Visualization#

  • author: Hamid Ali Syed

  • email: hamidsyed37[at]gmail[dot]com

Import packages#

import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import hvplot.pandas
import geopandas as gpd
import geoviews as gv
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
from IPython.display import display

Read the data#

df = pd.read_csv("IMD_Radar_Sites_2022.csv").drop(["Unnamed: 0", "State"], axis=1)

counts = df.groupby('Band').agg(count=('Band', 'size'))
display(counts)

# Print the total number of radars
total_radars = counts.sum()
print(f"Total number of radars: {total_radars[0]}")
count
Band
C 4
S 22
X 7 
Total number of radars: 33

import shapely.geometry as sgeom
import numpy as np
from cartopy.geodesic import Geodesic
def draw_circle_on_map(df):
    gd = Geodesic()
    geoms = []
    for _, row in df.iterrows():
        lon, lat = row['Longitude'], row['Latitude']
        if row['Band'] == "X":
            radius=100e3
        else:
            radius=250e3
        cp = gd.circle(lon=lon, lat=lat, radius=radius)
        geoms.append(sgeom.Polygon(cp))
    gdf = gpd.GeoDataFrame(df, geometry=geoms)
    return gdf

gdf = draw_circle_on_map(df)
gdf
Site Latitude Longitude Band geometry
0 Srinagar 34.083656 74.797371 X POLYGON ((74.79737 34.98511, 74.75915 34.98455...
1 Jammu 32.926600 74.857000 X POLYGON ((74.85700 33.82822, 74.81930 33.82767...
2 Jot 32.486800 76.059300 X POLYGON ((76.05930 33.38849, 76.02179 33.38793...
3 Kufri 31.097800 77.267800 X POLYGON ((77.26780 31.99968, 77.23087 31.99913...
4 Murari 30.789800 78.917850 X POLYGON ((78.91785 31.69173, 78.88104 31.69117...
5 Surkandaji 30.411400 78.288500 X POLYGON ((78.28850 31.31338, 78.25184 31.31283...
6 Patiala 30.339800 76.386900 S POLYGON ((76.38690 32.59454, 76.29399 32.59313...
7 Mukteshwar 29.460400 79.655800 X POLYGON ((79.65580 30.36251, 79.61950 30.36196...
8 Palam 28.590100 77.088800 S POLYGON ((77.08880 30.84545, 76.99762 30.84404...
9 Delhi 28.563200 77.191200 C POLYGON ((77.19120 30.81855, 77.10004 30.81715...
10 Mohanbari 27.472800 94.912000 S POLYGON ((94.91200 29.72852, 94.82184 29.72712...
11 Jaipur 26.912400 75.787300 C POLYGON ((75.78730 29.16831, 75.69764 29.16690...
12 Lucknow 26.846700 80.946200 S POLYGON ((80.94620 29.10263, 80.85659 29.10123...
13 Patna 25.594100 85.137600 S POLYGON ((85.13760 27.85043, 85.04904 27.84903...
14 Sohra 25.270200 91.732300 S POLYGON ((91.73230 27.52664, 91.64400 27.52523...
15 Agartala 23.831500 91.286800 S POLYGON ((91.28680 26.08838, 91.19961 26.08698...
16 Bhopal 23.259900 77.412600 S POLYGON ((77.41260 25.51695, 77.32583 25.51555...
17 Bhuj 23.242000 69.666900 S POLYGON ((69.66690 25.49906, 69.58014 25.49766...
18 Kolkata 22.572600 88.363900 S POLYGON ((88.36390 24.82985, 88.27761 24.82846...
19 Nagpur 21.145800 79.088200 S POLYGON ((79.08820 23.40346, 79.00287 23.40206...
20 Paradip 20.316600 86.611400 S POLYGON ((86.61140 22.57449, 86.52658 22.57309...
21 Veravali 19.734300 72.876300 C POLYGON ((72.87630 21.99234, 72.79183 21.99095...
22 Gopalpur 19.264700 84.862000 S POLYGON ((84.86200 21.52287, 84.77781 21.52147...
23 Mumbai 19.076000 72.877700 S POLYGON ((72.87770 21.33421, 72.79362 21.33282...
24 Visakhapatnam 17.686800 83.218500 S POLYGON ((83.21850 19.94536, 83.13518 19.94397...
25 Hyderabad 17.385000 78.486700 S POLYGON ((78.48670 19.64363, 78.40353 19.64224...
26 Machilipatnam 16.190500 81.136200 S POLYGON ((81.13620 18.44941, 81.05363 18.44802...
27 Panaji 15.490900 73.827800 S POLYGON ((73.82780 17.74996, 73.74555 17.74857...
28 Sriharikota 13.725900 80.226600 S POLYGON ((80.22660 15.98533, 80.14511 15.98394...
29 Chennai 13.082700 80.270700 S POLYGON ((80.27070 15.34225, 80.18947 15.34086...
30 Karaikal 10.925400 79.838000 S POLYGON ((79.83800 13.18533, 79.75754 13.18394...
31 Kochi 9.931200 76.267300 S POLYGON ((76.26730 12.19129, 76.18715 12.18990...
32 Thiruvananthapuram 8.524100 76.936600 C POLYGON ((76.93660 10.78438, 76.85684 10.78300... 
points = df.hvplot.points(x='Longitude', y='Latitude', geo=True, color='Band',
                          alpha=0.7, coastline = True,
                 xlim=(df.Longitude.min()-5, df.Longitude.max()+3),
                 ylim=(df.Latitude.min()-3, df.Latitude.max()+3),
                 tiles='OpenTopoMap', frame_height=800, frame_width=650, hover_cols=['Site', 'Band'], value_label='Count')

# Create the circle plot
circles = gv.Polygons(data=gdf.geometry,).opts(color = "gray", fill_alpha=0.2, xlabel = "LongitudeËšE", ylabel = "LatitudeËšN",
                                               frame_height=800, frame_width=650)
# Overlay the circle plot on top of the point plot
plot = points * circles
# Show the plot
display(plot)

import urllib.request
url = "https://raw.githubusercontent.com/syedhamidali/test_scripts/master/map_features.py"
urllib.request.urlretrieve(url, "map_features.py")
import map_features as mf

!git clone https://github.com/aman1chaudhary/India-Shapefiles.git

fatal: destination path 'India-Shapefiles' already exists and is not an empty directory.

india = gpd.read_file("India-Shapefiles/India Boundary/")
states = gpd.read_file("India-Shapefiles/India States Boundary/")

fig = plt.figure(figsize = [10,12], dpi=300)
ax = plt.axes(projection=ccrs.PlateCarree(), frameon=False)
BAND = ["X", "C", "S"]
col = ['red', '#4B04E2', '#58D68D']

# Count occurrences of each 'Band' value
band_counts = df['Band'].value_counts().to_dict()

for band, c in zip(BAND, col):
    # Get the count for the current 'Band' value
    count = band_counts[band]
    # Create the label for the legend
    label = f"{band} - Band ({count})"
    df[df['Band']==band].plot.scatter(x='Longitude', y='Latitude', ax=ax, c=c, s=10, label=label, zorder=10)

    ax.add_geometries(gdf[gdf.Band == band].geometry, crs=ccrs.PlateCarree(), 
                      alpha=0.4, edgecolor="k", facecolor=c) 
    
# Add text labels to each point
for i, txt in enumerate(df['Site']):
    x = df['Longitude'][i]
    y = df['Latitude'][i]
    if txt == "Delhi":
        y -= 0.5
    dx = 0.01 * (max(df['Longitude']) - min(df['Longitude']))
    dy = 0.01 * (max(df['Latitude']) - min(df['Latitude']))
    ax.text(x + dx, y + dy, txt, fontsize=8)
india.plot(ax=ax, ec = "k", fc = "none", lw=0.5, alpha = 0.6, )
states.plot(ax=ax, ec ="k", fc = "none", lw=0.2, alpha = 0.5, ls=":")
ax.legend(title = f"Total DWRs: {counts.sum()[0]}", shadow = True)
mf.map_features(ax=ax, ocean=True, borders=False, states=False, land=True)
ax.minorticks_on()
ax.tick_params(axis='both', which='major', labelsize=12, width=0.5, color='#555555', length=8, direction='out')
ax.tick_params(axis='both', which='minor', labelsize=10, width=0.5, color='#555555', length=4, direction='out')
ax.set_xticks(np.arange(df.Longitude.min(), df.Longitude.max()+1, 5))
ax.set_yticks(np.arange(df.Latitude.min(), df.Latitude.max()+1, 5))
ax.set_xlabel("LongitudeËšE")
ax.set_ylabel("LatitudeËšN")
ax.set_extent([65, 98, 5, 37])
ax.set_autoscale_on(True)
plt.show()
_images/interactive_dwrs_12_0.png 
import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import matplotlib.image as mpimg
import urllib.request
from PIL import Image
import io

url = "https://mausam.imd.gov.in/imd_latest/contents/map-marker-icon-png-green.png"

# Open the URL and read the image data into a bytes object
with urllib.request.urlopen(url) as response:
    img_data = response.read()

# Create a PIL Image object from the image data
ma_img = Image.open(io.BytesIO(img_data))
# Convert the PIL Image to a NumPy array
marker_img = np.array(ma_img)

# Create a function to create an OffsetImage object for each marker
def make_marker(lon, lat):
    # Set the size of the marker image
    size = 0.05

    # Convert the coordinates to the map's coordinate system
    x, y = ax.projection.transform_point(lon, lat, ccrs.PlateCarree())[:2]

    # Create the OffsetImage object
    img = OffsetImage(marker_img, zoom=size)
    img.image.axes = ax
    ab = AnnotationBbox(img, (x,y), frameon=False)
    ax.add_artist(ab)

    
BAND = ["X", "C", "S"]
col = ['red', '#4B04E2', '#04E2D8']

# Create the map figure
fig = plt.figure(figsize=[10, 12], dpi=300)
ax = plt.axes(projection=ccrs.PlateCarree(), frameon=False)

# Plot the data points
for band, c in zip(BAND, col):
    count = band_counts[band]
    label = f"{band} - Band ({count})"
    df[df['Band'] == band].plot.scatter(x='Longitude', y='Latitude', 
                                        ax=ax, c=c, s=10, label=label)
    ax.add_geometries(gdf[gdf.Band == band].geometry, crs=ccrs.PlateCarree(), 
                      alpha=0.4, edgecolor="k", facecolor=c)

# Add the custom marker to each data point
for i, row in df.iterrows():
    make_marker(row['Longitude'], row['Latitude'])

# Add text labels to each point
for i, txt in enumerate(df['Site']):
    x = df['Longitude'][i]
    y = df['Latitude'][i]
    if txt == "Delhi":
        y -= 0.5
    dx = 0.01 * (max(df['Longitude']) - min(df['Longitude']))
    dy = 0.01 * (max(df['Latitude']) - min(df['Latitude']))
    ax.text(x + dx, y + dy, txt, fontsize=8)

# Add the map features and labels
india.plot(ax=ax, ec="k", fc="none", lw=0.5, alpha=0.6)
states.plot(ax=ax, ec="k", fc="none", lw=0.2, alpha=0.5, ls=":")
mf.map_features(ax=ax, ocean=True, borders=False, states=False, land=True)
ax.minorticks_on()
ax.tick_params(axis='both', which='major', labelsize=12, width=0.5, 
               color='#555555', length=8, direction='out')
ax.tick_params(axis='both', which='minor', labelsize=10, width=0.5, 
               color='#555555', length=4, direction='out')
ax.set_xticks(np.arange(df.Longitude.min(), df.Longitude.max()+1, 5))
ax.set_yticks(np.arange(df.Latitude.min(), df.Latitude.max()+1, 5))
ax.set_xlabel("LongitudeËšE")
ax.set_ylabel("LatitudeËšN")
ax.set_extent([65, 98, 5, 37])
ax.set_autoscale_on(True)

# Show the legend and the plot
ax.legend(title=f"Total DWRs: {counts.sum()[0]}", shadow=True)
plt.show()
_images/interactive_dwrs_13_0.png 
import ipyleaflet as ipyl
# Create the polygon layer
gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.Longitude, df.Latitude))
polygons_layer = ipyl.GeoJSON(
    data=gdf.__geo_interface__,
    style={'color': 'gray', 'fillOpacity': 0.2})

import folium
from folium.plugins import MarkerCluster

# Set xlim and ylim
xlim = (df.Longitude.min() - 5, df.Longitude.max() + 3)
ylim = (df.Latitude.min() - 3, df.Latitude.max() + 3)

# Create the folium map object
m = folium.Map(location=[df['Latitude'].mean(), df['Longitude'].mean()], zoom_start=5, tiles='openstreetmap',xlim=xlim, ylim=ylim)

# Add markers to the map
marker_cluster = MarkerCluster().add_to(m)
for idx, row in df.iterrows():
    folium.Marker([row['Latitude'], row['Longitude']], 
                  popup=f"Site: {row['Site']}, Band: {row['Band']}", 
                  icon=folium.Icon(color=row['Band'])).add_to(marker_cluster)

# Add circles to the map
for idx, row in gdf.iterrows():
    if row['Band'] == 'X':
        radius = 100e3
    else:
        radius = 250e3
    folium.Circle(location=[row['geometry'].y, row['geometry'].x],
                  radius=radius,
                  fill=True,
                  fill_opacity=0.2,
                  color='gray').add_to(m)
# Display the map
m
Make this Notebook Trusted to load map: File -> Trust Notebook