WTFM

How to setup DNS using GH pages and google domains

 Programming  76  1 min

I had struggled setting up the DNS configuracion of my custom domain in GH pages. Luckly, I found a video which make the process very straght forward.

Ingredients

  • Github repo
  • Github pages
  • Google domains

Here’s the step by step video:

Summary

  1. Create a GH page
  2. Create a Domain
  3. Add a CNAME file (no sure if this is mandatory)
  4. Add DNS rules (A, CNAME and AAAA)
  5. Test it with dig $ dig asdf0x2199.dev +noall +answer -t A

display issues on i3

 Programming  71  1 min

Context

I’ve had a weird issues in my laptop (carbon x1) which suddenly started to hanging processes and the laptop’s monitor got laggy (e.g not showing in real time what I typed). I’m using i3 in a Pop!_OS.

Solution

I’ve ended up in system76 article where they mention issues with external monitor and I’ve found the following command which solved my problems:

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enh(economics): La ley de hotelling

 585  3 mins

La ley de Hotelling

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from IPython.display import IFrame

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IFrame("https://en.m.wikipedia.org/wiki/Hotelling%27s_law", width=600, height=300)

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IFrame("https://en.m.wikipedia.org/wiki/Location_model", width=600, height=300)

Hotelling con 4 jugadores

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%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

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def calc_profits(df, space):
    """Calcula el beneficio"""

    try:
        lastcol = df.columns[-1]  # last game
        df = df[lastcol].copy()  # change dataframe to serie
    except:
        df = df.copy()

    # IF EACH FACTORY ARE ALONE
    if len(df.rank().unique()) == 4:  # Each player on different space
        df = df.sort_values()  # Values ordered, so index get useful
        profits = df.copy()    # make profits variable as a copy, modify after

        profits.iloc[0] = (df.iloc[0] + df.iloc[1]) / 2
        profits.iloc[1] = (df.iloc[2] - df.iloc[0]) / 2
        profits.iloc[2] = (df.iloc[3] - df.iloc[1]) / 2
        profits.iloc[3] = (2 * space - df.iloc[3] - df.iloc[2]) / 2
        return profits

    # IF TWO FACTORY ARE TOGETHER
    elif len(df.rank().unique()) == 3:  # Only two players on the same space
        df = df.sort_values()
        profits = df.copy()
        uniques = df.unique()  # get unique values
        ns = [2 * len(df[df == v]) for v in uniques]  # number of factories at the same place
        n1, n2, n3 = [df[df == v].index for v in uniques]  # name of one factory for each group

        profits.loc[n1] = (df.loc[n1].values[0] + df.loc[n2].values[0]) / ns[0]
        profits.loc[n2] = (df.loc[n3].values[0] - df.loc[n1].values[0]) / ns[1]
        profits.loc[n3] = (2 * space - df.loc[n3].values[0] - df.loc[n2].values[0]) / ns[2]
        return profits

    # IF TWO FACTORY ARE IN TWO GROUPS
    elif len(df.rank().unique()) == 2:
        df = df.sort_values()
        profits = df.copy()
        uniques = df.unique()  # get unique values
        n1, n2 = [df[df == v].index for v in uniques]  # name of one factory for each group

        profits.loc[n1] = (df.loc[n1].values[0] + df.loc[n2].values[0]) / 4
        profits.loc[n2] = (2 * space - df.loc[n2].values[0] - df.loc[n1].values[0]) / 4
        return profits


def new_game(df, space):

    import pandas as pd
    import numpy as np

    lastcol = df.columns[-1]
    space_ini = df[lastcol].copy()
    res = space_ini.copy()
    names = space_ini.index

    prof_ini = calc_profits(space_ini.copy(), space)
    step = 1

    f = open('log.txt', 'a')
    br = '\n'
    splt = '=' * 30
    str_game = "# GAME Nº %s" % (lastcol)

    for name in names:

        space_sum = space_ini.copy()
        space_sum[name] = space_sum[name] + step
        prof_sum = calc_profits(space_sum, space)

        space_subs = space_ini.copy()
        space_subs[name] = space_subs[name] - step
        prof_subs = calc_profits(space_subs, space)

        if space_subs[name] <= 0:
            space_subs[name] = 0

        elif space_sum[name] >= space:
            space_sum[name] = space

        data = [prof_sum[name],prof_subs[name],prof_ini[name]]
        compar_profs = pd.Series(data=data,index=["sum", "subs", "ini"])
        best = compar_profs[compar_profs == compar_profs.max()].index[0]

        if len(compar_profs.unique()) == 1:
            best = "ini"
        elif all(compar_profs - compar_profs['ini'] <= 0.4):
            compar_profs - compar_profs['ini']
            compar_profs - compar_profs['ini'] <= 0.4
            best = "ini"

        store_spaces = {"sum": space_sum,
                        "subs": space_subs,
                        "ini": space_ini}

        res[name] = store_spaces[best][name]
    return res

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space = 100
df = pd.DataFrame(index=("x1", "x2", "x3", "x4"))
df[0] = np.linspace(0, space-20, 4)

games = 100
for g in range(1, games + 1):
    df[g] = new_game(df, space)

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df.T.plot()
plt.xlabel("Games")
plt.ylabel("Space")
sns.despine()
plt.ylim(0,100)

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df.iloc[:,-1]

Respira!

 Health  308  2 mins

Según un estudio publicado en Cell Reports Medicine, practicar ejercicios de respiración durante solo cinco minutos al día durante un mes puede mejorar el estado de ánimo y reducir la ansiedad.

El estudio encontró que el suspiro cíclico, en particular, fue efectivo para mejorar el estado de ánimo en comparación con la meditación de atención plena. Los ejercicios de respiración también pueden afectar la fisiología al inducir un estado físico más relajado y desplazar el equilibrio hacia el sistema parasimpático de descanso y digestión.

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