Last active
July 28, 2025 15:04
-
-
Save cavedave/a11fa410a471b4fb50b656e76e3edbe0 to your computer and use it in GitHub Desktop.
staircase.ipynb
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| # 1) find every record‐breaking year ≥ 1980 | |
| post80 = ts.where(ts.index >= 1980) | |
| current_max = -np.inf | |
| record_years = [] | |
| record_values = [] | |
| for yr, val in post80.items(): | |
| if np.isnan(val): | |
| continue | |
| if val >= current_max: | |
| current_max = val | |
| record_years.append(yr) | |
| record_values.append(val) | |
| # 2) build ALL steps | |
| all_steps = [(s, e, y) for s, e, y in zip(record_years, record_years[1:], record_values)] | |
| # 3) filter to only multi‑year “flat” periods (>=3 yr) | |
| steps = [(s, e, y) for (s, e, y) in all_steps if (e - s) >= 3] | |
| # 4) pre‑compute limits & frame list | |
| xmin, xmax = ts.index.min(), ts.index.max() | |
| ymin, ymax = ts.min() - 0.1, ts.max() + 0.1 | |
| frame_files = [] |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| # Assume df is your DataFrame and ts = df['Anomaly'], indexed by Year | |
| # 1) Identify every record-breaking year ≥ 1980 | |
| post80 = ts.where(ts.index >= 1980) | |
| current = -np.inf | |
| records = [] | |
| values = [] | |
| for yr, val in post80.items(): | |
| if np.isnan(val): continue | |
| if val >= current: | |
| current = val | |
| records.append(yr) | |
| values.append(val) | |
| # 2) Build steps between successive records | |
| all_steps = list(zip(records, records[1:], values)) | |
| # 3) Keep only multi‑year flats (>=3 yr) | |
| steps = [(s, e, y) for s, e, y in all_steps if (e - s) >= 3] | |
| # 4) Plot once, statically | |
| fig, ax = plt.subplots(figsize=(10, 6)) | |
| # zero‑line highlight | |
| ax.axhline(0, linestyle='--', linewidth=1, alpha=0.3) | |
| # full anomaly series | |
| ax.plot(ts.index, ts.values, lw=1, color='lightgray') | |
| # draw and label each plateau | |
| for s, e, y in steps: | |
| ax.hlines(y, s, e, linewidth=3, color='C0') | |
| mid = s + (e - s) / 2 | |
| ax.text( | |
| mid, y + 0.02, | |
| f"{e - s}yr", | |
| ha='center', va='bottom', | |
| fontweight='bold', fontsize=10 | |
| ) | |
| # lock your limits | |
| ax.set_xlim(ts.index.min(), ts.index.max()) | |
| ax.set_ylim(ts.min() - 0.1, ts.max() + 0.1) | |
| # labels & title | |
| ax.set_xlabel('Year') | |
| ax.set_ylabel('Temperature anomaly (°C)') | |
| ax.set_title( | |
| "Staircase of Denial\n" | |
| "“No Warming in Years”", | |
| fontsize=14, fontweight='bold' | |
| ) | |
| # light horizontal grid lines at major ticks | |
| ax.grid( | |
| True, | |
| which='major', | |
| axis='y', | |
| linestyle='--', | |
| linewidth=0.5, | |
| alpha=0.3 | |
| ) | |
| # 6) source & notes | |
| ax.text(0.99, -0.1, | |
| "Source: HadCRUT5 by @iamreddave", | |
| ha='right', va='top', | |
| transform=ax.transAxes, | |
| fontsize=9, color='gray') | |
| ax.text(0.20, -0.1, | |
| "Anomalies relative to 1961-1990", | |
| ha='right', va='top', | |
| transform=ax.transAxes, | |
| fontsize=9, color='gray') | |
| # save | |
| output_path = '/content/staircase_static.png' | |
| fig.savefig(output_path, dpi=150, bbox_inches='tight') | |
| plt.show() | |
| print("Saved static image to", output_path) |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "provenance": [], | |
| "authorship_tag": "ABX9TyOJRpWpnG2+n3gKKhzSdlgP", | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/cavedave/a11fa410a471b4fb50b656e76e3edbe0/staircase.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "lets make a staircase of denial gif and share the code\n", | |
| "\n", | |
| "the idea is that between every el nino hot year people go 'look it hasnt gotten warmer in X years global warming is disproven. Checkmate now, king me'\n", | |
| "\n", | |
| "And i want to make a way to easily show that warming continues inside the el nino cyscle and new record year is coming." | |
| ], | |
| "metadata": { | |
| "id": "QpovhOROUzWI" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## staircase" | |
| ], | |
| "metadata": { | |
