Quicklooks¶

Introduction:¶

In addition to data processing and wind retrieval, lidarwind also has a basic visualisation module. This example shows how to use this module to quickly check the data and the retrieved wind profiles. This example follows the steps listed below.

Steps:¶

Download pre-processed data from zenodo
Read the pre-processed and merge all data
Visualising variables from vertically pointing observations
Preparing the data for wind retrieval
Visualising radial observations for each azimuth
Applying the FFT-based method to retrieve wind
Visualising wind speed and direction

In [1]:

import pooch
import pandas as pd
import matplotlib as mpl 
import lidarwind as lst

In [2]:

mpl = lst.PlotSettings(mpl, style='dark_background').update_settings().mpl

Step 0: Download pre-processed data from zenodo¶

In [3]:

def sample_downloader(date):

    formated_date = date.strftime("%Y%m%d_%H")
    file_path = pooch.retrieve( 
        url=f"doi:10.5281/zenodo.7404576/cmtrace_windcube_{formated_date}_fixed_50m.zip",
        known_hash=None, processor=pooch.Unzip())
    
    return file_path[0]

In [4]:

date_range = pd.date_range(start='20210921 01', end='20210921 23', freq='H')

file_list = []
for date in date_range:
    file_list.append(sample_downloader(date))

Step 1: Read the pre-processed and merge all data¶

In [5]:

fileList = sorted(file_list)
merged_data = lst.ReadProcessedData(fileList).merge_data()

Step 2: Visualising variables from vertically pointing observations¶

In [6]:

lst.Visualizer(merged_data).view_orig_var('doppler_spectrum_width90', vmin=0.6, vmax=1.5, 
                                       cmap='turbo', show=True)

lst.Visualizer(merged_data).view_orig_var('radial_wind_speed90', vmin=-2, vmax=0.5, show=True)

lst.Visualizer(merged_data).view_orig_var('cnr90', vmin=-30, vmax=-5, cmap='turbo', show=True)

Step 3: Preparing the data for wind retrieval¶

In [7]:

transfd_data = lst.GetRestructuredData(merged_data)

Step 4: Visualising radial observations for each azimuth¶

In [8]:

lst.Visualizer(transfd_data.data_transf).view_orig_var('radial_wind', vmin=-7, vmax=7, 
                                                   plot_id='rad_wind_speed_panel', show=True)

Step 5: Applying the FFT-based method to retrieve wind¶

In [9]:

wind_prop = lst.FourierTransfWindMethod(transfd_data.data_transf).wind_prop()

/opt/miniconda3/envs/lidarSuit/lib/python3.8/site-packages/xrft/xrft.py:338: FutureWarning: Flags true_phase and true_amplitude will be set to True in future versions of xrft.dft to preserve the theoretical phasing and amplitude of Fourier Transform. Consider using xrft.fft to ensure future compatibility with numpy.fft like behavior and to deactivate this warning.
  warnings.warn(msg, FutureWarning)

Step 6: Visualising wind speed and direction¶

In [10]:

lst.Visualizer(wind_prop).view_ret_var('horizontal_wind_direction', vmin=0, vmax=360, 
                                    cmap='hsv', elv=wind_prop.elv.values, show=True)

lst.Visualizer(wind_prop).view_ret_var('horizontal_wind_speed', vmin=0, vmax=20, 
                                    cmap='turbo', elv=wind_prop.elv.values, show=True)

In [ ]: