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lets-plot in marimo: the import * problem #1126

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VivaldoMendes opened this issue Jul 6, 2024 · 19 comments
Closed

lets-plot in marimo: the import * problem #1126

VivaldoMendes opened this issue Jul 6, 2024 · 19 comments

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@VivaldoMendes
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Hi,

I need to use a reactive notebook in Python, so I am playing with marimo.py. I also tried producing some plots using lets-plot.

Unfortunately, when I tried the first line, marimo spells out:

import * is not allowed in marimo.

Do you have an example of how to proceed with lets-plot in order to avoid the import star problem?

Thanks
@alshan
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alshan commented Jul 8, 2024

To work around marimo restrictions you can:

Import the lets_plot module and then use dot-notation:

import lets_plot as pl

pl.LetsPlot.setup_html()

pl.ggplot(...
  1. import symbols manually as necessary:
from lets_plot import ggplot, aes, geom_point, ggtitle
  1. Import all symbols from lets-plot using the importlib facility:
import lets_plot
import importlib

# Get the __all__ list from lets_plot
all_symbols = lets_plot.__all__

# Import each symbol into the current namespace
for symbol in all_symbols:
    globals()[symbol] = getattr(lets_plot, symbol)

This is equivalent to a "star" import.

However, lets-plot doesn't seem to work well in marimo notebooks out-of-the-box, according to this report: #1018.
Unfortunately, we haven't been able to take a closer look at marimo yet.

@VivaldoMendes
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Thanks for your help.

I tried as you suggested and encountered a problem that I had found in a previous attempt of my own: the code blocks run without any error being spat out, but no plot is visible (or produced?). Please see the image below:

a

@JetBrains JetBrains deleted a comment from technewbie12 Jul 8, 2024
@alshan
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alshan commented Jul 8, 2024

You either need to have xix as the last expression in the cell or call xix.show().

@VivaldoMendes
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If I close the cell with xix, the output is the same as if there were no xix: no error and no plot.

If I call xix.show(), there is no error, no plot, and the following output pops up:

{'text/html': '<html lang="en">\n   <head>\n       <meta charset="UTF-8">\n       <script type="text/javascript" data-lets-plot-script="library" src="https://cdn.jsdelivr.net/gh/JetBrains/lets-plot@v4.3.3/js-package/distr/lets-plot.min.js"></script>\n   </head>\n   <body>\n          <div id="0n7UpK"></div>\n   <script type="text/javascript" data-lets-plot-script="plot">\n       var plotSpec={\n"data":{\n},\n"mapping":{\n"x":"rating",\n"fill":"cond"\n},\n"data_meta":{\n},\n"ggsize":{\n"width":700.0,\n"height":300.0\n},\n"theme":{\n"flavor":"high_contrast_dark",\n"legend_background":{\n"fill":"#14181e",\n"blank":false\n},\n"panel_grid_major_x":"blank",\n"plot_background":{\n"fill":"#14181e",\n"blank":false\n}\n},\n"kind":"plot",\n"scales":[{\n"aesthetic":"fill",\n"type":"seq",\n"scale_mapper_kind":"color_brewer"\n}],\n"layers":[{\n"geom":"density",\n"mapping":{\n},\n"data_meta":{\n},\n"color":"dark_green",\n"alpha":0.7,\n"data":{\n"..density..":[0.003297454217890694,0.003512417332407343,0.0037331684490372443,0.00395922404477703,0.0041900756598468696,0.0044251980886343455,0.004664058421056729,0.004906125819516359,0.00515088189424391,0.0053978315193415425,0.005646513913898397,0.005896513797764766,0.006147472420492328,0.006399098255052908,0.006651177145617216,0.006903581701175845,0.007156279734257008,0.007409341556442306,0.007662945959662886,0.007917384734088208,0.008173065599372299,0.008430513455537924,0.008690369892179884,0.008953390929173754,0.009220442997827311,0.009492497207495435,0.009770621978157621,0.01005597415339623,0.010349788739715178,0.010653367446368377,0.010968066224085244,0.011295282020664139,0.01163643898587157,0.011992974367111825,0.012366324340758041,0.012757910021881105,0.013169123887560768,0.013601316836368331,0.014055786089469904,0.014533764117746673,0.015036408755095747,0.015564794631475466,0.016119906031143334,0.01670263125276983,0.01731375851953352,0.017953973459699533,0.018623858152246195,0.01932389170842342,0.02005445233914936,0.020815820840183998,0.02160818541221305,0.022431647721323427,0.023286230096705968,0.024171883756494456,0.025088497949046523,0.026035909895212712,0.027013915416686237,0.0280222801358234,0.02906075113283251,0.030129068946452175,0.031226979803769716,0.032354247963361955,0.03351066805330466,0.03469607728176567,0.03591036739300617,0.037153496235937916,0.03842549880635589,0.03972649761812765,0.04105671225362106,0.04241646794019882,0.04380620299843255,0.045226475009499785,0.046677965554678076,0.0481614833894717,0.049677965929093106,0.05122847894097712,0.05281421436371837,0.05443648620005712,0.056096724463805925,0.05779646719619997,0.05953735060512073,0.06132109741985685,0.0631495035932358,0.0650244235206952,0.0669477539807188,0.06892141703160454,0.0709473421244259,0.0730274477100754,0.07516362262846023,0.07735770756952248,0.0796114768883744,0.0819266210404141,0.08430472987712538,0.08674727701008855,0.08925560541056284,0.09183091436630628,0.0944742478677374,0.09718648444410423,0.09996832841910494,0.10282030250661313,0.10574274162299499,0.10873578775505273,0.11179938569378932,0.114933279425561,0.1181370089650014,0.12140990741914823,0.12475109808976426,0.1281594914506719,0.13163378187820293,0.13517244406425452,0.13877372910107622,0.14243566029244184,0.14615602881459114,0.1499323894192076,0.1537620564365777,0.15764210039662946,0.16156934563566464,0.16554036929421656,0.1695515021340009,0.17359883160716827,0.17767820759742364,0.18178525121912553,0.18591536700702513,0.1900637587564705,0.19422544918310705,0.19839530346457523,0.20256805660740262,0.20673834445390316,0.21090073801061532,0.21504978064634783,0.21918002757919625,0.22328608695303692,0.2273626616999855,0.231404591300895,0.23540689249544414,0.23936479796037716,0.2432737919719127,0.24712964209808885,0.250928426029794,0.2546665527550936,0.2583407774088055,0.26194820928539675,0.26548631268442185,0.26895290045900755,0.27234612035357275,0.275664434440441,0.2789065921891876,0.2820715979199729,0.2851586735951879,0.2881672180850949,0.2910967641958301,0.2939469348658496,0.29671740001434976,0.2994078355581419,0.3020178860990557,0.30454713272089895,0.30454713272089895,0.306995067223279,0.30936107396131823,0.3116444202585381,0.3138442561201482,0.3159596237018505,0.31798947669270206,0.3199327094581255,0.32178819547021237,0.3235548342367348,0.3252316056378299,0.3268176302997498,0.32831223438776946,0.32971501699362044,0.33102591813404375,0.3322452852721498,0.3333739362266136,0.33441321634794285,0.33536504791691096,0.3362319698566909,0.33701716604424065,0.33772448075339584,0.33835842005547256,0.3389241383352393,0.33942740944174526,0.33987458237484197,0.34027252179870415,0.34062853406241816,0.3409502797840066,0.34124567440766745,0.3415227784648909,0.3417896795497278,0.34205436824939334,0.34232461044757556,0.34260781853490735,0.3429109241163099,0.34324025479748377,0.34360141756343876,0.343999191133234,0.3444374294910441,0.3449189785598529,0.3454456077071964,0.3460179574601254,0.34663550446743896,0.34729654439011043,0.3479981930348163,0.3479981930348163,0.34873640567969494,0.34950601418451355,0.35030078113766683,0.35111346997712223,0.3519359297382378,0.35275919283365176,0.35357358406353734,0.3543688388914952,0.3551342289041014,0.35585869230124134,0.35653096723938127,0.3571397258692089,0.3576737069701047,0.35812184518323414,0.3584733949786174,0.3587180476546654,0.35884603985634245,0.35884825230507766,0.3587162976544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      var plotContainer = document.getElementById("0n7UpK");\n       LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n   </script>\n   </body>\n</html>'

aa

In my case, marimo is working OK with other plotting packages.

@alshan
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alshan commented Jul 8, 2024 •
edited
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Apparently marimo has its own system for handling and displaying outputs, which differs from traditional Jupyter notebooks.
AFAIU for some commonly used libraries, marimo team includes built-in support out of the box as well as creates adapters or plugins for popular visualization libraries.
Maybe a good idea would be to ask marimo to create such an adapter for lets-plot.

@VivaldoMendes
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Thank you very much for your help. You have developed a very nice package. I will pass that information to them.

@signup2k
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signup2k commented Aug 9, 2024

@VivaldoMendes

try this, which works in my mamrio.

p = lp.ggplot(data, lp.aes(x='trade_date', y='price', color='symbol')) + lp.geom_line()
fig = p.to_html(iframe=True) # This is important, if you want the rendering as html
ui.Html(fig)

@VivaldoMendes
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@signup2k, Thanks. Unfortunately, your code does not work in my case. I get the following error:

NameError: name 'ui' is not defined

If I remove ui.Html(fig) from your code, I do not get any error, but the plot is nowhere to be seen, which was what I used to get on my first attempt with lets-plot in marimo.

@signup2k
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@signup2k, Thanks. Unfortunately, your code does not work in my case. I get the following error:

NameError: name 'ui' is not defined

If I remove ui.Html(fig) from your code, I do not get any error, but the plot is nowhere to be seen, which was what I used to get on my first attempt with lets-plot in marimo.

I am so sorry for this typo...It's actually mo.Html(fig), where mo comes from import marimo as mo

image

@VivaldoMendes
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I'm sorry, but your code doesn't work. Can you provide a full-fledged MWE that I can use to replicate it?

@signup2k
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I'm sorry, but your code doesn't work. Can you provide a full-fledged MWE that I can use to replicate it?

actually, it's quite simple...

import marimo as mo
import numpy as np
import importlib
import pandas as pd

import lets_plot as lp
lp.LetsPlot.setup_html()

# create dataframe
# create date
dates = pd.date_range(start='2024-01-01', end='2024-01-15')

# add symbols
symbols = ['MSFT', 'NVDA', 'AAPL']

# combine date and symbols
df = pd.DataFrame([(date, symbol) for date in dates for symbol in symbols], 
                  columns=['trade_date', 'symbol'])

# create random prie
np.random.seed(42)  # set random seeds
for symbol in symbols:
    start_price = np.random.uniform(100, 500)  # random initial price
    prices = np.random.normal(loc=start_price, scale=start_price*0.01, size=len(dates))
    prices = np.maximum(prices, 0)  # non-negative price
    df.loc[df['symbol'] == symbol, 'price'] = np.round(prices, 2)

# order
df = df.sort_values(['trade_date', 'symbol']).reset_index(drop=True)


# chart with lets-plot

def chart_with_lp(df):

    p = lp.ggplot(df, lp.aes(x='trade_date', y='price', color='symbol')) \
        + lp.geom_line() \
        + lp.ggtitle('Price')

    return p.to_html(iframe=True)

# show in marimo
fig = chart_with_lp(df)
mo.Html(fig)

@signup2k
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If the mo.Html(fig) still not work. (whichi shows nothing but a blank white canvas.)

You might need to check the browser with marimo running, whether it correctly load lets-plot.min.js

@VivaldoMendes
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@signup2k , thank you very much for your help. It works very well.

Sorry for bothering you again, but how do I import the function ggsave to save a plot? I checked the docs, and they suggest that the use of this function would be immediate. I followed the code there, tried to save a plot, and I got this error:

NameError: name 'ggsave' is not defined

Thanks again.

@signup2k
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@VivaldoMendes

Since, we import lets_plot as lp (because, marimo does not support from lets_plot import *, you shoud add lp. before ggsave (any call from lets_plot, should add lp..

Try lp.ggsave()

@VivaldoMendes
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Thanks for helping. I had tried lp.ggsave() before, but it did not work. Maybe I am forgetting something easy, but following the docs: lp.ggsave(fig, 'plot.pdf', w=8, h=4, unit='in', dpi=300),

I got the error:

raise ValueError("PlotSpec, SupPlotsSpec or GGBunch expected but was: {}".format(type(plot)))
ValueError: PlotSpec, SupPlotsSpec or GGBunch expected but was: <class 'str'>

@alshan
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alshan commented Aug 19, 2024

@VivaldoMendes
In the example code above, the fig variable is a Python string after p.to_html(iframe=True).
Whereas the 1st parameter in ggsave() should be p (again, in terms of the example above).

@VivaldoMendes
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I'm sorry, but I'm a little bit lost. The code above produces the plot; it is visible in marimo. But to save the plot as a pdf, if p is defined inside the function chart_with_lp(), how can I call it outside that function by using that simple lp.ggsave()?

Thanks for the feedback.

a

@alshan
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alshan commented Aug 20, 2024

You need to modify this code. For example, let chart_with_lp(df) return p and add show_lp_in_marimo(p) function where you can do

mo.Html(p.to_html(iframe=True))

@VivaldoMendes
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Hi @alshan and @signup2k, I now know what my problem was: I did not have the CairoSVG library on my Python installation. I had Kaleido and could export any format (pdf, SVG, png) when using other plotting packages. So, I could not figure out that I needed this particular package.

With the most recent marimo version (0.8.2), when I tried to save a plot produced by lets-plot as a PDF file, marimo spit out the following alert:

To export Lets-Plot figure to a PNG or PDF file please install CairoSVG libraryto your Python environment.
CairoSVG is free and distributed under the LGPL-3.0 license.
For more details visit: https://cairosvg.org/documentation/

Problem is solved. Thanks for helping.

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@alshan @signup2k @VivaldoMendes