Folks,
I am using django recurrence field in my app and its not clear how to format the field when passed via REST API.
Any help is appreciated.
from recurrence.fields import RecurrenceField
class Course(models.Model):
title = models.CharField(max_length=200)
recurrences = RecurrenceField()
Looks like its based of RFC 2445
https://www.rfc-editor.org/rfc/rfc2445#section-4.8.5.4
Format Definition: This property is defined by the following
notation:
rrule = "RRULE" rrulparam ":" recur CRLF
rrulparam = *(";" xparam)
Example: All examples assume the Eastern United States time zone.
Daily for 10 occurrences:
DTSTART;TZID=US-Eastern:19970902T090000
RRULE:FREQ=DAILY;COUNT=10
==> (1997 9:00 AM EDT)September 2-11
Daily until December 24, 1997:
DTSTART;TZID=US-Eastern:19970902T090000
RRULE:FREQ=DAILY;UNTIL=19971224T000000Z
==> (1997 9:00 AM EDT)September 2-30;October 1-25
(1997 9:00 AM EST)October 26-31;November 1-30;December 1-23
Every other day - forever:
DTSTART;TZID=US-Eastern:19970902T090000
RRULE:FREQ=DAILY;INTERVAL=2
==> (1997 9:00 AM EDT)September2,4,6,8...24,26,28,30;
October 2,4,6...20,22,24
(1997 9:00 AM EST)October 26,28,30;November 1,3,5,7...25,27,29;
Dec 1,3,...
Related
Need help debugging Regex
I have a string column in pandas data frame that contains dates formatted as follows. And there is only one such date in each string.
semicolons are only used to deliminate dates here and not present in actual strings
04/20/2009; 04/20/09; 4/20/09; 4/3/09; 011/14/83;
Mar-20-2009; Mar 20, 2009; March 20, 2009; Mar. 20, 2009; Mar 20 2009;
20 Mar 2009; 20 March 2009; 20 Mar. 2009; 20 March, 2009
Mar 20th, 2009; Mar 21st, 2009; Mar 22nd, 2009
Feb 2009; Sep 2009; Oct 2010
6/2008; 12/2009
2009; 2010
My job is to extract these using regex. Here is the pattern I came up with.
my_pattern = r"((?:(\d{0,2}\d)|(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z]*?)?[, -./]{0,2}(?:(\d{1,2})[dhnst]{0,2}|(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z]*?)[, -./]{1,2}(\d{2,4}))|(\d{4})"
sample_series.str.extract(my_pattern, expand=False)
regex_problem_image
So far, I see it work for every date except for the format "Jan 27, 1983", it matches the month name and the date. But the year isn't matched. I am relatively new to regex and I think my pattern design is quite bad too. I need help figuring out what's wrong with my regex expression and how I could debug or improve it. Thanks.
Here is the sample data to make the problem reproducible.
sample_list = ['.Got back to U.S. Jan 27, 1983.\n',
'.On 21 Oct 1983 patient was discharged from Scroder Hospital after EIGHT DAY ADMISSION\n',
'4-13-89 Communication with referring physician?: Not Done\n',
'7intake for follow up treatment at Anson General Hospital on 10 Feb 1983 # 12 AM\n',
'. Pt diagnosed in Apr 1976 after he presented with 2 month history of headaches and gait instability. MRI demonstrated 4 cm L cereballar mass in the paravermian region. He was admitted to PRM and underwent resection complicated by post-op delirium. Post-op sequelas include left palatal myoclonus and ataxia on the left upper and lower extremities which has progressively improved. Pt has not had any evidence of tumor recurrence.\n',
'1-14-81 Communication with referring physician?: Done\n',
'. Went to Emerson, in Newfane Alaska. Started in 2002 at CNM. Generally likes job, does not have time to do what she needs to do. Feels she is working more than should be.\n',
'09/14/2000 CPT Code: 90792: With medical services\n',
'. Sep 2015- Transferred to Memorial Hospital from above. Discharged to MH Partial Hospital on Zoloft, Trazadone and Neurontin but unclear if she followed up.\n',
'Born and raised in Fowlerville, IN. Parents divorced when she was young, states that it was a "bad" divorce. Received her college degree from Allegheny College in 2003. Past verbal, emotional, physical, sexual abuse: No\n']
sample_series = pd.Series(sample_list)
From your data :
>>> import pandas as pd
>>> sample_list = ['.Got back to U.S. Jan 27, 1983.\n',
'.On 21 Oct 1983 patient was discharged from Scroder Hospital after EIGHT DAY ADMISSION\n',
'4-13-89 Communication with referring physician?: Not Done\n',
'7intake for follow up treatment at Anson General Hospital on 10 Feb 1983 # 12 AM\n',
'. Pt diagnosed in Apr 1976 after he presented with 2 month history of headaches and gait instability. MRI demonstrated 4 cm L cereballar mass in the paravermian region. He was admitted to PRM and underwent resection complicated by post-op delirium. Post-op sequelas include left palatal myoclonus and ataxia on the left upper and lower extremities which has progressively improved. Pt has not had any evidence of tumor recurrence.\n',
'1-14-81 Communication with referring physician?: Done\n',
'. Went to Emerson, in Newfane Alaska. Started in 2002 at CNM. Generally likes job, does not have time to do what she needs to do. Feels she is working more than should be.\n',
'09/14/2000 CPT Code: 90792: With medical services\n',
'. Sep 2015- Transferred to Memorial Hospital from above. Discharged to MH Partial Hospital on Zoloft, Trazadone and Neurontin but unclear if she followed up.\n',
'Born and raised in Fowlerville, IN. Parents divorced when she was young, states that it was a "bad" divorce. Received her college degree from Allegheny College in 2003. Past verbal, emotional, physical, sexual abuse: No\n']
>>> sample_series = pd.Series(sample_list)
>>> df = sample_series.to_frame()
>>> df
0
0 .Got back to U.S. Jan 27, 1983.\n
1 .On 21 Oct 1983 patient was discharged from Sc...
