I have the following:
d=[1 2 3 4 5 6 7]
I want Matlab to assign a day name to every number by doing a loop or
any suitable method as follows:
1 =tuesday
2=wednesday
.
.
.
7=monday
the results I am aiming to get after running the program is :
the Matlab window asks the user to enter a number from 1 to 7
n=('enter a number from 1 to 7')
then,
if we enter ,for example, 4 , this means that the printed result is: Friday
or
if we entered , for example , 7, this means that the printed result is: Monday
and so on
Is there any way to do this
regards
You could use a cell array, which allows you to store an array of text strings. The curly bracket is the key:
>> weekdays = {'Mon', 'Tues', 'Weds', 'Thurs', 'Fri', 'Sat', 'Sun'};
>> weekdays{4}
ans =
Thurs
Edit: You can get the relevant number from the user by using MATLAB's input function:
n = input('Enter your number:');
disp(weekdays{n})
Using a map might be one approach:
weekDays = containers.Map({1, 2, 3, 4, 5, 6, 7} , ...
{'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday'});
number = input('enter a number from 1 to 7');
disp(sprintf('You did choose %s\n', weekDays(number)));
EDIT:
Using the solution by Bill Cheatham you end up with
weekdays = {'Mon', 'Tues', 'Weds', 'Thurs', 'Fri', 'Sat', 'Sun'};
number = input('enter a number from 1 to 7');
disp(sprintf('You did choose %s\n', weekdays{number}));
Related
I would like to calculate a z-score over a bin based on the data of a rolling look-back period.
Example
Todays visitor amount during [9:30-9:35) should be z-score normalized based off the (mean, std) of the last 3 days of visitors that visited during [9:30-9:35).
My current attempts both raise InvalidOperationError. Is there a way in polars to calculate this?
import polars as pl
def z_score(col: str, over: str, alias: str):
# calculate z-score normalized `col` over `over`
return (
(pl.col(col)-pl.col(col).over(over).mean()) / pl.col(col).over(over).std()
).alias(alias)
df = pl.from_dict(
{
"timestamp": pd.date_range("2019-12-02 9:30", "2019-12-02 12:30", freq="30s").union(
pd.date_range("2019-12-03 9:30", "2019-12-03 12:30", freq="30s")
),
"visitors": [(e % 2) + 1 for e in range(722)]
}
# 5 minute bins for grouping [9:30-9:35) -> 930
).with_column(
pl.col("timestamp").dt.truncate(every="5m").dt.strftime("%H%M").cast(pl.Int32).alias("five_minute_bin")
).with_column(
pl.col("timestamp").dt.truncate(every="3d").alias("daytrunc")
)
# normalize visitor amount for each 5 min bin over the rolling 3 day window using z-score.
# not rolling but also wont work (InvalidOperationError: window expression not allowed in aggregation)
# df.with_column(
# z_score("visitors", "five_minute_bin", "normalized").over("daytrunc")
# )
# won't work either (InvalidOperationError: window expression not allowed in aggregation)
#df.groupby_rolling(index_column="daytrunc", period="3i").agg(z_score("visitors", "five_minute_bin", "normalized"))
For an example of 4 days of data with four data-points each lying in two time-bins ({0,0} - {0,1}), ({1,0} - {1,1})
Input:
Day 0: x_d0_{0,0}, x_d0_{0,1}, x_d0_{1,0}, x_d0_{1,1}
Day 1: x_d1_{0,0}, x_d1_{0,1}, x_d1_{1,0}, x_d1_{1,1}
Day 2: x_d2_{0,0}, x_d2_{0,1}, x_d2_{1,0}, x_d2_{1,1}
Day 3: x_d3_{0,0}, x_d3_{0,1}, x_d3_{1,0}, x_d3_{1,1}
Output:
Day 0: norm_x_d0_{0,0} = nan, norm_x_d0_{0,1} = nan, norm_x_d0_{1,0} = nan, norm_x_d0_{1,1} = nan
Day 1: norm_x_d1_{0,0} = nan, norm_x_d1_{0,1} = nan, norm_x_d1_{1,0} = nan, norm_x_d1_{1,1} = nan
Day 2: norm_x_d2_{0,0} = nan, norm_x_d2_{0,1} = nan, norm_x_d2_{1,0} = nan, norm_x_d2_{1,1} = nan
Day 3: norm_x_d3_{0,0} = (x_d3_{0,0} - np.mean([x_d0_{0,0}, x_d0_{0,1}, X_d1_{0,0}, ..., x_d3_{0,1}]) / np.std([x_d0_{0,0}, x_d0_{0,1}, X_d1_{0,0}, ..., x_d3_{0,1}])) , ... ,
They key here is to use over to restrict your calculations to the five minute bins and then use the rolling functions to get the rolling mean and standard deviation over days restricted by those five minute bin keys. five_minute_bin works as in your code and I believe that a truncated day_bin is necessary so that, for example, 9:33 on one day will include 9:31 both 9:34 on the same and 9:31 from 2 days ago.
days = 5
pl.DataFrame(
{
"timestamp": pl.concat(
[
pl.date_range(
datetime(2019, 12, d, 9, 30), datetime(2019, 12, d, 12, 30), "30s"
)
for d in range(2, days + 2)
]
),
"visitors": [(e % 2) + 1 for e in range(days * 361)],
}
).with_columns(
five_minute_bin=pl.col("timestamp").dt.truncate(every="5m").dt.strftime("%H%M"),
day_bin=pl.col("timestamp").dt.truncate(every="1d"),
).with_columns(
standardized_visitors=(
(
pl.col("visitors")
- pl.col("visitors").rolling_mean("3d", by="day_bin", closed="right")
)
/ pl.col("visitors").rolling_std("3d", by="day_bin", closed="right")
).over("five_minute_bin")
)
Now, that said, when trying out the code for this, I found polars doesn't handle non-unique values in the by-column in the rolling function correctly, so that the same values in the same 5-minute bin don't end up as the same standardized values. Opened bug report here: https://github.com/pola-rs/polars/issues/6691. For large amounts of real world data, this shouldn't actually matter that much, unless your data systematically differs in distribution within the 5 minute bins.
Im currently using micropython and it does not have the .zfill method.
What Im trying to get is to get the YYMMDDhhmmss of the UTC.
The time that it gives me for example is
t = (2019, 10, 11, 3, 40, 8, 686538, None)
I'm able to access the ones that I need by using t[:6]. Now the problem is with the single digit numbers, the 3 and 8. I was able to get it to show 1910113408, but I need to get 19101034008 I would need to get the zeroes before those 2. I used
t = "".join(map(str,t))
t = t[2:]
So my idea was to iterate over t and then check if the number is less than 10. If it is. I will add zeroes in front of it, replacing the number . And this is what I came up with.
t = (2019, 1, 1, 2, 40, 0)
t = list(t)
for i in t:
if t[i] < 10:
t[i] = 0+t[i]
t[i] = t[i]
print(t)
However, this gives me IndexError: list index out of range
Please help, I'm pretty new to coding/python.
When you use
for i in t:
i is not index, each item.
>>> for i in t:
... print(i)
...
2019
10
11
3
40
8
686538
None
If you want to use index, do like following:
>>> for i, v in enumerate(t):
... print("{} is {}".format(i,v))
...
0 is 2019
1 is 10
2 is 11
3 is 3
4 is 40
5 is 8
6 is 686538
7 is None
another way to create '191011034008'
>>> t = (2019, 10, 11, 3, 40, 8, 686538, None)
>>> "".join(map(lambda x: "%02d" % x, t[:6]))
'20191011034008'
>>> "".join(map(lambda x: "%02d" % x, t[:6]))[2:]
'191011034008'
note that:
%02d add leading zero when argument is lower than 10 otherwise (greater or equal 10) use itself. So year is still 4digit string.
This lambda does not expect that argument is None.
I tested this code at https://micropython.org/unicorn/
edited :
str.format method version:
"".join(map(lambda x: "{:02d}".format(x), t[:6]))[2:]
or
"".join(map(lambda x: "{0:02d}".format(x), t[:6]))[2:]
second example's 0 is parameter index.
You can use parameter index if you want to specify it (ex: position mismatch between format-string and params, want to write same parameter multiple times...and so on) .
>>> print("arg 0: {0}, arg 2: {2}, arg 1: {1}, arg 0 again: {0}".format(1, 11, 111))
arg 0: 1, arg 2: 111, arg 1: 11, arg 0 again: 1
I'd recommend you to use Python's string formatting syntax.
>> t = (2019, 10, 11, 3, 40, 8, 686538, None)
>> r = ("%d%02d%02d%02d%02d%02d" % t[:-2])[2:]
>> print(r)
191011034008
Let's see what's going on here:
%d means "display a number"
%2d means "display a number, at least 2 digits"
%02d means "display a number, at least 2 digits, pad with zeroes"
so we're feeding all the relevant numbers, padding them as needed, and cut the "20" out of "2019".
So, I have four excel files with dates, that I read out and convert.
num = xlsread('1.xlsx', 1, 'A:B')
num2 = xlsread('2.xlsx', 1, 'A:B');
num3 = xlsread('3.xlsx', 1, 'A:B');
num4 = xlsread('4.xlsx', 1, 'A:B');
dnum = datetime(num(:,1),1,1) + caldays(num(:,2));
dnum2= datetime(num2(:,1),1,1) + caldays(num2(:,2));
dnum3= datetime(num3(:,1),1,1) + caldays(num3(:,2));
dnum4=datetime(num4(:,1),1,1) + caldays(num4(:,2));
plot(dnum, 1*ones(size(dnum)), 'x-','linewidth', 1)
plot(dnum2, 2*ones(size(dnum2)), 'x-','linewidth', 1 )
plot(dnum3, 3*ones(size(dnum3)), 'x-', 'linewidth', 1)
plot(dnum4, 4*ones(size(dnum4)), 'x-', 'linewidth', 1)
This are the files that contain dates from many years, but if I want to just collect dates from 2016, what can I do?
Create a filter array with year.
FilterYears=year(dnum)==2016
FilteredData=data[FilterYears]
Hope this helps.
I have 2 sets of Date, their 1st and last dates are the same respectively but their dates within might not be the same to each other. Both DateA and DateB contain different values on their each date, which are arrays A and B.
DateA= '2016-01-01'
'2016-01-02'
'2016-01-04'
'2016-01-05'
'2016-01-06'
'2016-01-07'
'2016-01-08'
'2016-01-09'
'2016-01-10'
'2016-01-12'
'2016-01-13'
'2016-01-14'
'2016-01-16'
'2016-01-17'
'2016-01-18'
'2016-01-19'
'2016-01-20'
DateB= '2016-01-01'
'2016-01-02'
'2016-01-03'
'2016-01-04'
'2016-01-05'
'2016-01-09'
'2016-01-10'
'2016-01-11'
'2016-01-12'
'2016-01-13'
'2016-01-15'
'2016-01-16'
'2016-01-17'
'2016-01-19'
'2016-01-20'
A = [5, 2, 3, 4, 6, 1, 7, 9, 3, 6, 1, 7, 9, 2, 1, 4, 6]
B = [4, 2, 7, 1, 8, 4, 9, 5, 3, 9, 3, 6, 7, 2, 9]
I have converted the dates into datenumber,ie
datenumberA= 736330
736331
736333
736334
736335
736336
736337
736338
736339
736341
736342
736343
736345
736346
736347
datenumberB= 736330
736331
736332
736333
736334
736338
736339
736340
736341
736342
736344
736345
736346
736348
736349
Now I want to compare the value of A on DateA(n) to that of B on DateB while DateB is the date that is closest to and before the date of DateA(n).
For example,
comparing the value of A on DateA '2016-01-12' to that of B on DateB '2016-01-11'.
Please help and thanks a lot.
It'll get you the desired output!
all_k=0;
out(1)=1; % not comparing the first index as you mentioned
for n=2:size(datenumberA,1)
j=0;
while 1
k=find(datenumberB+j==datenumberA(n)-1); %finding the index of DateB closest to and before DateA(n)
if size(k,1)==1 break; end %if found, come out of the while loop
j=j+1; % otherwise keep adding 1 in the values of datenumberB until found
end
if size(find(all_k==k),2) ~=1 % to avoid if any DateB is already compared
out(end+1)=A(n)> B(k); %Comparing Value in A with corresponding value in B
all_k(end+1)=k; end %Storing which indices of DateB are already compared
end
out' %Output
Output:-
ans =
1
0
0
1
0
0
1
0
0
1
0
0
1
I have a report that is pulling average results per quarter. This will be a growing, rolling report with up to 5 years of data on the graph. I need to output Q1-2014, etc. I have created the quarters
if month ({ORDER_RESULTS.RESULT_DATE}) in [1,2,3] then "Q1"
Else if month ({ORDER_RESULTS.RESULT_DATE}) in [4, 5, 6] then "Q2"
Else if month ({ORDER_RESULTS.RESULT_DATE}) in [7, 8, 9] then "Q3"
Else if month ({ORDER_RESULTS.RESULT_DATE}) in [10, 11, 12] then "Q4"
And created a formula to append the year to each quarter:
{#Quarterly} & " - " & year({ORDER_RESULTS.RESULT_DATE})
The result looks like this: Q1-2,014.00.
How do I get it to look like Q1-2014?
Thanks a bunch!
Change the formula like this.
{#Quarterly} & " - " & ToText(year({ORDER_RESULTS.RESULT_DATE}),0,"")
"Q" + ToText(DatePart("q",{ORDER_RESULTS.RESULT_DATE})),"#") + "-" + ToText(DatePart("yyyy",{ORDER_RESULTS.RESULT_DATE})),"#")