Update acknowledging comment 1 and 4:
Indentation corrected, does compile in Geany, obvious redundancies stripped.
The first question is: why am I getting the args error when I try to run my code? The error message refers to a LO file.... (linux) /opt/libreoffice7.2/programs/pythonscript.py # line 915.
An excerpt from that file says...
def invoke(self, args, out, outindex ): # 910
log.debug( "PythonScript.invoke " + str( args ) ) # 911
try: # 912
if (self.args): # 913
args += self.args # 915
ret = self.func( *args ) # 915
My code is...
# -*- coding: UTF-8 -*-
from __future__ import unicode_literals
# Gets the current document
doc = XSCRIPTCONTEXT.getDocument()
ctx = XSCRIPTCONTEXT.getComponentContext()
sm = CTX.ServiceManager
import time
import datetime
import serial
import openpyxl
def PySerLO (args=None):
from datetime import date
from openpyxl import load_workbook
ser = serial.Serial('/dev/ttyUSB0') # ""ls /dev/ttyUSB* -l"" if unsure.
# Load the workbook and select the sheet...
wb = load_workbook('/media/mpa/UserFiles/LibreOffice/mpaNEW-spreadsheet.xlsx')
sheet = wb['Sheet1']
headings = ("Date", "Time", "Raw Data")
sheet.append(headings)
try:
while True:
# Get current date, Get current time, Read the serial port
today = date.today()
now = datetime.datetime.now().time()
data = ser.readline().decode()
if data != "":
row = (today, now, float((data))) # For some reason the first data point is garbage so don't do anything with this
row = (today, ("%s"%now), float((data)))
sheet.append(row)
ser.flush()
#Save the workbook to preserve data
wb.save('/media/mpa/UserFiles/LibreOffice/mpaNEW-spreadsheet.xlsx')
finally:
# Make sure the workbook is saved at end
wb.save('/media/mpa/UserFiles/LibreOffice/mpaNEW-spreadsheet.xlsx')
print('Data Finished')
I get this error on executing a macro executing command button...
I appreciate your patience and suggestions on improvement thus far.
The ultimate aim of this is to track and instantly plot scientific data, introduce my students to the power of LibreOffice - They download LO and I just pass them the fully contained spreadsheet. They then connect the hardware and they have an excellent resource on LO forever. Almost none of them have ever heard of LibreOffice (or Linux either!)
First of all, I do not think you should start by embedding the macro in the document. I don't think it is required for the reasons you seem to believe. Instead, put your code in My Macros (the LibreOffice user directory) where it is easier to develop. Then call it from a button or by going to Tools > Macros > Run Macro - no need to use APSO as it seems like that is causing you even more confusion. The only reason for embedding is for convenience, to make it easier to keep the document and a bit of code together. For complex code that needs to be given to multiple people, it's generally better to create a full-fledged extension.
Second, it looks like the error may be from M1.py line 22 - is this your code? I cannot tell where line 22 might be or where exactly the error is coming from. Most of us reading your question are looking for the code where the error message comes from, but we cannot find it because you did not provide the relevant file name or line number of your code. It is your job to simplify your code enough so that you can see where the error is coming from, and then give that information in the question.
The error mentions create_instance which is the call for creating an UNO service.
Also I am not comfortable with the idea of storing XSCRIPTCONTEXT values in global variables unless you're sure you know what you're doing (and it's pretty clear you don't. :) )
Also I have strong doubts about achieving success by combining different libraries - it looks like you want to use both openpyxl and the UNO interface in the same code, and I would not expect that to turn out well. Just stick with plain UNO because there are plenty of people who can help with that. (Or if you choose openpyxl then I will ignore your question and someone knowledgeable about that may be able to help.)
Finally, it seems like import uno is missing. That alone could explain the error, and it's hard to imagine how you have all this code yet didn't even start with that.
Before trying all of this complex code, get a simple example working. It looks like there are a lot of python-uno fundamentals you still need to figure out first, and it doesn't look like you have put much effort into working through a tutorial yet.
