from datetime import datetime timedelta
import pytz
ppt = pytz.timezone('US/Pacific')
first = ppt.localize(datetime(2013, 3, 10, 0, 0, 0))
first+=timedelta(hours=2)
first
returns datetime.datetime(2013, 3, 10, 2, 0, tzinfo=<DstTzInfo 'US/Pacific' PST-1 day, 16:00:00 STD>)
It should return datetime.datetime(2013, 3, 10, 3, 0, tzinfo=<DstTzInfo 'US/Pacific' PDT-1 day, 17:00:00 DST>)
You can workaround this, apparent, bug by doing astimezone(ppt) after adding the hours.
So, is this a bug? Am I doing it wrong? Or is it intended to have code refresh after adding time?
You need to call normalize() using the timezone object again when doing datetime arithmetic:
>>> first
datetime.datetime(2013, 3, 10, 2, 0, tzinfo=<DstTzInfo 'US/Pacific' PST-1 day, 16:00:00 STD>)
>>> ppt.normalize(first)
datetime.datetime(2013, 3, 10, 3, 0, tzinfo=<DstTzInfo 'US/Pacific' PDT-1 day, 17:00:00 DST>)
As noted in the docs:
In addition, if you perform date arithmetic on local times that cross DST boundaries, the result may be in an incorrect timezone. A normalize() method is provided to correct this.
Related
I have a list of NetCDF files that I would like to open with the xarray.open_mfdataset function.
This would normally be trivial, however I am running into an issue because the files I cam trying to open do not have any "time" dimension included in them:
data
Out[51]:
<xarray.Dataset>
Dimensions: (lat: 850, lon: 1500)
Coordinates:
* lat (lat) float64 54.98 54.94 54.9 54.86 ... 21.14 21.1 21.06 21.02
* lon (lon) float64 -126.0 -125.9 -125.9 -125.9 ... -66.1 -66.06 -66.02
Data variables:
Data (lat, lon) float32 ...
When I try to open my list of files with open_mfdataset, I of course get an error:
xr.open_mfdataset(files)
ValueError: Could not find any dimension coordinates to use to order the datasets for concatenation
I however do have a list of dates corresponding to each file:
dates
Out[54]:
array([datetime.datetime(2009, 1, 1, 0, 0),
datetime.datetime(2009, 1, 2, 0, 0),
datetime.datetime(2009, 1, 3, 0, 0), ...,
datetime.datetime(2019, 12, 29, 0, 0),
datetime.datetime(2019, 12, 30, 0, 0),
datetime.datetime(2019, 12, 31, 0, 0)], dtype=object)
I assume there is some way I add a time dimension to each file and open them all with open_mfdataset, possibly with the "preprocess" argument.
Thanks for any help.
Here is my solution:
Create a function which adds a time dimension to a DataArray, and fill it with a arbitrary date:
def add_time_dim(xda):
xda = xda.expand_dims(time = [datetime.now()])
return xda
Then, pass this function to the preprocess argument when running the open_mfdataset functions:
data = xr.open_mfdataset(files, preprocess = add_time_dim)
Finally, fill the time dimension with my dates:
data['time'] = dates
Is there a way to make bootstrap datetime picker to show me only options that represent intervals of half hour? (8:00; 8:30; 9:00; 9:30; ...)
Also I would like to know if there are ways to limit time intervals (say start from 8:00 AM until 18:00 PM).
You can set interval with the stepping parameter, while you can use enabledHours to limit valid hours of day. Assuming that "datetimepicker" is the id of your component, you can initialize it as follows:
$('#datetimepicker').datetimepicker({
stepping: 30,
enabledHours: [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]
});
You can find further information in the official documentation of the library
For me it works:-
$('.datetimepicker').datetimepicker({
autoclose:true,
minuteStep: 30,
hoursDisabled: [19, 20, 21, 22, 23, 00, 1, 2, 3, 4, 5, 6, 7]
});
I have a txt file contain some lines, like this:
[datetime.datetime(2013, 1, 4, 9, 35, 0, 4996), datetime.datetime(2013, 1, 4, 9, 40, 0, 4998),datetime.datetime(2013, 1, 4, 9, 45, 0, 5000)]
how to load data and translate to list like this
[2013-01-04 09:35:00.004996,
2013-01-04 09:40:00.004998,
2013-01-04 09:45:00.005000]
for line in dataFile.readlines():
print(type(line))
I get
<class 'str'>
how to do please
Thank you in advance
you will always have strings in a text file. but you can convert the strings to datetime objects:
import time
fmt = '%Y-%m-%d %H:%M:%S.%f'
dt = time.strptime('2013-01-04 09:35:00.004996', fmt)
print(dt)
or maybe i got you wrong and in your file you really have the string that looks like a list (please clarify); then you could try
from ast import literal_eval
import datetime
import re
strg = '[datetime.datetime(2013, 1, 4, 9, 35, 0, 4996), datetime.datetime(2013, 1, 4, 9, 40, 0, 4998),datetime.datetime(2013, 1, 4, 9, 45, 0, 5000)]'
dates = []
match = re.findall('datetime.datetime\([0-9 ,]+\)', strg)
for date_str in match:
args = literal_eval(date_str.replace('datetime.datetime', ''))
dates.append(datetime.datetime(*args))
print(dates)
Your text file is not dumped property for reading as json file.
