semvec/pypi-packages huggingface dataset:
LabSmith-HVS448x64 2.0.3.dev0
Python x64 support for controlling the LabSmith HVS448

#hvs448_x64
3/5/2019 HVS448 Python support V 2.0 dev
The python extension hvs448_x64.pyd contains Python (Windows, 3.6, 64-bit) support for controlling LabSmith HVS448 Devices.
usage: from hvs448_x64 import *
It contains the following objects:
CHVS448: interface to the CHVS448. Use this to create an interface to your instrument type
     via the appropriate method:
hv = CHVS448.CreateHVS200V()
hv = CHVS448.CreateHVS400V()
hv = CHVS448.CreateHVS800V()
hv = CHVS448.CreateHVS1500V()
hv = CHVS448.CreateHVS3000V()
hv = CHVS448.CreateHVS3000D()
hv = CHVS448.CreateHVS6000D()
hv = CHVS448.CreateHVS8000D()
Then establish a connection to the instrument using
hv.InitConnection(<com port number>)
When finished, use hv.CloseConnection() to free the com port
CHVChannel: Interface to a high-voltage channel/sequencer of the HVS
CMaster: Interface to the master sequencer of the HVS

use help(<object>) for a list of supported methods and variables.
print(hvs448.help()) displays this list of objects
yieldcurve 0.0.1
Estimates yield curves using various methodologies

This package will allow for easy building of yield curves using various methodologies found in academic literature. 
Currently, this package only has the Nelson Siegal function programmed but other functions are being worked on at the moment.

To use:
Make sure your dataframe consists of yields with column names denoting the maturity of the security in months.
Initiate the function as such:
  curve = NelsonSiegal(est_l=True)
  curve.fit(data, params=[1,1,1,1], l=none)

The NelsonSiegal function contains one argument. est_l - this argument is basically asking if you would like to have the lambda estimated
from the data using a non-linear least squares optimizer. Setting this to true will do so. Setting this to false means you will have to 
provide your own value of lambda into the fit function.
The fit function contains three arguments. Data - this is just your dataframe, params - this is an array of parameters used as starting 
values in the optimisation function. Finally, l is just the value for lambda if you chose this option in the initiation function.
yieldcurve-vlademel 0.0.1
Estimates yield curves using various methodologies

This package will allow for easy building of yield curves using various methodologies found in academic literature. 
Currently, this package only has the Nelson Siegal function programmed but other functions are being worked on at the moment.

To use:
Make sure your dataframe consists of yields with column names denoting the maturity of the security in months.
Initiate the function as such:
  curve = NelsonSiegal(est_l=True)
  curve.fit(data, params=[1,1,1,1], l=none)

The NelsonSiegal function contains one argument. est_l - this argument is basically asking if you would like to have the lambda estimated
from the data using a non-linear least squares optimizer. Setting this to true will do so. Setting this to false means you will have to 
provide your own value of lambda into the fit function.
The fit function contains three arguments. Data - this is just your dataframe, params - this is an array of parameters used as starting 
values in the optimisation function. Finally, l is just the value for lambda if you chose this option in the initiation function.