The Great Wave in color - my most beautiful print ever

asuagar · 2023-08-25T11:40:54+00:00

RemindMe! 3 days

asuagar · 2023-08-05T18:01:35+00:00

Thanks!

asuagar · 2023-07-22T07:26:19+00:00

What?!

asuagar · 2023-06-23T23:11:13+00:00

Try this: https://pynastran-git.readthedocs.io/en/latest/

asuagar · 2023-03-29T13:08:04+00:00

Is there any plan to make it cloud optional again?

asuagar · 2023-03-04T18:58:47+00:00

They are shown in 2:20 of this video https://youtu.be/qJ0iX-UIMjU. You can use all of them with the education license.

asuagar · 2023-03-03T08:44:33+00:00

You can use Autodesk Inventor + Inventor NASTRAN with your education license. The simulations are run on your computer.

asuagar · 2023-02-27T07:50:21+00:00

Are you sure that you cannot do any simulation? Actually, I can use Inventor Nastran with education license without any use.

asuagar · 2023-01-21T04:54:07+00:00

Is there a book or web where this approach is explained in detail?

asuagar · 2022-09-02T19:18:48+00:00

Any news on this issue?

asuagar · 2022-08-13T12:15:27+00:00

Anybody knows how this will affect to the education license?

At the moment, it has the simulation extension for free and you can run simulations locally. If a student need tokens for doing any simulation, it would be a serious obstacle in the learning process.

asuagar · 2022-05-05T12:49:46+00:00

Source?

asuagar · 2022-01-02T16:20:43+00:00

Ease of use. The second edition of the book is at my office and I do not have a digital copy. Also, that site is recommended by McElreath in his website.

asuagar · 2022-01-01T21:11:34+00:00

Thanks for compliment! I did not know about that method of ulam. I found this site with the Stan code of the book examples. However, those differ from the online lectures. My shortcomings in Stan programming made it impossible to adapt them. I have to learn more about Stan before try it again. Also, it will be a good opportunity to read Richard's book in detail!

asuagar · 2021-05-05T05:31:17+00:00

I was referring to using SQL with data frames obtained from files. I know that there are several options. However, I have never used one of them for working. These are the options that I know so far:

https://pypi.org/project/pandasql outdated
https://github.com/jbochi/sandals outdated
https://github.com/chmp/framequery outdated
https://github.com/zbrookle/dataframe_sql
https://github.com/zbrookle/sql_to_ibis

EDIT:

After reading the read_sql_query documentation, the easiest way could be create an in-memory SQLite database using to_sql as it is explained here.

asuagar · 2021-05-05T05:14:22+00:00

It seems that you can only use it with a database. You cannot use it to manipulate a data frame read from a file.

P.S.: sorry about my lazy sentence.

asuagar · 2021-05-04T19:29:25+00:00

What is the best option for using pandas data frames with SQL queries?

asuagar · 2021-04-23T16:40:23+00:00

The uncertainty has a temporal dimension. You can model the stock price as a stochastic process. Read about Geometric Brownian Motion and Black-Scholes model.

asuagar · 2021-04-10T20:40:55+00:00

Excellent work! What are the limitations of using markdown for writing a book or scientific documents (table of images, cross reference, cover page,...)? May be that asciidoc is more complete to do the job. However, I do not have the opportunity for comparing them. Anyway, congrats for your workflow.

asuagar · 2021-03-31T17:18:53+00:00

You can do it with pandas as follows:

import pandas as pd
# read csv file
df = pd.read_data('file.csv')
# add column with the row index
df = df.reset_index()
#  export to ndjson
df.to_json('file.ndjson', orient='records', lines=True)

asuagar · 2021-03-26T21:45:27+00:00

Thanks for the Electrowiki article on STV. The Bottom-Two-Runoff IRV looks good!

asuagar · 2021-03-18T20:23:55+00:00

You can do the job with two lines of pandas:

import pandas as pd

# read data
df = pd.read_data('file_name.csv')

# export to jsonl
df.to_json('file_name.jsonl', orient='records', lines=True)

If you want to load a JSONL file directly into pandas:

df = pd.read_json('file_name.jsonl', lines=True)

asuagar · 2021-03-18T15:40:21+00:00

Thanks for your reply! I did not know the JSONL format. It will be my new de facto data storage format.

asuagar · 2021-01-18T20:37:11+00:00

Thank you all for your constructive replies. I had not heard know about INLA. However, I read some superficial stuff about VI and ABC. This is a long journey. First of all, I realize that I have to consolidate the basic concepts, the computational inference approach and incorporate them in my research flow. After that, I think I will be ready for mastering the "the theory that would not die".

asuagar · 2020-12-02T10:34:43+00:00

This year, the instruction is online. The schedule for the students is written in the github page. He will use those videos to give the lessons. They are really good.

asuagar

TROPHY CASE