its ok you can solve your problem as below
import datetime
output=[]
#I am assuming that you have already defined datafile
for line in dataFile.readlines():
output.append(eval(line))
print output
but before writing data to your text file you need to use json.dumps(object) then it is easy to get your object back by using json.load().
dateList = []
for line in dataFile.readlines():
match = re.findall('datetime.datetime\([0-9 ,]+\)', line)
for date_str in match:
# I can get this
print(eval(date_str))
# Translate
dates = date_str.replace('datetime.datetime', '')
dateList.append(dates)
# get this
print(dateList)
Thanks ALL
When I enter the following commands directly into the R console
library("xts")
mySeries <- xts(c(1.0, 2.0, 3.0, 5.0, 6.0), order.by=c(ISOdatetime(2001, 1, 1, 0, 0, 0), ISOdatetime(2001, 1, 2, 0, 0, 0), ISOdatetime(2001, 1, 3, 0, 0, 0), ISOdatetime(2001, 1, 4, 0, 0, 0), ISOdatetime(2001, 1, 5, 0, 0, 0)))
resultingSeries <- to.monthly(mySeries)
resultingSeries
I will get an output like this
mySeries.Open mySeries.High mySeries.Low mySeries.Close
Jan 2001 1 6 1 6
When I look into the attributes, I see the following output
attributes(resultingSeries)
$dim
[1] 1 4
$dimnames
$dimnames[[1]]
NULL
$dimnames[[2]]
[1] "mySeries.Open" "mySeries.High" "mySeries.Low" "mySeries.Close"
$index
[1] 978307200
attr(,"tclass")
[1] "yearmon"
$tclass
[1] "POSIXct" "POSIXt"
$tzone
[1] ""
$class
[1] "xts" "zoo"
$.indexCLASS
[1] "yearmon"
This is the same I get in Java. I'm wondering where the magic happens so that I see the nice output I get in R. I have no access to the event loop, since I'm using JRI like this (since, it's the recommended way and simplifies error handling):
REngine engine = REngine.engineForClass("org.rosuda.REngine.JRI.JRIEngine");
REXP result = engine.parseAndEval(...)
/edit
In Java I execute each command from above as follows:
REXP result = engine.parseAndEval("resultingSeries") // or any other command
What I get is
org.rosuda.REngine.REXPDouble#4ac66122+[12]
The payload being doubles: 1, 6, 1, 6
The attributes are the same as specified above.
Now R does some magic to display the output above. Is there a way I can get the same output without having to create it manually by myself? Where's the implementation stored, that R gets the above mentioned output?
Here is a piece of code that will work, here i extracted the first element of the field mySeries.Open from the object resultingSeries (which i converted to a data frame) which is equal to 1, notice that you can't pass all of the resultingSeries object strait into Java, you will need to break it down.
package stackoverflow;
import org.rosuda.JRI.REXP;
import org.rosuda.JRI.Rengine;
/**
*
* #author yschellekens
*/
public class StackOverflow {
public static void main(String[] args) throws Exception {
String[] Rargs = {"--vanilla"};
Rengine rengine = new Rengine( Rargs, false, null);
rengine.eval("library('xts')");
rengine.eval("mySeries <- xts(c(1.0, 2.0, 3.0, 5.0, 6.0), order.by=c(ISOdatetime(2001, 1, 1, 0, 0, 0), ISOdatetime(2001, 1, 2, 0, 0, 0), ISOdatetime(2001, 1, 3, 0, 0, 0), ISOdatetime(2001, 1, 4, 0, 0, 0), ISOdatetime(2001, 1, 5, 0, 0, 0)))");
rengine.eval("resultingSeries <- to.monthly(mySeries)");
rengine.eval("resultingSeries<-as.data.frame(resultingSeries)");
REXP result= rengine.eval("resultingSeries$mySeries.Open");
System.out.println("Greeting from R: "+result.asDouble());
}
}
And the Java output:
run:
Greeting from R: 1.0
I figured out the following workaround. The solution is far from perfect.
R offers a command to save its console output as characters vector.
capture.output( {command} )
We can access the output using
REXPString s = rengine.parseAndEval("capture.output( to.monthly(mySeries))")
String[] output = result.asStrings()
The variable output will contain all output lines
[0] mySeries.Open mySeries.High mySeries.Low mySeries.Close
[1]Jan 2001 1 6 1 6
Alternatively you coud use JRIEngine and attack yourself to the event loop, which it did not want in my case (due to the more complicated error handling).
LinkedIn's API returns the following value:
[creationTimestamp] => 1407247548000
It looks similar to a UNIX timestamp, but there are three "extra" zeros at the end. What format is this in, and how can I decode it?
It is a timestamp in milliseconds. Handling this is language dependent. Some languages may expect a timestamp in milliseconds, while others may expect it in seconds. Python 3, for example, expects seconds, but also handles microseconds (1000 milliseconds).
from datetime import datetime
ts = 1407247548124
dt = datetime.utcfromtimestamp(ts / 1000)
print(dt) # datetime(2014, 8, 5, 14, 5, 48, 124000)
Python 2 doesn't handle milliseconds directly (it ignores the fractional part), so you need to split the milliseconds out separately.
from datetime import datetime
ts = 14072475481234
secs, millis = divmod(ts, 1000)
dt = datetime.utcfromtimestamp(secs).replace(microsecond=millis * 1000)
print(dt) # datetime(2014, 8, 5, 14, 5, 48, 124000)