I want to read each line of an excel file (.xlsx-file) in the column called 'ABC'. There are 4667 lines and each line there is a string.
I want to print each string. But it does not work.
import requests
import pandas as pd
get_all_ABC = pd.read_excel('C:\Users\XXX\XXX2\XXX3\table.xlsx', header = 0)
row_iterator = get_all_ABC.iterrows()
_, last = row_iterator.__next__()`
for i, row in row_iterator:
r= requests.get(row["ABC"])
r= requests.get(last["ABC"])
last = row
data = (r.text)
print ((r.text))
Why are you using the requests library? That is for making HTTP requests. Also, it's almost always bad practice to iterate over rows in pandas, and 99% of the time unnecessary.
Also, r.text will be undefined as it's outside of the for loop scope.
Could you explain exactly what you're trying to accomplish? I don't think I'm understanding correctly.
Julian L is right in his points. I mixed a lot up. I have to use the requests method for my overallproblem because I use the GET Method on a RESTApi Server and use the strings which are written in the round about 4000 lines in the column 'ABC' in the excel file. Before I tried the following python script (in that script I also do not use an iteration):
import requests
import pandas as pd
get_all_ABC = pd.read_excel('C:\Users\XXX\XXX2\XXX3\table.xlsx', skiprows=1).set_index('ABC')
r = requests.get('http://localhost:5000/api/sensors/data?ABC={get_all_ABC}')
print(r.json())
But this does not work either.
This thread leads nowhere. I delete this one and open a new one in which I describe the problem in more detail.
I struggled with the following for a couple of hours yesterday. I figured out a workaround, but I'd like to understand a little more of what's going on in the background and, ideally, I'd like to remove the intermediate file from my code just for the sake of elegance. I'm using python, by the way and files_df starts off as a pandas dataframe.
Can you help me understand why the following code gives me an error.
files_json = files_df.to_json(orient='records')
for file_json in files_json:
print(file_json) #do stuff
But this code works?
files_json = files_df.to_json(orient='records')
with open('export_json.json', 'w') as f:
f.write(files_json)
with open('export_json.json') as data:
files_json = json.load(data)
for file_json in files_json:
print(file_json) #do stuff
Obviously, the export/import is converting the data somehow into a usable format. I would like to understand that a little better and know if there is some option within the pandas files_df.to_json command to perform the same conversion.
json.load is the opposite of json.dump, but you export from pandas data frames into file and than import again with standard library into some sort of python structure.
Try files_df.to_dict
This question has been updated
I am writing a python script using the python-bugzilla 1.1.0 pypi. I am able to get all the bug IDs but I want to know if there is a way for me to access each bug's XML page? Here is the code I have so far:
bz = bugzilla.Bugzilla(url='https://bugzilla.mycompany.com/xmlrpc.cgi')
try:
bz.login('name#email.com', 'password');
print'Authorization cookie received.'
except bugzilla.BugzillaError:
print(str(sys.exc_info()[1]))
sys.exit(1)
#getting all the bug ID's and displaying them
bugs = bz.query(bz.build_query(assigned_to="your-bugzilla-account"))
for bug in bugs:
print bug.id
I don't know how to access each bug's XML page and not sure if it is even possible to do so. Can anyone help me with this? Thanks.
bz.getbugs()
Will get all bugs, bz.getbugssimple is also worth a look.
#!/usr/bin/env python
import bugzilla
bz = bugzilla.Bugzilla(url='https://bugzilla.company.com/xmlrpc.cgi')
bz.login('username#company.com', 'password')
results = bz.query(bz.url_to_query(queryUrl))
bids = []
for b in results:
bids.append(b.id)
print bids
I recently started using Python so I could interact with the Bloomberg API, and I'm having some trouble storing the data into a Pandas dataframe (or a panel). I can get the output in the command prompt just fine, so that's not an issue.
