the second element to seperate the timestamp and only keep it as an integer
trials.append (int(row[1].split(' ')[0]))
elif start_reading and not in_blink:
eye_data.append(row)
if in_saccade:
sac_data.append(row)
else:
fix_data.append(row)
# drop the last data point, because it is the 'END' message
eye_data.pop(-1)
sac_data.pop(-1)
fix_data.pop(-1)
# convert every item in list into a float, substract the start of the first trial to set the start of the first video to t0=0
# then devide by 1000 to convert from milliseconds to seconds
for row in eye_data:
for i, item in enumerate (row):
row[i] = float (item)
for row in fix_data:
for i, item in enumerate (row):
row[i] = float (item)
for row in sac_data:
for i, item in enumerate (row):
row[i] = float (item)
for row in fix_timestamps:
for i, item in enumerate (row):
row [i] = (float(item)-trials[0])/1000
for row in sac_timestamps:
for i, item in enumerate (row):
row [i] = (float(item)-trials[0])/1000
for row in blink_timestamps:
for i, item in enumerate (row):
row [i] = (float(item)-trials[0])/1000
sample_rate = float (sample_rate_info[4])
# convert into pandas fix_data Frames for a better overview
eye_data = pd.DataFrame(eye_data)
fix_data = pd.DataFrame(fix_data)
sac_data = pd.DataFrame(sac_data)
fix_timestamps = pd.DataFrame(fix_timestamps)
sac_timestamps = pd.DataFrame(sac_timestamps)
trials = np.array(trials)
blink_timestamps = pd.DataFrame(blink_timestamps)
# rename header for an even better overview
eye_data = eye_data.rename(columns=data_header)
fix_data = fix_data.rename(columns=data_header)
sac_data = sac_data.rename(columns=data_header)
fix_timestamps = fix_timestamps.rename(columns=event_header)
sac_timestamps = sac_timestamps.rename(columns=event_header)
blink_timestamps = blink_timestamps.rename(columns=event_header)
# substract the first timestamp of trials to set the start of the first video to t0=0
eye_data.TimeStamp -= trials[0]
fix_data.TimeStamp -= trials[0]
sac_data.TimeStamp -= trials[0]
trials -= trials[0]
trials = trials /1000 # does not work with trials/=1000
# devide TimeStamp to get time in seconds
eye_data.TimeStamp /=1000
fix_data.TimeStamp /=1000
sac_data.TimeStamp /=1000
return eye_data, fix_data, sac_data, fix_timestamps, sac_timestamps, blink_timestamps, trials, sample_rate
except:
print ('Could not read ' + str(directory) + ' properly!!! Returned empty data')
return eye_data, fix_data, sac_data, fix_timestamps, sac_timestamps, blink_timestamps, trials, sample_rate
Hope it helps you guys. Some parts of the code you may need to change, like the index where to split the strings to get the crutial information about event on/offsets. Or you don't want to convert your timestamps into seconds or do not want to set the onset of your first trial to 0. That is up to you.
Additionally in my data we sent a message to know when we started measuring ('SYNCTIME') and I had only ONE condition in my experiment, so there is only one 'TRIAL' message
Cheers