barrysmyth

import requests
from lxml import html

def search(seq, n=10, m=10):
    """Search the OEIS for sequences matching seq and return a list of results.
    
    Args:
        seq: list of integers
        n: number of results to return.
        m: number of terms to return per result.

barrysmyth

# Create 2-row gridspec to hold the colour bar (cax) and the main graph (ax)
fig, (cax, ax) = plt.subplots(nrows=2,figsize=(15,10), gridspec_kw={"height_ratios":[.05, 1]})

# --- Creating the  colour map
# Use the coolwarm colour map, which comes with Matplotlib.
cmap = plt.cm.coolwarm

# Normalise the log-deaths into a 0, 1 scale
norm = colors.Normalize(vmin=deaths.min(), vmax=deaths.max())

barrysmyth

sns.set_context('talk')
                
fig, ax = plt.subplots(figsize=(15,8))

# Plot the segments as a stacked bar at the x coordinate.
def plot_segments(ax, x, segments, width=0.7, min_y=0, colour_dict=colour_dict):
    current_y = min_y
    for segment in segments:
        
        # Plot a single bar at x with height=segment and its base at current_y

barrysmyth

# Use the coolwarm colour map, which comes with Matplotlib.
cmap = plt.cm.coolwarm

# Normalise the log-deaths into a 0, 1 scale
norm = colors.Normalize(vmin=deaths.min(), vmax=deaths.max())

# Now the ith colour in colours corresponds to the ith country in deaths.
colours = cmap(norm(deaths))

# Build a simple colour dictionary indexed by country name.

barrysmyth

fig, ax = plt.subplots(figsize=(15,8))

# Plot the segments as a stacked bar for a given x coordinate.
# Each x coordinate value will correspond to a country in our dataset.
def plot_segments(ax, x, segments, width=0.7, min_y=0):
    
    current_y = min_y  # The base of each bar.
    
    # Create a new segment for each weekly value and stack the segments.
    for segment in segments:

barrysmyth

# Load in sample dataset
df = pd.read_csv('../data/segmented_bar_graph_example_data.csv').set_index('country').fillna(0)

# Extract the log(deaths)
# Use the sorted index from weeklies to sort deaths in the same way.
deaths = df.loc[weeklies.index]['log_deaths']

# Separate the weekly mobility drops and sort them in descending order of the sum of the
# the weekly drops
weeklies = df.drop(columns=['log_deaths'])\

barrysmyth

# Test a simple heatmap for using the SI dataframe for a sample
# of 10 countries
import pandas as pd
import seaborn as sns

sns.heatmap(
  stringency_for_country_by_date.loc[use_countries[:10]], 
  ax=ax, cbar=True
)

barrysmyth

fig, ax = plt.subplots(figsize=(30, len(use_countries)*1.25))

# ----------------------------
# The Stringency Index Heatmap
# ----------------------------

# For maximum flexibility we use a separate axis to plot the heatmap's 
# colour bar (horizontally) as the SI legend.
cbar_ax = fig.add_axes([.15, .17, .38, 3/len(stringency_for_country_by_date)])