Cloud Condensation Nuclei Plots

import act
import matplotlib.pyplot as plt

username = 'mallain'
token = '10fd692feea71fb1'
datastream = 'bnfaosccn2colaspectraM1.b1'
startdate = '2025-05-08'
enddate = '2025-05-11T23:59:59'

# Download and read the data
result_ccn = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)
ds_ccn = act.io.read_arm_netcdf(result_ccn)
ds_ccn.clean.cleanup()

if 'lat' not in ds_ccn.coords:
    ds_ccn = ds_ccn.set_coords(['lat', 'lon'])

print("Variables:", list(ds_ccn.data_vars))

ccn_var = 'concentration'

# Plot
disp = act.plotting.TimeSeriesDisplay(ds_ccn, figsize=(12, 8))
disp.plot(ccn_var, label='CCN Concentration [#/cm³]')
disp.axes[0].set_title('Cloud Condensation Nuclei (CCN) Number Concentration')
disp.axes[0].set_ylabel('Supersaturation (%)')
disp.axes[0].set_xlabel('Time (UTC)')
disp.day_night_background()
disp.axes[0].legend()

plt.show()

import act
import matplotlib.pyplot as plt
import numpy as np

# Set your username and token here
username = 'mallain'
token = '10fd692feea71fb1'

#Set the datastream and start/enddates
datastream = 'bnfaosccn2colaspectraM1.b1'
startdate = '2025-05-08'
enddate = '2025-05-11T23:59:59'

# Download and read data
files = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)
ds = act.io.read_arm_netcdf(files)
ds.clean.cleanup()

#Get the list of supersaturation levels and time values
supersat = ds['supersaturation_setpoint'].values  # Y-axis grouping
times = ds['time'].values

#Print all variables in the dataset
print("📋 Variables in this dataset:")
print(list(ds.data_vars))

#Get supersaturation levels and time
supersat = ds['supersaturation_setpoint'].values
times = ds['time'].values

#Create the plot
fig, ax = plt.subplots(figsize=(12, 5))
colors = plt.cm.viridis(np.linspace(0, 1, len(supersat)))

#Plot one trace per supersaturation level
for i, ss in enumerate(supersat):
    ccn = ds['concentration'].sel(supersaturation_setpoint=ss)
    valid = ~ccn.isnull()
    ax.scatter(times[valid], ccn.values[valid], s=10, color=colors[i], label=f'SS = {ss:.2f}')

#Plot makeup
ax.set_title('CCN Number Concentration vs Time at Different Supersaturations')
ax.set_xlabel('Time (UTC)')
ax.set_ylabel('CCN Concentration [#/cm³]')
ax.set_yscale('log')
ax.legend(title='Supersaturation (%)')
ax.grid(True)
plt.tight_layout()
plt.show()

import act
import matplotlib.pyplot as plt
import pandas as pd

# Set your username and token here!
username = 'mallain'
token = '10fd692feea71fb1'

# Set the datastream and start/enddates
datastream = 'bnfwbpluvio2M1.a1'
startdate = '2025-05-08'
enddate = '2025-05-11T23:59:59'

# Use ACT to easily download the data.  Watch for the data citation!  Show some support
# for ARM's instrument experts and cite their data if you use it in a publication
result_rain = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)

# Let's read in the data using ACT and check out the data
ds_rain = act.io.read_arm_netcdf(result_rain)
ds_rain.clean.cleanup()

#printing output label
print("Available rain-related variables:")
#printing list of variables
print(list(ds_rain.data_vars))
#saving rain variables
rain_rate_var = 'intensity_rt'
#giving a shortcut
ds = ds_rain

#downloading and reading rain data
result_rain = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)
ds_rain = act.io.read_arm_netcdf(result_rain)
#cleaning up data (QC control)
ds_rain.clean.cleanup()

#store data
rain_rate_var = 'intensity_rt'
df_rain = ds_rain[rain_rate_var].to_dataframe().dropna()

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=df_rain.index,
    y=df_rain[rain_rate_var],
    mode='lines+markers',
    name='Rain Rate [mm/hr]',
    line=dict(color='blue')
))

fig.update_layout(
    title='Rain Rate (Pluvio2)',
    xaxis_title='Time (UTC)',
    yaxis_title='Rain Rate [mm/hr]',
    hovermode='x unified',
    template='plotly_white',
    height=500,
)

fig.show()