Import

import matplotlib.pyplot as plt
import pandas as pd
#from cartopy import crs as ccrs
#from cartopy import feature as cfeature
from datetime import datetime,timedelta
#from dateutil.relativedelta import relativedelta

import geopandas as gpd
from lonboard import viz, Map, ScatterplotLayer, HeatmapLayer
import duckdb

# Use the current time, or set your own for a past time.
# Set current to False if you want to specify a past time.

nowTime = datetime.now()

current = True
current = False
if (current):
    validTime = datetime.now()
    year = validTime.year
    month = validTime.month
    day = validTime.day
    hour = validTime.hour
    validTime = datetime(year, month, day, hour)
    offset = timedelta(hours = 1)
    validTime = validTime - offset
else:
    year = 2026
    month = 1
    day = 1
    hour = 0
    time_1 = datetime(year, month, day, hour)

time_0 = time_1 - timedelta(hours=1)
YYYY_0 = time_0.strftime("%Y")
YYYY_1 = time_1.strftime("%Y")
print(time_0, time_1)
# Handle edge case when the two hours straddle the end/beginning of a yearw
if (YYYY_0 == YYYY_1):
    URLs = [f'https://data.source.coop/dynamical/asos-parquet/year={YYYY_1}/data.parquet']
else:
    URLs = [f'https://data.source.coop/dynamical/asos-parquet/year={YYYY_0}/data.parquet',
            f'https://data.source.coop/dynamical/asos-parquet/year={YYYY_1}/data.parquet']

URLs

df = duckdb.execute("""
    SELECT *
    FROM read_parquet($1, hive_partitioning=true)
    WHERE 
---      country = 'FR' AND
    valid BETWEEN $2 AND $3
    ORDER BY country
""", [URLs, time_0, time_1]).fetchdf()

df

from numpy.random import default_rng
from pandas import Series, MultiIndex

rng = default_rng(0)

country = [ 'ZA', 'MX', 'CA', 'RU', 'KR', 'DE', 'BR', 'CN', 'GB', 'US', 'AU', 'IN', 'JP', 'FR']
years = (df['valid'])

index = MultiIndex.from_product([country, years], names=['country', 'valid'])
s = Series(rng.integers(20, 100, size=len(index)), index=index, name='count')

s

from numpy.random import default_rng
from pandas import Series, MultiIndex

#rng = default_rng(0)

country = ['ZA', 'MX', 'CA', 'RU', 'KR', 'DE', 'BR', 'CN', 'GB', 'US', 'AU', 'IN', 'JP', 'FR']
valid = [2000, 2005, 2010, 2015, 2020, 2025]

index = MultiIndex.from_product([country, valid], names=['country', 'valid'])
s = Series(rng.integers(20, 100, size=len(index)), index=index, name='count')

s

from seaborn import barplot

fig, ax = plt.subplots(figsize=(12, 6))

barplot(
    data=s.reset_index(),
    x='country', y='count', hue='valid',
    hue_order=years, palette='viridis_r',
    ax=ax
)
#bars = ax.barh(df['country'], df['tmpc'], color='#4682b4')
ax.legend(ncol=3, title='Year', loc='lower right', bbox_to_anchor=(1, 1))

ax.spines[['top', 'right', 'left']].set_visible(False)
ax.yaxis.grid(color=ax.get_facecolor())
ax.set_xticklabels([t.get_text().title() for t in ax.get_xticklabels()]);
ax.set_xlabel('');
plt.tight_layout()
#plt.savefig('Temp2.pdf', bbox_inches='tight')
# 5. Show the plot
plt.show()

fig, ax = plt.subplots(figsize=(10, 6))

bars = ax.barh(df['country'], df['tmpc'], color='#4682b4')

ax.set_title('Temp in Celsius for 2025-12-31 23:10:00 to 2026-01-01 00:00:00', fontsize=14, pad=15)
ax.set_xlabel('Temp ($^\circ$C)', fontsize=12)
ax.set_xlim(-50, 50)
ax.set_ylabel('Country', fontsize=12)

plt.tight_layout()

# 5. Show the plot
plt.show()

import pandas as pd
import fsspec
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature

# 1. Load Parquet Data from URL
# parquet_url = "https://data.source.coop/dynamical/asos-parquet/year=2025/data.parquet" # Replace with your actual URL
# with fsspec.open(parquet_url) as file:
#     df = pd.read_parquet(file)

# 2. Set up Matplotlib Figure with Plate Carree projection
fig = plt.figure(figsize=(14, 7))
ax = plt.axes(projection=ccrs.PlateCarree())

# 3. Plot the temperature data (assuming columns: 'longitude', 'latitude', 'temperature')
scatter = ax.scatter(
    df['longitude'], 
    df['latitude'], 
    c=df['tmpc'],  # The temperature parameter
    cmap='coolwarm',      # Colormap for temperature (blue = cold, red = hot)
    s=15,                 # Marker size
    alpha=0.8,
    transform=ccrs.PlateCarree()
)

# 4. Add map features for context
ax.coastlines(linewidth=0.8)
ax.add_feature(cfeature.BORDERS, linestyle=':', alpha=0.5)
ax.add_feature(cfeature.LAND, facecolor='lightgray', alpha=0.3)
ax.add_feature(cfeature.COASTLINE)
ax.add_feature(cfeature.OCEAN, color='lightblue')


# 4. Add geographic visual anchors
ax.coastlines(resolution="110m", color="black", linewidth=1)
ax.gridlines(draw_labels=True, dms=True, xlocs=[-180, -90, 0, 90, 180])

# 5. Add a colorbar and title
cbar = plt.colorbar(scatter, ax=ax, orientation='horizontal', pad=0.05, shrink=0.7)
cbar.set_label('Temperature (°C)', fontsize=12)
plt.title('Global distribution of stations', fontsize=16, pad=15)

# Display the map
plt.show()

import pandas as pd
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import numpy as np

# 1. Load data from Parquet
# Ensure your Parquet file has columns named 'lat', 'lon', and 'temperature'
#df = pd.read_csv('global_temperature_data.parquet') 

# 2. Setup the figure and map projection (Flat Globe / Robinson)
fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson(central_longitude=0))

# 3. Plot the temperature data
# We use a scatter plot, mapping longitude/latitude to the globe
scatter = ax.scatter(
    df['longitude'], df['latitude'], 
    c=df['tmpc'], 
    cmap='coolwarm', 
    s=5, # Marker size
    alpha=0.8,
    transform=ccrs.PlateCarree() # Specifies that the data's original coordinates are lat/lon
)

# 4. Add geographic context and formatting
ax.coastlines(color='black', linewidth=1)
ax.gridlines(draw_labels=True, linestyle='--', color='lightgray')
ax.gridlines(draw_labels=True, dms=True, xlocs=[-180, -120, -60, 0, 60, 120, 180])
ax.add_feature(cfeature.BORDERS, linestyle=':')
ax.add_feature(cfeature.LAND, facecolor='lightgray', alpha=0.5)
ax.add_feature(cfeature.OCEAN, color='lightblue')

# 5. Add a colorbar and title
cbar = plt.colorbar(scatter, ax=ax, orientation='horizontal', pad=0.05, shrink=0.7)
cbar.set_label('Temperature (°C)')
plt.title('Global distribution of stations')

# Show the plot
plt.show()