2 4-13-89 Communication with referring physician...
3 7intake for follow up treatment at Anson Gener...
4 . Pt diagnosed in Apr 1976 after he presented...
5 1-14-81 Communication with referring physician...
6 . Went to Emerson, in Newfane Alaska. Started ...
7 09/14/2000 CPT Code: 90792: With medical servi...
8 . Sep 2015- Transferred to Memorial Hospital f...
9 Born and raised in Fowlerville, IN. Parents d...
We can use a tool called datefinder to find the date in each row :
>>> import datefinder
>>> def find_date(df):
... return [match for match in datefinder.find_dates(df[0])]
>>> df["Vals"] = df.apply(find_date, axis=1)
>>> df
0 Vals
0 .Got back to U.S. Jan 27, 1983.\n [1983-01-27 00:00:00]
1 .On 21 Oct 1983 patient was discharged from Sc... [1983-10-21 00:00:00]
2 4-13-89 Communication with referring physician... [1989-04-13 00:00:00]
3 7intake for follow up treatment at Anson Gener... []
4 . Pt diagnosed in Apr 1976 after he presented... [1976-04-30 00:00:00, 2021-09-02 00:00:00, 202...
5 1-14-81 Communication with referring physician... [1981-01-14 00:00:00]
6 . Went to Emerson, in Newfane Alaska. Started ... [2002-09-30 00:00:00]
7 09/14/2000 CPT Code: 90792: With medical servi... [2000-09-14 00:00:00]
8 . Sep 2015- Transferred to Memorial Hospital f... [2015-09-30 00:00:00]
9 Born and raised in Fowlerville, IN. Parents d... [2003-09-30 00:00:00]
I have two simple tables. I need to able to determine who is 'new' as of a particular date (say January) and then count only those attributes. There's a 1:M relationship on name. I basically need to answer the following questions with the below data:
What is the total number of FamilyMembers based on log-in for the month? (Done using custom measure)
Out of the total of #1 - how many have logged in for the first time?
Out of the total of #2 - how many were children? How many were adults?
Log In Table
ID
Name
Date
login1
Sam
Jan
login2
Sam
Jan
login3
Dave
Jan
login4
Dave
Jan
login5
Jack
Jan
login6
Sam
Jan
login7
James
Feb
login8
James
Feb
login9
James
Feb
login10
Sam
Feb
login11
Sam
Feb
login12
Steve
Feb
Contact Table
Name
FamilyMembers
Child
Adult
Sam
3
1
2
James
2
1
1
Dave
4
2
2
Jack
1
0
1
Steve
6
1
5
Using this data, filtered on February we would see Steve never signed in prior to that date, so that makes him 'new'. James is also new.
My closest attempt is the custom 'Count of New Individuals' Measure
VAR currentUsers = VALUES('Log-Ins'[Name])
VAR currentDate = MIN('Log-Ins'[Date])
VAR pastUsers = CALCULATETABLE(VALUES('Log-Ins'[Name]),
ALL('Log-Ins'[Date].[Month],'Log-Ins'[Date].[MonthNo],'Log-Ins'[Date].[Year])
, 'Log-Ins'[Date]<currentDate)
VAR newUsers = EXCEPT(currentUsers,pastUsers)
RETURN COUNTROWS(newUsers)
As you can see this gives me the count of new individuals but I want to count their attributes to say :: Out of the 11 total family members, 8 were new. Out of those 8, 6 were adults and 2 were children.
I may be getting lost in the translation, but I don't understand how exactly you want to display the information.
#ContactsWhoLoggedIN :=
CALCULATE(COUNTROWS(Contacts),FILTER(Contacts,CALCULATE(COUNTROWS(LogIN)>0)))
#NewCWhoLoggedIN :=
CALCULATE(COUNTROWS(Contacts),
FILTER(Contacts,
//LoggedIn in the Current Date Context
CALCULATE(COUNTROWS(LogIN))>0
&&
//Never LoogedIN before the Current Date Context
CALCULATE(COUNTROWS(LogIN),FILTER(ALL(Dates),Dates[Date]<MIN(Dates[Date])))=0
)
)
#CWhoLoggedBackIN := [#ContactsWhoLoggedIN]-[#NewCWhoLoggedIN]
#FM_NewCWLI :=
CALCULATE(SUM(Contacts[FamilyMembers]),
FILTER(Contacts,
//LoggedIn in the Current Date Context
CALCULATE(COUNTROWS(LogIN))>0
&&
//Never LoogedIN before the Current Date Context
CALCULATE(COUNTROWS(LogIN),FILTER(ALL(Dates),Dates[Date]<MIN(Dates[Date])))=0
)
)
I remember this pattern from "Microsoft Excel 2013: Building Data Models with PowerPivot"
EDIT - I added all the last 50 texts, I saw that were sent from various people, unfortunately, it's not an automatic email...
list of all the text is HERE
I'm struggling to find a matched pattern that will identify the needed items (date, start time, time zone) from this text:
1 April 20 16:00-16:30 Israel Time
Tomorrow, Wed Feb 12, 08:00-9:00 AM IST(IL)
Tomorrow, Wed Jan 22, 09:30-10:00 PM PST
11-May-20 19:00-20:30 Israel Time
The start time is an easy one: (\d+:\d+)- but I'm not sure what to be done with the other words and digits.
Based on the data you provided, something like this would do it, with 3 captures as requested:
(\d+[-\s]\w+[-\s]\d+|\w+ \d+),?\s(\d+\:\d+)\-\d+\:\d+\s(?:AM\s|PM\s)?(.*)
Online reference
Say we have a model as below
class Cake(models.Model):
baked_on = models.DateTimeField(auto_now_add=True)
cake_name = models.CharField(max_length=20)
Now, there are multiple Cakes baked on the same day, and I need a query that will return me a monthly cake report which consists of each day of the month, and the names of the first and last cakes baked on that day.
For example, if the data is something like this:
baked_on cake_name
11 Jan 12:30 Vanilla
11 Jan 14:30 Strawberry
11 Jan 20:45 Avocado
12 Jan 09:05 Raspberry
12 Jan 16:30 Sprinkles
12 Jan 20:11 Chocolate
My query's output should look like
date first last
11 Jan Vanilla Avocado
12 Jan Raspberry Chocolate
How should I go about doing this in a single ORM call?
Django 2.0 introduced window functions that are made for that kind of queries. Simple answer for your question will be:
Cake.objects.annotate(
first_cake=Window(
expression=FirstValue('cake_name'),
partition_by=[TruncDate('baked_on')],
order_by=F('baked_on').asc(),
),
last_cake=Window(
expression=FirstValue('cake_name'),
partition_by=[TruncDate('baked_on')],
order_by=F('baked_on').desc(),
),
day=TruncDate('baked_on'),
).distinct().values_list('day', 'first_cake', 'last_cake')
Why FirstValue in last_cake? That's becaues window query by default will traverse through each row and won't look ahead, so for every row, last row will be equal to current row. Using last_row together with descending sorting will fix that. Either that or you can define frame for which window query should work:
Cake.objects.annotate(
first_cake=Window(
expression=FirstValue('cake_name'),
partition_by=[TruncDate('baked_on')],
order_by=F('baked_on').asc(),
),
last_cake=Window(
expression=LastValue('cake_name'),
partition_by=[TruncDate('baked_on')],
order_by=F('baked_on').asc(),
frame=ValueRange(),
),
day=TruncDate('baked_on'),
).distinct().values_list('day', 'first_cake', 'last_cake')
I have a model called DeliverySlot. Its attributes look like:
#<DeliverySlot:0x007f955a322cf0> {
:id => 2562,
:from => Sat, 31 Dec 2016 12:00:00 UTC +00:00
}
from is a datetime column. Delivery slots are an hour and 30 minutes apart from each other.
How can I get all delivery slots from Monday-Friday that are between the hour of 2pm (14:00) and 5pm (17:00)?
As of now I have:
Assuming, Time.now.utc.strftime('%A') is Monday.
DeliverySlot.where(from: (Time.now.utc..(Time.now.utc + 5.days).end_of_day))
I am using Postgres btw. Should I be using Postgres date functions? If so, which ones?
I know I am bit late and you probably got this down pat by now.
I created a method called slots and it takes 2 parameters they are dates.
...
# Model.slot(Date.today, Date.today+7.days)
def self.slot(start_date, end_date)
start_date.upto(end_date) do |date|
if ['Monday','Tuesday','Wednesday','Thursday','Friday'].include?(date.strftime('%A'))
(date.beginning_of_day.to_i .. date.end_of_day.to_i).step(30.minutes).each do |time|
if Time.at( time ).strftime('%R') > '13:00' && Time.at( time ).strftime('%R') < '17:00'
puts "Create Slot Date: #{date.strftime('%D')} Time: #{Time.at( time )}"
end
end
end
end
end
I hope that this helps