richashworth

# vim:ft=kitty

## name: Tokyo Night Moon
## license: MIT
## author: Folke Lemaitre
## upstream: https://github.com/folke/tokyonight.nvim/raw/main/extras/kitty/tokyonight_moon.conf


background #222436
foreground #c8d3f5

richashworth

vim:fileencoding=utf-8:foldmethod=marker
# Ensures that the configuration file is encoded in UTF-8 and uses marker-based folding.

include ~/.config/kitty/theme.conf
# Includes an external theme configuration file for consistent theming.

font_family Maple Mono NF 
# Sets the font.

italic_font auto

richashworth

@startgantt

<style>
ganttDiagram {
	task {
		FontName Helvetica
		FontColor black
		FontSize 12
		FontStyle
		BackGroundColor GreenYellow

richashworth

const MILLIS_PER_DAY = 1000*60*60*24
const HEADER_ROWS = 1

function hide() {
  const ss    = SpreadsheetApp.getActiveSpreadsheet()
  const sheet = ss.getSheets()[0]
  const dateCol  = sheet.getRange("A:A").getValues().map(function(row) {
    return row[0]
  })

richashworth

pos_adjs = ', '.join(get_related_adjectives("climate change", nlp(positive_phrase)))
neg_adjs = ', '.join(get_related_adjectives("climate change", nlp(negative_phrase)))

pos_row = [positive_phrase, pos_adjs, TextBlob(pos_adjs).sentiment[0]]
neg_row = [negative_phrase, neg_adjs, TextBlob(neg_adjs).sentiment[0]]

HTML(DataFrame([pos_row, neg_row], columns=["Text", "Relevant_Adjectives", "Relevant_Sentiment"]).to_html(index=False)

richashworth

climate_change_forms = ["The facts about climate change",
                        "Changes in our climate",
                        "The climate is changing rapidly",
                        "Because the climate has changed,"]
df = DataFrame(climate_change_forms, columns=["Text"])
df['Lemmas'] = list(map(lambda x: ' '.join(list(map(lambda y: y.lemma_, nlp(x)))), climate_change_forms))
HTML(df.to_html(index=False))

richashworth

pos_adjs = ', '.join(get_related_adjectives("climate change", nlp(positive_phrase)))
neg_adjs = ', '.join(get_related_adjectives("climate change", nlp(negative_phrase)))

pos_row = [positive_phrase, pos_adjs, TextBlob(pos_adjs).sentiment[0]]
neg_row = [negative_phrase, neg_adjs, TextBlob(neg_adjs).sentiment[0]]

HTML(DataFrame([pos_row, neg_row], columns=["Text", "Relevant_Adjectives", "Relevant_Sentiment"]).to_html(index=False)

richashworth

def get_related_adjectives(p_term, doc):
  if len(p_term.split()) >1: _, *_, term = p_term.upper().split() 
  else: term = p_term.upper()
  
  dependencies = list([(x.text.upper(), x.pos_, 
                        [(y.text.upper(), spacy.explain(y.dep_)) for y in list(x.children)]) for x in doc])
  
  matches = list(filter(lambda x: x[2] and (x[0]==term or any(map(lambda a: a[0]==term, x[2]))), dependencies))
  
  adjectives = []

richashworth

positive_phrase = "It's great that people are talking about climate change"
negative_phrase = "Rapid climate change is bad for the environment"
for x in [positive_phrase, negative_phrase]: spacy.displacy.render(nlp(x), jupyter=True)

richashworth

from textblob import TextBlob
positive_phrase = TextBlob("It's great that people are talking about climate change")
positive_phrase.sentiment_assessments