A very similar question was asked here:
Pandas wrapper for Bloomberg api?
The referenced code in the accepted answer for that question is for the old API, however, and it doesn't work for the new open API. Apparently the user who asked the question was able to easily modify that code to work with the new API, but I'm used to having my hand held in R, and this is my first endeavor with Python.
Could some benevolent user show me how to get this data into Pandas? There is an example in the Python API (available here: http://www.openbloomberg.com/open-api/) called SimpleHistoryExample.py that I've been working with that I've included below. I believe I'll need to modify mostly around the 'while(True)' loop toward the end of the 'main()' function, but everything I've tried so far has had issues.
Thanks in advance, and I hope this can be of help to anyone using Pandas for finance.
# SimpleHistoryExample.py
import blpapi
from optparse import OptionParser
def parseCmdLine():
parser = OptionParser(description="Retrieve reference data.")
parser.add_option("-a",
"--ip",
dest="host",
help="server name or IP (default: %default)",
metavar="ipAddress",
default="localhost")
parser.add_option("-p",
dest="port",
type="int",
help="server port (default: %default)",
metavar="tcpPort",
default=8194)
(options, args) = parser.parse_args()
return options
def main():
options = parseCmdLine()
# Fill SessionOptions
sessionOptions = blpapi.SessionOptions()
sessionOptions.setServerHost(options.host)
sessionOptions.setServerPort(options.port)
print "Connecting to %s:%s" % (options.host, options.port)
# Create a Session
session = blpapi.Session(sessionOptions)
# Start a Session
if not session.start():
print "Failed to start session."
return
try:
# Open service to get historical data from
if not session.openService("//blp/refdata"):
print "Failed to open //blp/refdata"
return
# Obtain previously opened service
refDataService = session.getService("//blp/refdata")
# Create and fill the request for the historical data
request = refDataService.createRequest("HistoricalDataRequest")
request.getElement("securities").appendValue("IBM US Equity")
request.getElement("securities").appendValue("MSFT US Equity")
request.getElement("fields").appendValue("PX_LAST")
request.getElement("fields").appendValue("OPEN")
request.set("periodicityAdjustment", "ACTUAL")
request.set("periodicitySelection", "DAILY")
request.set("startDate", "20061227")
request.set("endDate", "20061231")
request.set("maxDataPoints", 100)
print "Sending Request:", request
# Send the request
session.sendRequest(request)
# Process received events
while(True):
# We provide timeout to give the chance for Ctrl+C handling:
ev = session.nextEvent(500)
for msg in ev:
print msg
if ev.eventType() == blpapi.Event.RESPONSE:
# Response completly received, so we could exit
break
finally:
# Stop the session
session.stop()
if __name__ == "__main__":
print "SimpleHistoryExample"
try:
main()
except KeyboardInterrupt:
print "Ctrl+C pressed. Stopping..."
I use tia (https://github.com/bpsmith/tia/blob/master/examples/datamgr.ipynb)
It already downloads data as a panda dataframe from bloomberg.
You can download history for multiple tickers in one single call and even download some bloombergs reference data (Central Bank date meetings, holidays for a certain country, etc)
And you just install it with pip.
This link is full of examples but to download historical data is as easy as:
import pandas as pd
import tia.bbg.datamgr as dm
mgr = dm.BbgDataManager()
sids = mgr['MSFT US EQUITY', 'IBM US EQUITY', 'CSCO US EQUITY']
df = sids.get_historical('PX_LAST', '1/1/2014', '11/12/2014')
and df is a pandas dataframe.
Hope it helps
You can also use pdblp for this (Disclaimer: I'm the author). There is a tutorial showing similar functionality available here https://matthewgilbert.github.io/pdblp/tutorial.html, the functionality could be achieved using something like
import pdblp
con = pdblp.BCon()
con.start()
con.bdh(['IBM US Equity', 'MSFT US Equity'], ['PX_LAST', 'OPEN'],
'20061227', '20061231', elms=[("periodicityAdjustment", "ACTUAL")])
I've just published this which might help
http://github.com/alex314159/blpapiwrapper
It's basically not very intuitive to unpack the message, but this is what works for me, where strData is a list of bloomberg fields, for instance ['PX_LAST','PX_OPEN']:
fieldDataArray = msg.getElement('securityData').getElement('fieldData')
size = fieldDataArray.numValues()
fieldDataList = [fieldDataArray.getValueAsElement(i) for i in range(0,size)]
outDates = [x.getElementAsDatetime('date') for x in fieldDataList]
output = pandas.DataFrame(index=outDates,columns=strData)
for strD in strData:
outData = [x.getElementAsFloat(strD) for x in fieldDataList]
output[strD] = outData
output.replace('#N/A History',pandas.np.nan,inplace=True)
output.index = output.index.to_datetime()
return output
I've been using pybbg to do this sort of stuff. You can get it here:
https://github.com/bpsmith/pybbg
Import the package and you can then do (this is in the source code, bbg.py file):
banner('ReferenceDataRequest: single security, single field, frame response')
req = ReferenceDataRequest('msft us equity', 'px_last', response_type='frame')
print req.execute().response
The advantages:
Easy to use; minimal boilerplate, and parses indices and dates for you.
It's blocking. Since you mention R, I assume you are using this in some type of an interactive environment, like IPython. So this is what you want , rather than having to mess around with callbacks.
It can also do historical (i.e. price series), intraday and bulk data request (no tick data yet).
Disadvantages:
Only works in Windows, as far as I know (you must have BB workstationg installed and running).
Following on the above, it depends on the 32 bit OLE api for Python. It only works with the 32 bit version - so you will need 32 bit python and 32 bit OLE bindings
There are some bugs. In my experience, when retrieving data for a number of instruments, it tends to hang IPython. Not sure what causes this.
Based on the last point, I would suggest that if you are getting large amounts of data, you retrieve and store these in an excel sheet (one instrument per sheet), and then import these. read_excel isn't efficient for doing this; you need to use the ExcelReader (?) object, and then iterate over the sheets. Otherwise, using read_excel will reopen the file each time you read a sheet; this can take ages.
Tia https://github.com/bpsmith/tia is the best I've found, and I've tried them all... It allows you to do:
import pandas as pd
import datetime
import tia.bbg.datamgr as dm
mgr = dm.BbgDataManager()
sids = mgr['BAC US EQUITY', 'JPM US EQUITY']
df = sids.get_historical(['BEST_PX_BPS_RATIO','BEST_ROE'],
datetime.date(2013,1,1),
datetime.date(2013,2,1),
BEST_FPERIOD_OVERRIDE="1GY",
non_trading_day_fill_option="ALL_CALENDAR_DAYS",
non_trading_day_fill_method="PREVIOUS_VALUE")
print df
#and you'll probably want to carry on with something like this
df1=df.unstack(level=0).reset_index()
df1.columns = ('ticker','field','date','value')
df1.pivot_table(index=['date','ticker'],values='value',columns='field')
df1.pivot_table(index=['date','field'],values='value',columns='ticker')
The caching is nice too.
Both https://github.com/alex314159/blpapiwrapper and https://github.com/kyuni22/pybbg do the basic job (thanks guys!) but have trouble with multiple securities/fields as well as overrides which you will inevitably need.
The one thing this https://github.com/kyuni22/pybbg has that tia doesn't have is bds(security, field).
A proper Bloomberg API for python now exists which does not use COM. It has all of the hooks to allow you to replicate the functionality of the Excel addin, with the obvious advantage of a proper programming language endpoint. The request and response objects are fairly poorly documented, and are quite obtuse. Still, the examples in the API are good, and some playing around using the inspect module and printing of response messages should get you up to speed. Sadly, the standard terminal licence only works on Windows. For *nix you will need a server licence (even more expensive). I have used it quite extensively.
https://www.bloomberg.com/professional/support/api-library/