| "id": "a1l5rz8vKgQW" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "I heard about the escalator of denial here and wanted to update it and make the code public https://skepticalscience.com/graphics.php?g=465\n", | |
| "\n", | |
| "and this tweet also shows it https://x.com/RARohde/status/1614991656583041024\n", | |
| "\n" | |
| ], | |
| "metadata": { | |
| "id": "15WdK15XKh4T" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "hadcrut data https://www.metoffice.gov.uk/hadobs/hadcrut5/data/HadCRUT.5.0.2.0/download.html" | |
| ], | |
| "metadata": { | |
| "id": "QG8tIYGLKnfG" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "d69c314c", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 206 | |
| }, | |
| "outputId": "66ee5d72-1def-414c-b09f-ab4e618b55cc" | |
| }, | |
| "source": [ | |
| "import pandas as pd\n", | |
| "\n", | |
| "url = \"https://www.metoffice.gov.uk/hadobs/hadcrut5/data/HadCRUT.5.0.2.0/analysis/diagnostics/HadCRUT.5.0.2.0.analysis.summary_series.global.annual.csv\"\n", | |
| "df = pd.read_csv(url)\n", | |
| "display(df.head())" | |
| ], | |
| "execution_count": 1, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| " Time Anomaly (deg C) Lower confidence limit (2.5%) \\\n", | |
| "0 1850 -0.417711 -0.589256 \n", | |
| "1 1851 -0.233350 -0.411868 \n", | |
| "2 1852 -0.229399 -0.409382 \n", | |
| "3 1853 -0.270354 -0.430009 \n", | |
| "4 1854 -0.291521 -0.432712 \n", | |
| "\n", | |
| " Upper confidence limit (97.5%) \n", | |
| "0 -0.246166 \n", | |
| "1 -0.054832 \n", | |
| "2 -0.049416 \n", | |
| "3 -0.110700 \n", | |
| "4 -0.150330 " | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-21ca6b15-ae58-4874-9be6-3f9a3906bf8c\" class=\"colab-df-container\">\n", | |
| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Time</th>\n", | |
| " <th>Anomaly (deg C)</th>\n", | |
| " <th>Lower confidence limit (2.5%)</th>\n", | |
| " <th>Upper confidence limit (97.5%)</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>1850</td>\n", | |
| " <td>-0.417711</td>\n", | |
| " <td>-0.589256</td>\n", | |
| " <td>-0.246166</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>1851</td>\n", | |
| " <td>-0.233350</td>\n", | |
| " <td>-0.411868</td>\n", | |
| " <td>-0.054832</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>1852</td>\n", | |
| " <td>-0.229399</td>\n", | |
| " <td>-0.409382</td>\n", | |
| " <td>-0.049416</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>1853</td>\n", | |
| " <td>-0.270354</td>\n", | |
| " <td>-0.430009</td>\n", | |
| " <td>-0.110700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>1854</td>\n", | |
| " <td>-0.291521</td>\n", | |
| " <td>-0.432712</td>\n", | |
| " <td>-0.150330</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>\n", | |
| " <div class=\"colab-df-buttons\">\n", | |
| "\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21ca6b15-ae58-4874-9be6-3f9a3906bf8c')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n", | |
| " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| "\n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-buttons div {\n", | |
| " margin-bottom: 4px;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-21ca6b15-ae58-4874-9be6-3f9a3906bf8c button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-21ca6b15-ae58-4874-9be6-3f9a3906bf8c');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
| " </script>\n", | |
| " </div>\n", | |
| "\n", | |
| "\n", | |
| " <div id=\"df-1ac674bb-d601-4afe-ab01-1191b01bed61\">\n", | |
| " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1ac674bb-d601-4afe-ab01-1191b01bed61')\"\n", | |
| " title=\"Suggest charts\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <g>\n", | |
| " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n", | |
| " </g>\n", | |
| "</svg>\n", | |
| " </button>\n", | |
| "\n", | |
| "<style>\n", | |
| " .colab-df-quickchart {\n", | |
| " --bg-color: #E8F0FE;\n", | |
| " --fill-color: #1967D2;\n", | |
| " --hover-bg-color: #E2EBFA;\n", | |
| " --hover-fill-color: #174EA6;\n", | |
| " --disabled-fill-color: #AAA;\n", | |
| " --disabled-bg-color: #DDD;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-quickchart {\n", | |
| " --bg-color: #3B4455;\n", | |
| " --fill-color: #D2E3FC;\n", | |
| " --hover-bg-color: #434B5C;\n", | |
| " --hover-fill-color: #FFFFFF;\n", | |
| " --disabled-bg-color: #3B4455;\n", | |
| " --disabled-fill-color: #666;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart {\n", | |
| " background-color: var(--bg-color);\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: var(--fill-color);\n", | |
| " height: 32px;\n", | |
| " padding: 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart:hover {\n", | |
| " background-color: var(--hover-bg-color);\n", | |
| " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: var(--button-hover-fill-color);\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart-complete:disabled,\n", | |
| " .colab-df-quickchart-complete:disabled:hover {\n", | |
| " background-color: var(--disabled-bg-color);\n", | |
| " fill: var(--disabled-fill-color);\n", | |
| " box-shadow: none;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-spinner {\n", | |
| " border: 2px solid var(--fill-color);\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " animation:\n", | |
| " spin 1s steps(1) infinite;\n", | |
| " }\n", | |
| "\n", | |
| " @keyframes spin {\n", | |
| " 0% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " }\n", | |
| " 20% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 30% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 40% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 60% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 80% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " 90% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " }\n", | |
| "</style>\n", | |
| "\n", | |
| " <script>\n", | |
| " async function quickchart(key) {\n", | |
| " const quickchartButtonEl =\n", | |
| " document.querySelector('#' + key + ' button');\n", | |
| " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", | |
| " quickchartButtonEl.classList.add('colab-df-spinner');\n", | |
| " try {\n", | |
| " const charts = await google.colab.kernel.invokeFunction(\n", | |
| " 'suggestCharts', [key], {});\n", | |
| " } catch (error) {\n", | |
| " console.error('Error during call to suggestCharts:', error);\n", | |
| " }\n", | |
| " quickchartButtonEl.classList.remove('colab-df-spinner');\n", | |
| " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", | |
| " }\n", | |
| " (() => {\n", | |
| " let quickchartButtonEl =\n", | |
| " document.querySelector('#df-1ac674bb-d601-4afe-ab01-1191b01bed61 button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| " })();\n", | |
| " </script>\n", | |
| " </div>\n", | |
| "\n", | |
| " </div>\n", | |
| " </div>\n" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "summary": "{\n \"name\": \"display(df\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"Time\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1850,\n \"max\": 1854,\n \"num_unique_values\": 5,\n \"samples\": [\n 1851,\n 1854,\n 1852\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Anomaly (deg C)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.07676006671244463,\n \"min\": -0.4177114,\n \"max\": -0.22939907,\n \"num_unique_values\": 5,\n \"samples\": [\n -0.2333498,\n -0.29152083,\n -0.22939907\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Lower confidence limit (2.5%)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.07597173954235004,\n \"min\": -0.58925647,\n \"max\": -0.40938243,\n \"num_unique_values\": 5,\n \"samples\": [\n -0.41186792,\n -0.4327115,\n -0.40938243\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Upper confidence limit (97.5%)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.08081957301408688,\n \"min\": -0.2461663,\n \"max\": -0.04941572,\n \"num_unique_values\": 5,\n \"samples\": [\n -0.054831687,\n -0.15033019,\n -0.04941572\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import imageio\n", | |
| "from scipy.signal import argrelextrema\n", | |
| "\n", | |
| "\n", | |
| "# rename columns\n", | |
| "df = df.rename(columns={'Time':'Year', 'Anomaly (deg C)':'Anomaly'})\n", | |
| "df.set_index('Year', inplace=True)\n", | |
| "ts = df['Anomaly']" | |
| ], | |
| "metadata": { | |
| "id": "017GpJsNLoBY" | |
| }, | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Make the years" | |
| ], | |
| "metadata": { | |
| "id": "srGGdU8cwj1Y" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# 1) find every record‐breaking year ≥ 1980\n", | |
| "post80 = ts.where(ts.index >= 1980)\n", | |
| "current_max = -np.inf\n", | |
| "record_years = []\n", | |
| "record_values = []\n", | |
| "\n", | |
| "for yr, val in post80.items():\n", | |
| " if np.isnan(val):\n", | |
| " continue\n", | |
| " if val >= current_max:\n", | |
| " current_max = val\n", | |
| " record_years.append(yr)\n", | |
| " record_values.append(val)\n", | |
| "\n", | |
| "# 2) build ALL steps\n", | |
| "all_steps = [(s, e, y) for s, e, y in zip(record_years, record_years[1:], record_values)]\n", | |
| "\n", | |
| "# 3) filter to only multi‑year “flat” periods (>=3 yr)\n", | |
| "steps = [(s, e, y) for (s, e, y) in all_steps if (e - s) >= 3]\n", | |
| "\n", | |
| "# 4) pre‑compute limits & frame list\n", | |
| "xmin, xmax = ts.index.min(), ts.index.max()\n", | |
| "ymin, ymax = ts.min() - 0.1, ts.max() + 0.1\n", | |
| "frame_files = []" | |
| ], | |
| "metadata": { | |
| "id": "sTNDDL3Xjlf5" | |
| }, | |
| "execution_count": 3, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import imageio\n", | |
| "\n", | |
| "# …after loading df and computing ts, steps, xmin,xmax,ymin,ymax…\n", | |
| "\n", | |
| "frame_files = []\n", | |
| "\n", | |
| "for year in ts.index:\n", | |
| " if year < 1980:\n", | |
| " continue\n", | |
| " fig, ax = plt.subplots(figsize=(8,5))\n", | |
| " ax.set_position([0.12, 0.15, 0.80, 0.75])\n", | |
| "\n", | |
| " # 1) Zero line\n", | |
| " ax.axhline(0, color='black', linestyle='--', linewidth=1, alpha=0.1)\n", | |
| "\n", | |
| " # 2) full series\n", | |
| " ax.plot(ts.index, ts.values, color='lightgray', lw=1)\n", | |
| "\n", | |
| " # 3) staircase steps\n", | |
| " for s, e, y in steps:\n", | |
| " if e <= year:\n", | |
| " ax.hlines(y, s, e, lw=3, color='C0')\n", | |
| " elif s <= year < e:\n", | |
| " ax.hlines(y, s, year, lw=4, color='tomato')\n", | |
| " ax.text(\n", | |
| " year, y + 0.02,\n", | |
| " f\"No warming in {year - s} yr\",\n", | |
| " ha='right', va='bottom',\n", | |
| " color='tomato', fontsize=11, fontweight='bold'\n", | |
| " )\n", | |
| "\n", | |
| " # 4) lock axes\n", | |
| " ax.set_xlim(xmin, xmax)\n", | |
| " ax.set_ylim(ymin, ymax)\n", | |
| "\n", | |
| " # 5) titles & labels\n", | |
| " ax.set_title(\n", | |
| " \"Staircase of Denial — “No warming in years”\\n\",\n", | |
| " loc='center', fontsize=16, fontweight='bold',\n", | |
| " y=0.94, pad=5\n", | |
| " )\n", | |
| " ax.set_xlabel('Year', fontsize=12)\n", | |
| " ax.set_ylabel('Temperature anomaly (°C)', fontsize=12)\n", | |
| "\n", | |
| " # 6) source & notes\n", | |
| " ax.text(0.99, -0.1,\n", | |
| " \"Source: HadCRUT5 by @iamreddave\",\n", | |
| " ha='right', va='top',\n", | |
| " transform=ax.transAxes,\n", | |
| " fontsize=9, color='gray')\n", | |
| " ax.text(0.20, -0.1,\n", | |
| " \"Anomalies relative to 1961-1990\",\n", | |
| " ha='right', va='top',\n", | |
| " transform=ax.transAxes,\n", | |
| " fontsize=9, color='gray')\n", | |
| "\n", | |
| " # 7) save\n", | |
| " fname = f'frame_{year}.png'\n", | |
| " fig.savefig(fname, dpi=120)\n", | |
| " plt.close(fig)\n", | |
| " frame_files.append(fname)" | |
| ], | |
| "metadata": { | |
| "id": "l76R5v5GujNI" | |
| }, | |
| "execution_count": 7, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "output_path = '/content/staircase_hadcrut5.gif'\n", | |
| "with imageio.get_writer(\n", | |
| " '/content/staircase_hadcrut5.gif',\n", | |
| " mode='I',\n", | |
| " fps=7\n", | |
| ") as writer:\n", | |
| " for fn in frame_files:\n", | |
| " writer.append_data(imageio.imread(fn))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Jaq21m9jungW", | |
| "outputId": "ac0e173b-334a-4b74-ef32-cdcd2734c9a6" | |
| }, | |
| "execution_count": 8, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/tmp/ipython-input-8-3463716466.py:8: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning disappear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.\n", | |
| " writer.append_data(imageio.imread(fn))\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## one picture" | |
| ], | |
| "metadata": { | |
| "id": "BODzFHpIi1oM" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "# Assume df is your DataFrame and ts = df['Anomaly'], indexed by Year\n", | |
| "\n", | |
| "# 1) Identify every record-breaking year ≥ 1980\n", | |
| "post80 = ts.where(ts.index >= 1980)\n", | |
| "current = -np.inf\n", | |
| "records = []\n", | |
| "values = []\n", | |
| "\n", | |
| "for yr, val in post80.items():\n", | |
| " if np.isnan(val): continue\n", | |
| " if val >= current:\n", | |
| " current = val\n", | |
| " records.append(yr)\n", | |
| " values.append(val)\n", | |
| "\n", | |
| "# 2) Build steps between successive records\n", | |
| "all_steps = list(zip(records, records[1:], values))\n", | |
| "\n", | |
| "# 3) Keep only multi‑year flats (>=3 yr)\n", | |
| "steps = [(s, e, y) for s, e, y in all_steps if (e - s) >= 3]\n", | |
| "\n", | |
| "# 4) Plot once, statically\n", | |
| "fig, ax = plt.subplots(figsize=(10, 6))\n", | |
| "\n", | |
| "# zero‑line highlight\n", | |
| "ax.axhline(0, linestyle='--', linewidth=1, alpha=0.3)\n", | |
| "\n", | |
| "# full anomaly series\n", | |
| "ax.plot(ts.index, ts.values, lw=1, color='lightgray')\n", | |
| "\n", | |
| "# draw and label each plateau\n", | |
| "for s, e, y in steps:\n", | |
| " ax.hlines(y, s, e, linewidth=3, color='C0')\n", | |
| " mid = s + (e - s) / 2\n", | |
| " ax.text(\n", | |
| " mid, y + 0.02,\n", | |
| " f\"{e - s}yr\",\n", | |
| " ha='center', va='bottom',\n", | |
| " fontweight='bold', fontsize=10\n", | |
| " )\n", | |
| "\n", | |
| "# lock your limits\n", | |
| "ax.set_xlim(ts.index.min(), ts.index.max())\n", | |
| "ax.set_ylim(ts.min() - 0.1, ts.max() + 0.1)\n", | |
| "\n", | |
| "# labels & title\n", | |
| "ax.set_xlabel('Year')\n", | |
| "ax.set_ylabel('Temperature anomaly (°C)')\n", | |
| "ax.set_title(\n", | |
| " \"Staircase of Denial\\n\"\n", | |
| " \"“No Warming in Years”\",\n", | |
| " fontsize=14, fontweight='bold'\n", | |
| ")\n", | |
| "\n", | |
| "# light horizontal grid lines at major ticks\n", | |
| "ax.grid(\n", | |
| " True,\n", | |
| " which='major',\n", | |
| " axis='y',\n", | |
| " linestyle='--',\n", | |
| " linewidth=0.5,\n", | |
| " alpha=0.3\n", | |
| ")\n", | |
| "\n", | |
| " # 6) source & notes\n", | |
| "ax.text(0.99, -0.1,\n", | |
| " \"Source: HadCRUT5 by @iamreddave\",\n", | |
| " ha='right', va='top',\n", | |
| " transform=ax.transAxes,\n", | |
| " fontsize=9, color='gray')\n", | |
| "ax.text(0.20, -0.1,\n", | |
| " \"Anomalies relative to 1961-1990\",\n", | |
| " ha='right', va='top',\n", | |
| " transform=ax.transAxes,\n", | |
| " fontsize=9, color='gray')\n", | |
| "\n", | |
| "# save\n", | |
| "output_path = '/content/staircase_static.png'\n", | |
| "fig.savefig(output_path, dpi=150, bbox_inches='tight')\n", | |
| "plt.show()\n", | |
| "\n", | |
| "print(\"Saved static image to\", output_path)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 621 | |
| }, | |
| "id": "dCnfGQMOi2uW", | |
| "outputId": "92671b57-0917-404f-d242-f3c4e0232e18" | |
| }, | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 1000x600 with 1 Axes>" | |
| ], | |
| "image/png": "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\n" | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Saved static image to /content/staircase_static.png\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Print out each plateau’s start, end and length\n", | |
| "for s, e, y in steps:\n", | |
| " print(f\"{s} → {e} : {e - s} years at {y:.3f} °C\")\n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "huOlXlJ_mxUu", | |
| "outputId": "a93b0499-592e-4845-a662-14f2a9ea9fca" | |
| }, | |
| "execution_count": 6, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "1981 → 1988 : 7 years at 0.250 °C\n", | |
| "1990 → 1995 : 5 years at 0.361 °C\n", | |
| "1998 → 2005 : 7 years at 0.577 °C\n", | |
| "2005 → 2010 : 5 years at 0.607 °C\n", | |
| "2010 → 2015 : 5 years at 0.680 °C\n", | |
| "2016 → 2023 : 7 years at 0.933 °C\n" | |
| ] | |
| } | |
| ] | |
| } | |
| ] | |
| } |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment