From e9af16a051740e86178472cef6cd70e2f3fc9e49 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 4 Dec 2023 17:30:18 -0600 Subject: [PATCH 01/54] ADD: _echo_class_wt.py: RadarEchoClass module with all methods/code from PyREClass. --- pyart/retrieve/_echo_class_wt.py | 319 +++++++++++++++++++++++++++++++ 1 file changed, 319 insertions(+) create mode 100644 pyart/retrieve/_echo_class_wt.py diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py new file mode 100644 index 0000000000..f648b570cf --- /dev/null +++ b/pyart/retrieve/_echo_class_wt.py @@ -0,0 +1,319 @@ +""" +Classification of Precipitation Echoes in Radar Data. + +Created on Thu Oct 12 23:12:19 2017 +@author: Bhupendra Raut +@modifed: 02/13/2020 +@references: 10.1109/TGRS.2020.2965649 + +.. autosummary:: + getWTClass + labelClasses + reflectivity_to_rainrate + getScaleBreak + getWTSum + atwt2d +""" + +import numpy as np +from numpy import log, floor +import sys + + +def getWTClass(grid, conv_scale_km=20, refl_field="reflectivity_horizontal"): + """ + Compute ATWT described as Raut et al (2008) and classify radar echoes + using scheme of Raut et al (2020). + First, convert dBZ to rain rates using standard Z-R relationship or user given coefiecient. This is to + transform the normally distributed dBZ to gamma-like distribution, enhancing the structure of the field. + + Parameters: + =========== + dbz_data: ndarray + 3D array containing radar data. Last dimension should be levels. + res_km: float + Resolution of the radar data in km + conv_scale_km: float + Approximate scale break (in km) between convective and stratiform scales + + Returns: + ======== + wt_class: ndarray + Precipitation type classification: 0. N/A 1. stratiform/non-convective, + 2. convective cores and 3. moderate+transitional (mix) convective + regions. + """ + + # Extract grid data and get the resolution + dbz_data = grid.fields[refl_field]['data'] + # Warning: dx and dy are considred to be same (res_km). + res_km = (grid.x["data"][1] - grid.x["data"][0])/1000 + + try: + dbz_data = dbz_data.filled(0) # In case it's a masked array. + except Exception: + pass + + # save the mask for missing data. + dbz_data[np.isnan(dbz_data)] = 0 + dbz_data_t = reflectivity_to_rainrate(dbz_data) # transform the dbz data + + # get scale break in pixels + scale_break = getScaleBreak(res_km, conv_scale_km) + wt_sum = getWTSum(dbz_data_t, scale_break) + wt_class = labelClasses(wt_sum, dbz_data) + + wt_class = wt_class.squeeze() + return wt_class + + + + +def labelClasses(wt_sum, vol_data): + """ + Labels classes using given thresholds: + - 0. no precipitation, + - 1. stratiform, + - 2. intense/heavy convective, + - 3. transitional/intermediate regions. + + Following hard coded values are optimised and validated using C-band radars + over Darwin, Australia (2.5 km grid spacing) and tested for Solapur, India (1km grid spacing) [Raut et al. 2020]. + conv_wt_threshold = 5 # WT value more than this is strong convection + tran_wt_threshold = 2 # WT value for moderate convection + min_dbz_threshold = 10 # pixels below this value are not classified. + conv_dbz_threshold = 30 # pixel below this value are not convective. This works for most cases. + + Parameters: + =========== + wt_sum: ndarray + Integrated wavelet transform + vol_data: ndarray + Array, vector or matrix of data + + Returns: + ======== + wt_class: ndarray + Precipitation type classification. + """ + + conv_wt_threshold = 5 # WT value more than this is strong convection + tran_wt_threshold = 1 # WT value for moderate convection + min_dbz_threshold = 5 # pixels below this value are not classified + conv_dbz_threshold = 25 # pixel below this value are not convective + conv_core_threshold = 40 + + # I first used negative numbers to annotate the categories. Then multiply it by -1. + wt_class = np.where((wt_sum >= tran_wt_threshold) & (vol_data >= conv_core_threshold), -2, 0) + wt_class = np.where((wt_sum >= conv_wt_threshold) & (vol_data >= conv_dbz_threshold), -2, 0) + wt_class = np.where((wt_sum < conv_wt_threshold) & (wt_sum >= tran_wt_threshold) + & (vol_data >= conv_dbz_threshold), -3, wt_class) + wt_class = np.where((wt_class == 0) & (vol_data >= min_dbz_threshold), -1, wt_class) + + wt_class = -1 * wt_class + wt_class = np.where((wt_class == 0), np.nan, wt_class) + + return wt_class.astype(np.int32) + + + +def reflectivity_to_rainrate(dbz, acoeff=200, bcoeff=1.6): + """ + Uses standard values for ZRA=200 and ZRB=1.6. + + Parameters: + =========== + dbz: ndarray + Array, vector or matrix of reflectivity in dBZ. + acoeff: float + Z = a*R^b a coefficient. + bcoeff: float + Z = a*R^b b coefficient. + + Returns: + ======== + rr: ndarray + Rain rate in (mm/h) + """ + rr = ((10.0 ** (dbz / 10.0)) / acoeff) ** (1.0 / bcoeff) + print("rain rate max "+ str(rr.max())) + return rr + + + +def getScaleBreak(res_km, conv_scale_km): + """ + Compute scale break for convection and stratiform regions. WT will be + computed upto this scale and features will be designated as convection. + + Parameters: + =========== + res_km: float + resolution of the image. + conv_scale_km: float + expected size of spatial variations due to convection. + + Returns: + ======== + dyadic: int + integer scale break in pixels. + """ + scale_break = log((conv_scale_km / res_km)) / log(2) + 1 + return int(round(scale_break)) + + + +def getWTSum(vol_data, conv_scale): + """ + Returns sum of WT upto given scale. Works with both 2d scans and + volumetric data. + + Parameters: + =========== + vol_data: ndarray + Array, vector or matrix of data. + conv_scale: float + Expected size of spatial variations due to convection. + + Returns: + ======== + wt_sum: ndarray + Integrated wavelet transform. + """ + dims = vol_data.shape + + # if data is 2d + # if len(dims) == 2 or any(dims==1): + wt, bg = atwt2d(vol_data, max_scale=conv_scale) + wt_sum = np.sum(wt, axis=(0)) + """ else: # else for volume data + num_levels = min(dims) # too many assumptions here for height of the volume. + wt_sum = np.zeros(dims) + + for lev in range(num_levels): + if vol_data[:, :, lev].max < 1: + next() # this needs reviewing + wt = atwt2d(vol_data[lev, :, :], max_scale=conv_scale) + + # sum all the WT scales. + wt_sum[lev, :, :] = np.sum(wt, axis=(0)) """ + + # Only positive WT corresponds to convection in radar + # wt_sum[wt_sum<0] = 0 + return wt_sum + + +def atwt2d(data2d, max_scale=-1): + """ + Computes a trous wavelet transform (ATWT). Computes ATWT of the 2d array + up to max_scale. If max_scale is outside the boundaries, number of scales + will be reduced. + + Data is mirrored at the boundaries. 'Negative WT are removed. Not tested + for non-square data. + + @authors: Bhupendra A. Raut and Dileep M. Puranik + @references: Press et al. (1992) Numerical Recipes in C. + + Parameters: + =========== + data2d: ndarray + 2D image as array or matrix. + max_scale: + Computes wavelets up to max_scale. Leave blank for maximum possible + scales. + + Returns: + =========== + tuple of ndarray + ATWT of input image and the final smoothed image or background image. + """ + + if not isinstance(data2d, np.ndarray): + sys.exit("the input is not a numpy array") + + data2d = data2d.squeeze() + + dims = data2d.shape + min_dims = np.min(dims) + max_possible_scales = int(floor(log(min_dims) / log(2))) + + if max_scale < 0 or max_possible_scales <= max_scale: + max_scale = max_possible_scales - 1 + + ny = dims[0] + nx = dims[1] + + # For saving wt components + wt = np.zeros((max_scale, ny, nx)) + + temp1 = np.zeros(dims) + temp2 = np.zeros(dims) + + sf = (0.0625, 0.25, 0.375) # scaling function + + # start wavelet loop + for scale in range(1, max_scale + 1): + # print(scale) + x1 = 2 ** (scale - 1) + x2 = 2 * x1 + + # Row-wise smoothing + for i in range(0, nx): + # find the indices for prev and next points on the line + prev2 = abs(i - x2) + prev1 = abs(i - x1) + next1 = i + x1 + next2 = i + x2 + + # If these indices are outside the image, "mirror" them + # Sometime this causes issues at higher scales. + if next1 > nx - 1: + next1 = 2 * (nx - 1) - next1 + + if next2 > nx - 1: + next2 = 2 * (nx - 1) - next2 + + if prev1 < 0 or prev2 < 0: + prev1 = next1 + prev2 = next2 + + for j in range(0, ny): + left2 = data2d[j, prev2] + left1 = data2d[j, prev1] + right1 = data2d[j, next1] + right2 = data2d[j, next2] + temp1[j, i] = (sf[0] * (left2 + right2) + + sf[1] * (left1 + right1) + + sf[2] * data2d[j, i]) + + # Column-wise smoothing + for i in range(0, ny): + prev2 = abs(i - x2) + prev1 = abs(i - x1) + next1 = i + x1 + next2 = i + x2 + + # If these indices are outside the image use next values + if next1 > ny - 1: + next1 = 2 * (ny - 1) - next1 + if next2 > ny - 1: + next2 = 2 * (ny - 1) - next2 + if prev1 < 0 or prev2 < 0: + prev1 = next1 + prev2 = next2 + + for j in range(0, nx): + top2 = temp1[prev2, j] + top1 = temp1[prev1, j] + bottom1 = temp1[next1, j] + bottom2 = temp1[next2, j] + temp2[i, j] = (sf[0] * (top2 + bottom2) + + sf[1] * (top1 + bottom1) + + sf[2] * temp1[i, j]) + + wt[scale - 1, :, :] = data2d - temp2 + data2d[:] = temp2 + + + return wt, data2d \ No newline at end of file From 4cb9a987b4d54f95938dd8a2f2e52b97a1b7a755 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 4 Dec 2023 17:35:42 -0600 Subject: [PATCH 02/54] ADD:wrapper function for wt_reclass --- pyart/retrieve/echo_class.py | 42 ++++++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 7a15fa448c..8226019253 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -9,6 +9,7 @@ from ..config import get_field_name, get_fillvalue, get_metadata from ._echo_class import _feature_detection, steiner_class_buff +from ._echo_class_wt import getWTClass def steiner_conv_strat( @@ -978,3 +979,44 @@ def get_freq_band(freq): warn("Unknown frequency band") return None + + +def conv_strat_mywt(grid, conv_scale_km=20, refl_field="reflectivity"): + """ + This function classifies radar echoes using the ATWT algorithm. + + Parameters + ---------- + grid : Grid + Grid object containing radar data. + conv_scale_km : float + Approximate scale break (in km) between convective and stratiform scales. + Default is = 20. + refl_field : str + Field name for reflectivity data in the pyart grid object. + Default is "reflectivity_horizontal". + + Returns + ------- + ndarray + Precipitation type classification. + Copy the other docs string + """ + + # Call the actual getWTClass function with provided arguments + reclass = getWTClass(grid, conv_scale_km, refl_field) + reclass = np.expand_dims(reclass, axis=0) + + # put data into a dictionary to be added as a field + reclass_dict = { + "wt_reclass": { + "data": reclass, + "standard_name": "echo_class", + "long_name": "Wavelet transform radar echo class", + "valid_min": 0, + "valid_max": 3, + "comment_1": ('0 = Undefined'), + } + } + + return(reclass_dict) From 75a7cbce93e04e50f0bf4528b3036331b4da1668 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 4 Dec 2023 18:36:30 -0600 Subject: [PATCH 03/54] REFORMAT: Function names comply with PyART & PEP8 standards. --- pyart/retrieve/__init__.py | 1 + pyart/retrieve/_echo_class_wt.py | 35 ++++++++++++++++---------------- pyart/retrieve/echo_class.py | 8 ++++---- 3 files changed, 23 insertions(+), 21 deletions(-) diff --git a/pyart/retrieve/__init__.py b/pyart/retrieve/__init__.py index 3b79ba5391..bd2354c19e 100644 --- a/pyart/retrieve/__init__.py +++ b/pyart/retrieve/__init__.py @@ -10,6 +10,7 @@ from .echo_class import get_freq_band # noqa from .echo_class import hydroclass_semisupervised # noqa from .echo_class import steiner_conv_strat # noqa +from .echo_class import conv_strat_raut from .gate_id import fetch_radar_time_profile, map_profile_to_gates # noqa from .kdp_proc import kdp_maesaka, kdp_schneebeli, kdp_vulpiani # noqa from .qpe import est_rain_rate_a # noqa diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index f648b570cf..ee4cb23374 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -8,10 +8,10 @@ .. autosummary:: getWTClass - labelClasses + label_classes reflectivity_to_rainrate - getScaleBreak - getWTSum + calc_scale_break + sum_conv_wavelets atwt2d """ @@ -20,7 +20,7 @@ import sys -def getWTClass(grid, conv_scale_km=20, refl_field="reflectivity_horizontal"): +def get_reclass(grid, conv_scale_km=20, refl_field="reflectivity_horizontal"): """ Compute ATWT described as Raut et al (2008) and classify radar echoes using scheme of Raut et al (2020). @@ -59,9 +59,9 @@ def getWTClass(grid, conv_scale_km=20, refl_field="reflectivity_horizontal"): dbz_data_t = reflectivity_to_rainrate(dbz_data) # transform the dbz data # get scale break in pixels - scale_break = getScaleBreak(res_km, conv_scale_km) - wt_sum = getWTSum(dbz_data_t, scale_break) - wt_class = labelClasses(wt_sum, dbz_data) + scale_break = calc_scale_break(res_km, conv_scale_km) + wt_sum = sum_conv_wavelets(dbz_data_t, scale_break) + wt_class = label_classes(wt_sum, dbz_data) wt_class = wt_class.squeeze() return wt_class @@ -69,7 +69,7 @@ def getWTClass(grid, conv_scale_km=20, refl_field="reflectivity_horizontal"): -def labelClasses(wt_sum, vol_data): +def label_classes(wt_sum, vol_data): """ Labels classes using given thresholds: - 0. no precipitation, @@ -83,7 +83,7 @@ def labelClasses(wt_sum, vol_data): tran_wt_threshold = 2 # WT value for moderate convection min_dbz_threshold = 10 # pixels below this value are not classified. conv_dbz_threshold = 30 # pixel below this value are not convective. This works for most cases. - +git push -u Parameters: =========== wt_sum: ndarray @@ -97,11 +97,13 @@ def labelClasses(wt_sum, vol_data): Precipitation type classification. """ - conv_wt_threshold = 5 # WT value more than this is strong convection - tran_wt_threshold = 1 # WT value for moderate convection - min_dbz_threshold = 5 # pixels below this value are not classified - conv_dbz_threshold = 25 # pixel below this value are not convective - conv_core_threshold = 40 + conv_wt_threshold = 5 # WT sum more than this is strong convection or convective cores (reccomended value 4-6). + conv_core_threshold = 42 # Reflectivity thrshold for covective cores (User Choice reccomended > 40 dBZ). + + tran_wt_threshold = 1.5 # WT value for moderate/intermediate convection (reccomended value 1-2) + min_dbz_threshold = 5 # Reflectivities below this value are not classified (reccomended value 0-15) + conv_dbz_threshold = 25 # pixel below this value are not convective (reccomended value 25-30 dBZ) + # I first used negative numbers to annotate the categories. Then multiply it by -1. wt_class = np.where((wt_sum >= tran_wt_threshold) & (vol_data >= conv_core_threshold), -2, 0) @@ -136,12 +138,11 @@ def reflectivity_to_rainrate(dbz, acoeff=200, bcoeff=1.6): Rain rate in (mm/h) """ rr = ((10.0 ** (dbz / 10.0)) / acoeff) ** (1.0 / bcoeff) - print("rain rate max "+ str(rr.max())) return rr -def getScaleBreak(res_km, conv_scale_km): +def calc_scale_break(res_km, conv_scale_km): """ Compute scale break for convection and stratiform regions. WT will be computed upto this scale and features will be designated as convection. @@ -163,7 +164,7 @@ def getScaleBreak(res_km, conv_scale_km): -def getWTSum(vol_data, conv_scale): +def sum_conv_wavelets(vol_data, conv_scale): """ Returns sum of WT upto given scale. Works with both 2d scans and volumetric data. diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 8226019253..737461a20e 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -9,7 +9,7 @@ from ..config import get_field_name, get_fillvalue, get_metadata from ._echo_class import _feature_detection, steiner_class_buff -from ._echo_class_wt import getWTClass +from ._echo_class_wt import get_reclass def steiner_conv_strat( @@ -981,7 +981,7 @@ def get_freq_band(freq): return None -def conv_strat_mywt(grid, conv_scale_km=20, refl_field="reflectivity"): +def conv_strat_raut(grid, conv_scale_km=20, refl_field="reflectivity"): """ This function classifies radar echoes using the ATWT algorithm. @@ -1003,8 +1003,8 @@ def conv_strat_mywt(grid, conv_scale_km=20, refl_field="reflectivity"): Copy the other docs string """ - # Call the actual getWTClass function with provided arguments - reclass = getWTClass(grid, conv_scale_km, refl_field) + # Call the actual get_relass function + reclass = get_relass(grid, conv_scale_km, refl_field) reclass = np.expand_dims(reclass, axis=0) # put data into a dictionary to be added as a field From 6ce7cc8c711256c1047ff6596e93896a66274ff0 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 4 Dec 2023 19:11:43 -0600 Subject: [PATCH 04/54] REFORMAT: Function names comply with PyART & PEP8 standards. --- pyart/retrieve/echo_class.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 737461a20e..f888ccb481 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -1004,15 +1004,15 @@ def conv_strat_raut(grid, conv_scale_km=20, refl_field="reflectivity"): """ # Call the actual get_relass function - reclass = get_relass(grid, conv_scale_km, refl_field) + reclass = get_reclass(grid, conv_scale_km, refl_field) reclass = np.expand_dims(reclass, axis=0) # put data into a dictionary to be added as a field reclass_dict = { "wt_reclass": { "data": reclass, - "standard_name": "echo_class", - "long_name": "Wavelet transform radar echo class", + "standard_name": "wavelet_echo_class", + "long_name": "Wavelet-based multiresolution radar echo classification", "valid_min": 0, "valid_max": 3, "comment_1": ('0 = Undefined'), From ad60ced6c4e321fe07ee811c63a7cfd17dff2d64 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 4 Dec 2023 19:13:23 -0600 Subject: [PATCH 05/54] Revert "REFORMAT: Function names comply with PyART & PEP8 standards." This reverts commit 6ce7cc8c711256c1047ff6596e93896a66274ff0. --- pyart/retrieve/echo_class.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index f888ccb481..737461a20e 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -1004,15 +1004,15 @@ def conv_strat_raut(grid, conv_scale_km=20, refl_field="reflectivity"): """ # Call the actual get_relass function - reclass = get_reclass(grid, conv_scale_km, refl_field) + reclass = get_relass(grid, conv_scale_km, refl_field) reclass = np.expand_dims(reclass, axis=0) # put data into a dictionary to be added as a field reclass_dict = { "wt_reclass": { "data": reclass, - "standard_name": "wavelet_echo_class", - "long_name": "Wavelet-based multiresolution radar echo classification", + "standard_name": "echo_class", + "long_name": "Wavelet transform radar echo class", "valid_min": 0, "valid_max": 3, "comment_1": ('0 = Undefined'), From cb5942287e741a2b90185401b6a69a8476a33bc0 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 4 Dec 2023 19:11:43 -0600 Subject: [PATCH 06/54] REFORMAT: Function names comply with PyART & PEP8 standards. --- pyart/retrieve/echo_class.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 737461a20e..f888ccb481 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -1004,15 +1004,15 @@ def conv_strat_raut(grid, conv_scale_km=20, refl_field="reflectivity"): """ # Call the actual get_relass function - reclass = get_relass(grid, conv_scale_km, refl_field) + reclass = get_reclass(grid, conv_scale_km, refl_field) reclass = np.expand_dims(reclass, axis=0) # put data into a dictionary to be added as a field reclass_dict = { "wt_reclass": { "data": reclass, - "standard_name": "echo_class", - "long_name": "Wavelet transform radar echo class", + "standard_name": "wavelet_echo_class", + "long_name": "Wavelet-based multiresolution radar echo classification", "valid_min": 0, "valid_max": 3, "comment_1": ('0 = Undefined'), From 8e727bf5d587a022e87d0ab0748d7b8bc31cb931 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Fri, 8 Dec 2023 03:05:16 -0600 Subject: [PATCH 07/54] ADD: masked array, detailed func arguments --- pyart/retrieve/_echo_class_wt.py | 23 ++++++++++++++--------- pyart/retrieve/echo_class.py | 10 +++++++++- 2 files changed, 23 insertions(+), 10 deletions(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index ee4cb23374..783a0515f3 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -44,8 +44,10 @@ def get_reclass(grid, conv_scale_km=20, refl_field="reflectivity_horizontal"): regions. """ - # Extract grid data and get the resolution + # Extract grid data, save mask and get the resolution dbz_data = grid.fields[refl_field]['data'] + radar_mask = np.ma.getmask(dbz_data) + # Warning: dx and dy are considred to be same (res_km). res_km = (grid.x["data"][1] - grid.x["data"][0])/1000 @@ -63,8 +65,11 @@ def get_reclass(grid, conv_scale_km=20, refl_field="reflectivity_horizontal"): wt_sum = sum_conv_wavelets(dbz_data_t, scale_break) wt_class = label_classes(wt_sum, dbz_data) - wt_class = wt_class.squeeze() - return wt_class + wt_class_ma = np.ma.masked_where(radar_mask, wt_class) # add mask back + wt_class_ma = wt_class_ma.squeeze() + + + return wt_class_ma @@ -72,10 +77,10 @@ def get_reclass(grid, conv_scale_km=20, refl_field="reflectivity_horizontal"): def label_classes(wt_sum, vol_data): """ Labels classes using given thresholds: - - 0. no precipitation, + - 0. no precipitation, marked by the given min_dbz threshold. - 1. stratiform, - - 2. intense/heavy convective, - - 3. transitional/intermediate regions. + - 2. transitional/intermediate regions, + - 3. intense/heavy convective. Following hard coded values are optimised and validated using C-band radars over Darwin, Australia (2.5 km grid spacing) and tested for Solapur, India (1km grid spacing) [Raut et al. 2020]. @@ -106,10 +111,10 @@ def label_classes(wt_sum, vol_data): # I first used negative numbers to annotate the categories. Then multiply it by -1. - wt_class = np.where((wt_sum >= tran_wt_threshold) & (vol_data >= conv_core_threshold), -2, 0) - wt_class = np.where((wt_sum >= conv_wt_threshold) & (vol_data >= conv_dbz_threshold), -2, 0) + wt_class = np.where((wt_sum >= tran_wt_threshold) & (vol_data >= conv_core_threshold), -3, 0) + wt_class = np.where((wt_sum >= conv_wt_threshold) & (vol_data >= conv_dbz_threshold), -3, 0) wt_class = np.where((wt_sum < conv_wt_threshold) & (wt_sum >= tran_wt_threshold) - & (vol_data >= conv_dbz_threshold), -3, wt_class) + & (vol_data >= conv_dbz_threshold), -2, wt_class) wt_class = np.where((wt_class == 0) & (vol_data >= min_dbz_threshold), -1, wt_class) wt_class = -1 * wt_class diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index f888ccb481..d1a6410f02 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -981,7 +981,15 @@ def get_freq_band(freq): return None -def conv_strat_raut(grid, conv_scale_km=20, refl_field="reflectivity"): +def conv_strat_raut(grid, + refl_field="reflectivity", + conv_wt_threshold=5, + tran_wt_threshold=1.5, + conv_scale_km=20, + min_dbz_threshold=5, + conv_dbz_threshold = 25, + conv_core_threshold=42): + """ This function classifies radar echoes using the ATWT algorithm. From 83970735f02ad0d25b9a20d108be4641a94b6ab9 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Fri, 8 Dec 2023 03:24:39 -0600 Subject: [PATCH 08/54] MOD:func user arguments used,class num changed. --- pyart/retrieve/_echo_class_wt.py | 47 +++++++++++++++++++++----------- pyart/retrieve/echo_class.py | 26 ++++++++++++++++-- 2 files changed, 54 insertions(+), 19 deletions(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index 783a0515f3..1bd2ec1fed 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -20,7 +20,16 @@ import sys -def get_reclass(grid, conv_scale_km=20, refl_field="reflectivity_horizontal"): +def get_reclass(grid, + refl_field, + zr_a, + zr_b, + conv_wt_threshold, + tran_wt_threshold, + conv_scale_km, + min_dbz_threshold, + conv_dbz_threshold, + conv_core_threshold): """ Compute ATWT described as Raut et al (2008) and classify radar echoes using scheme of Raut et al (2020). @@ -58,12 +67,19 @@ def get_reclass(grid, conv_scale_km=20, refl_field="reflectivity_horizontal"): # save the mask for missing data. dbz_data[np.isnan(dbz_data)] = 0 - dbz_data_t = reflectivity_to_rainrate(dbz_data) # transform the dbz data + dbz_data_t = reflectivity_to_rainrate(dbz_data, acoeff=zr_a, bcoeff=zr_b) # transform the dbz data # get scale break in pixels scale_break = calc_scale_break(res_km, conv_scale_km) wt_sum = sum_conv_wavelets(dbz_data_t, scale_break) - wt_class = label_classes(wt_sum, dbz_data) + + wt_class = label_classes(wt_sum, + dbz_data, + conv_wt_threshold, + tran_wt_threshold, + min_dbz_threshold, + conv_dbz_threshold, + conv_core_threshold) wt_class_ma = np.ma.masked_where(radar_mask, wt_class) # add mask back wt_class_ma = wt_class_ma.squeeze() @@ -74,7 +90,13 @@ def get_reclass(grid, conv_scale_km=20, refl_field="reflectivity_horizontal"): -def label_classes(wt_sum, vol_data): +def label_classes(wt_sum, + dbz_data, + conv_wt_threshold, + tran_wt_threshold, + min_dbz_threshold, + conv_dbz_threshold, + conv_core_threshold): """ Labels classes using given thresholds: - 0. no precipitation, marked by the given min_dbz threshold. @@ -101,21 +123,14 @@ def label_classes(wt_sum, vol_data): wt_class: ndarray Precipitation type classification. """ - - conv_wt_threshold = 5 # WT sum more than this is strong convection or convective cores (reccomended value 4-6). - conv_core_threshold = 42 # Reflectivity thrshold for covective cores (User Choice reccomended > 40 dBZ). - - tran_wt_threshold = 1.5 # WT value for moderate/intermediate convection (reccomended value 1-2) - min_dbz_threshold = 5 # Reflectivities below this value are not classified (reccomended value 0-15) - conv_dbz_threshold = 25 # pixel below this value are not convective (reccomended value 25-30 dBZ) # I first used negative numbers to annotate the categories. Then multiply it by -1. - wt_class = np.where((wt_sum >= tran_wt_threshold) & (vol_data >= conv_core_threshold), -3, 0) - wt_class = np.where((wt_sum >= conv_wt_threshold) & (vol_data >= conv_dbz_threshold), -3, 0) + wt_class = np.where((wt_sum >= tran_wt_threshold) & (dbz_data >= conv_core_threshold), -3, 0) + wt_class = np.where((wt_sum >= conv_wt_threshold) & (dbz_data >= conv_dbz_threshold), -3, 0) wt_class = np.where((wt_sum < conv_wt_threshold) & (wt_sum >= tran_wt_threshold) - & (vol_data >= conv_dbz_threshold), -2, wt_class) - wt_class = np.where((wt_class == 0) & (vol_data >= min_dbz_threshold), -1, wt_class) + & (dbz_data >= conv_dbz_threshold), -2, wt_class) + wt_class = np.where((wt_class == 0) & (dbz_data >= min_dbz_threshold), -1, wt_class) wt_class = -1 * wt_class wt_class = np.where((wt_class == 0), np.nan, wt_class) @@ -124,7 +139,7 @@ def label_classes(wt_sum, vol_data): -def reflectivity_to_rainrate(dbz, acoeff=200, bcoeff=1.6): +def reflectivity_to_rainrate(dbz, acoeff, bcoeff): """ Uses standard values for ZRA=200 and ZRB=1.6. diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index d1a6410f02..22fd85e41c 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -982,7 +982,9 @@ def get_freq_band(freq): def conv_strat_raut(grid, - refl_field="reflectivity", + refl_field, + zr_a=200, + zr_b=1.6, conv_wt_threshold=5, tran_wt_threshold=1.5, conv_scale_km=20, @@ -1004,6 +1006,14 @@ def conv_strat_raut(grid, Field name for reflectivity data in the pyart grid object. Default is "reflectivity_horizontal". + zr_a and zr_b are standard coeeficnets used for C-band radar. change it for the type of radar. + + conv_wt_threshold = 5 # WT sum more than this is strong convection or convective cores (reccomended value 4-6). + conv_core_threshold = 42 # Reflectivity thrshold for covective cores (User Choice reccomended > 40 dBZ). + + tran_wt_threshold = 1.5 # WT value for moderate/intermediate convection (reccomended value 1-2) + min_dbz_threshold = 5 # Reflectivities below this value are not classified (reccomended value 0-15) + conv_dbz_threshold = 25 # pixel below this value are not convective (reccomended value 25-30 dBZ) Returns ------- ndarray @@ -1011,8 +1021,18 @@ def conv_strat_raut(grid, Copy the other docs string """ - # Call the actual get_relass function - reclass = get_reclass(grid, conv_scale_km, refl_field) + # Call the actual get_relass function to obtain radar echo classificatino + reclass = get_reclass(grid, + refl_field, + zr_a, + zr_b, + conv_wt_threshold=conv_wt_threshold, + tran_wt_threshold=tran_wt_threshold, + conv_scale_km=conv_scale_km, + min_dbz_threshold=min_dbz_threshold, + conv_dbz_threshold=conv_dbz_threshold, + conv_core_threshold=conv_core_threshold) + reclass = np.expand_dims(reclass, axis=0) # put data into a dictionary to be added as a field From 8cbf8fc7dc66ef440c1844fdf796111e25e2263d Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Fri, 8 Dec 2023 04:03:09 -0600 Subject: [PATCH 09/54] DOC:function and user arguments. --- pyart/retrieve/echo_class.py | 87 +++++++++++++++++++++++------------- 1 file changed, 57 insertions(+), 30 deletions(-) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 22fd85e41c..748d3ccd46 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -980,45 +980,72 @@ def get_freq_band(freq): return None - -def conv_strat_raut(grid, - refl_field, - zr_a=200, - zr_b=1.6, - conv_wt_threshold=5, - tran_wt_threshold=1.5, - conv_scale_km=20, - min_dbz_threshold=5, - conv_dbz_threshold = 25, - conv_core_threshold=42): - +def conv_strat_raut(grid, refl_field, zr_a=200, zr_b=1.6, + conv_wt_threshold=5, tran_wt_threshold=1.5, + conv_scale_km=20, min_dbz_threshold=5, + conv_dbz_threshold=25, conv_core_threshold=42): """ - This function classifies radar echoes using the ATWT algorithm. + Classifies radar echoes into convective cores, mixed convection, and stratiform regions using the ATWT algorithm. + + This function applies the ATWT (A Trous Wavelet Transform) algorithm from Raut et al (2008) to classify + radar echoes using the scheme of Raut et al (2020). It differentiates between convective and stratiform precipitation, + identifying convective cores, moderate/intermediate mixed convection, and stratiform regions + based on wavelet transform and reflectivity thresholds. Parameters ---------- grid : Grid Grid object containing radar data. - conv_scale_km : float - Approximate scale break (in km) between convective and stratiform scales. - Default is = 20. refl_field : str - Field name for reflectivity data in the pyart grid object. - Default is "reflectivity_horizontal". + Field name for reflectivity data in the Py-ART grid object. + zr_a : float, optional + Coefficient 'a' in the Z-R relationship Z = a*R^b for reflectivity to rain rate conversion. + Default is 200. The algorithm is not sensitive to precise values of 'zr_a' and 'zr_b'; however, + they must be adjusted based on the type of radar used. + zr_b : float, optional + Coefficient 'b' in the Z-R relationship Z = a*R^b. Default is 1.6. + conv_wt_threshold : float, optional + Threshold for sum of small scale wavelet components to identify strong convection. + Default is 5. Recommended values are between 4 and 6. + tran_wt_threshold : float, optional + Threshold for sum of small scale wavelet components to identify moderate/intermediate mixed convection. + Default is 1.5. Recommended values are between 1 and 2. + conv_scale_km : float, optional + Approximate scale break (in km) between convective and stratiform scales. + Scale break may vary between 15 and 30 km over different regions and seasons; however, + the algorithm is not sensitive to small variations in the scale break. + Default is 20 km taken from Raut et al (2018). + min_dbz_threshold : float, optional + Minimum reflectivity threshold. Reflectivities below this value are not classified. + Default is 5 dBZ. This value must be greater than or equal to '0'. + conv_dbz_threshold : float, optional + Reflectivities below this threshold will not be considered to be classified as convective. Default is 25 dBZ. + Recommended values are between 25 and 30 dBZ. + conv_core_threshold : float, optional + Reflectivity threshold to identify convective cores. Default is 42 dBZ. + Recommended value must be is greater than or equal to 40 dBZ. The algorithm is not sensitive to this value. - zr_a and zr_b are standard coeeficnets used for C-band radar. change it for the type of radar. - - conv_wt_threshold = 5 # WT sum more than this is strong convection or convective cores (reccomended value 4-6). - conv_core_threshold = 42 # Reflectivity thrshold for covective cores (User Choice reccomended > 40 dBZ). - - tran_wt_threshold = 1.5 # WT value for moderate/intermediate convection (reccomended value 1-2) - min_dbz_threshold = 5 # Reflectivities below this value are not classified (reccomended value 0-15) - conv_dbz_threshold = 25 # pixel below this value are not convective (reccomended value 25-30 dBZ) Returns - ------- - ndarray - Precipitation type classification. - Copy the other docs string +------- + dict + A dictionary structured as a Py-ART grid field, suitable for adding to a Py-ART Grid object. The dictionary + contains the classification data and associated metadata. The classification categories are as follows: + - 0: No precipitation or unclassified + - 1: Stratiform/non-convective + - 2: Convective cores + - 3: Moderate/Transitional convective regions + + References + ---------- + Raut, B. A., Karekar, R. N., & Puranik, D. M. (2008). Wavelet-based technique to extract convective clouds from + infrared satellite images. IEEE Geoscience and remote sensing letters, 5(3), 328-330. + + Raut, B. A., Seed, A. W., Reeder, M. J., & Jakob, C. (2018). A multiplicative cascade model for high‐resolution + space‐time downscaling of rainfall. Journal of Geophysical Research: Atmospheres, 123(4), 2050-2067. + + Raut, B. A., Louf, V., Gayatri, K., Murugavel, P., Konwar, M., & Prabhakaran, T. (2020). A multiresolution technique + for the classification of precipitation echoes in radar data. IEEE Transactions on Geoscience and Remote Sensing, + 58(8), 5409-5415. """ # Call the actual get_relass function to obtain radar echo classificatino From e7b9b0c8f9b7b85c9461a8d2751b212e8ff97583 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Fri, 8 Dec 2023 04:06:47 -0600 Subject: [PATCH 10/54] MOD: added metadata to field dictinery. --- pyart/retrieve/echo_class.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 748d3ccd46..037bddcd27 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -1070,7 +1070,18 @@ def conv_strat_raut(grid, refl_field, zr_a=200, zr_b=1.6, "long_name": "Wavelet-based multiresolution radar echo classification", "valid_min": 0, "valid_max": 3, - "comment_1": ('0 = Undefined'), + "comment_1": '0 = Undefined', + "parameters": { + "refl_field": refl_field, + "zr_a": zr_a, + "zr_b": zr_b, + "conv_wt_threshold": conv_wt_threshold, + "tran_wt_threshold": tran_wt_threshold, + "conv_scale_km": conv_scale_km, + "min_dbz_threshold": min_dbz_threshold, + "conv_dbz_threshold": conv_dbz_threshold, + "conv_core_threshold": conv_core_threshold + } } } From fb64b64a15fecb858cae7bb22c7969983c6a493b Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Fri, 8 Dec 2023 12:17:15 -0600 Subject: [PATCH 11/54] order of classes changed --- pyart/retrieve/echo_class.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 037bddcd27..dd599387ea 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -980,6 +980,7 @@ def get_freq_band(freq): return None + def conv_strat_raut(grid, refl_field, zr_a=200, zr_b=1.6, conv_wt_threshold=5, tran_wt_threshold=1.5, conv_scale_km=20, min_dbz_threshold=5, @@ -1031,9 +1032,9 @@ def conv_strat_raut(grid, refl_field, zr_a=200, zr_b=1.6, A dictionary structured as a Py-ART grid field, suitable for adding to a Py-ART Grid object. The dictionary contains the classification data and associated metadata. The classification categories are as follows: - 0: No precipitation or unclassified - - 1: Stratiform/non-convective - - 2: Convective cores - - 3: Moderate/Transitional convective regions + - 1: Stratiform/non-convective regions + - 2: Transitional and mixed convective regions + - 3: Convective cores References ---------- From b58541e5b1f1999096c94c2038f8b8c51692aff0 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 11 Dec 2023 16:39:23 -0600 Subject: [PATCH 12/54] cappi level added;better description of calsses --- pyart/retrieve/_echo_class_wt.py | 9 +++++++-- pyart/retrieve/echo_class.py | 8 +++++--- 2 files changed, 12 insertions(+), 5 deletions(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index 1bd2ec1fed..835e6bf54a 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -21,7 +21,8 @@ def get_reclass(grid, - refl_field, + refl_field, + level, zr_a, zr_b, conv_wt_threshold, @@ -54,7 +55,11 @@ def get_reclass(grid, """ # Extract grid data, save mask and get the resolution - dbz_data = grid.fields[refl_field]['data'] + try: + dbz_data = grid.fields[refl_field]['data'][level, :, :] + except: + dbz_data = grid.fields[refl_field]['data'][:, :] + radar_mask = np.ma.getmask(dbz_data) # Warning: dx and dy are considred to be same (res_km). diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index dd599387ea..0c3ca4d56b 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -981,7 +981,7 @@ def get_freq_band(freq): return None -def conv_strat_raut(grid, refl_field, zr_a=200, zr_b=1.6, +def conv_strat_raut(grid, refl_field, cappi_level=0, zr_a=200, zr_b=1.6, conv_wt_threshold=5, tran_wt_threshold=1.5, conv_scale_km=20, min_dbz_threshold=5, conv_dbz_threshold=25, conv_core_threshold=42): @@ -1028,7 +1028,7 @@ def conv_strat_raut(grid, refl_field, zr_a=200, zr_b=1.6, Returns ------- - dict + dict: A dictionary structured as a Py-ART grid field, suitable for adding to a Py-ART Grid object. The dictionary contains the classification data and associated metadata. The classification categories are as follows: - 0: No precipitation or unclassified @@ -1052,6 +1052,7 @@ def conv_strat_raut(grid, refl_field, zr_a=200, zr_b=1.6, # Call the actual get_relass function to obtain radar echo classificatino reclass = get_reclass(grid, refl_field, + cappi_level, zr_a, zr_b, conv_wt_threshold=conv_wt_threshold, @@ -1071,9 +1072,10 @@ def conv_strat_raut(grid, refl_field, zr_a=200, zr_b=1.6, "long_name": "Wavelet-based multiresolution radar echo classification", "valid_min": 0, "valid_max": 3, - "comment_1": '0 = Undefined', + "classification_description": "0: No precipitation or unclassified, 1: Stratiform/non-convective, 2: Mixed intermediate convection, 3: Convective cores", "parameters": { "refl_field": refl_field, + "cappi_level": cappi_level, "zr_a": zr_a, "zr_b": zr_b, "conv_wt_threshold": conv_wt_threshold, From 368fc6c3c62f8f242e4771ae78873524fd02e7fd Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 11 Dec 2023 18:02:54 -0600 Subject: [PATCH 13/54] REFORMAT: PEP8 style --- pyart/retrieve/_echo_class_wt.py | 183 +++++++++++++++++-------------- 1 file changed, 98 insertions(+), 85 deletions(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index 835e6bf54a..45858b558e 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -20,19 +20,21 @@ import sys -def get_reclass(grid, - refl_field, - level, - zr_a, - zr_b, - conv_wt_threshold, - tran_wt_threshold, - conv_scale_km, - min_dbz_threshold, - conv_dbz_threshold, - conv_core_threshold): +def get_reclass( + grid, + refl_field, + level, + zr_a, + zr_b, + conv_wt_threshold, + tran_wt_threshold, + conv_scale_km, + min_dbz_threshold, + conv_dbz_threshold, + conv_core_threshold, +): """ - Compute ATWT described as Raut et al (2008) and classify radar echoes + Compute ATWT described as Raut et al (2008) and classify radar echoes using scheme of Raut et al (2020). First, convert dBZ to rain rates using standard Z-R relationship or user given coefiecient. This is to transform the normally distributed dBZ to gamma-like distribution, enhancing the structure of the field. @@ -49,21 +51,21 @@ def get_reclass(grid, Returns: ======== wt_class: ndarray - Precipitation type classification: 0. N/A 1. stratiform/non-convective, - 2. convective cores and 3. moderate+transitional (mix) convective - regions. + Precipitation type classification: 0. N/A 1. stratiform/non-convective, + 2. convective cores and 3. moderate+transitional (mix) convective + regions. """ # Extract grid data, save mask and get the resolution try: - dbz_data = grid.fields[refl_field]['data'][level, :, :] + dbz_data = grid.fields[refl_field]["data"][level, :, :] except: - dbz_data = grid.fields[refl_field]['data'][:, :] + dbz_data = grid.fields[refl_field]["data"][:, :] radar_mask = np.ma.getmask(dbz_data) # Warning: dx and dy are considred to be same (res_km). - res_km = (grid.x["data"][1] - grid.x["data"][0])/1000 + res_km = (grid.x["data"][1] - grid.x["data"][0]) / 1000 try: dbz_data = dbz_data.filled(0) # In case it's a masked array. @@ -72,69 +74,80 @@ def get_reclass(grid, # save the mask for missing data. dbz_data[np.isnan(dbz_data)] = 0 - dbz_data_t = reflectivity_to_rainrate(dbz_data, acoeff=zr_a, bcoeff=zr_b) # transform the dbz data - + dbz_data_t = reflectivity_to_rainrate( + dbz_data, acoeff=zr_a, bcoeff=zr_b + ) # transform the dbz data + # get scale break in pixels scale_break = calc_scale_break(res_km, conv_scale_km) wt_sum = sum_conv_wavelets(dbz_data_t, scale_break) - wt_class = label_classes(wt_sum, - dbz_data, - conv_wt_threshold, - tran_wt_threshold, - min_dbz_threshold, - conv_dbz_threshold, - conv_core_threshold) - - wt_class_ma = np.ma.masked_where(radar_mask, wt_class) # add mask back + wt_class = label_classes( + wt_sum, + dbz_data, + conv_wt_threshold, + tran_wt_threshold, + min_dbz_threshold, + conv_dbz_threshold, + conv_core_threshold, + ) + + wt_class_ma = np.ma.masked_where(radar_mask, wt_class) # add mask back wt_class_ma = wt_class_ma.squeeze() - return wt_class_ma - - -def label_classes(wt_sum, - dbz_data, - conv_wt_threshold, - tran_wt_threshold, - min_dbz_threshold, - conv_dbz_threshold, - conv_core_threshold): - """ - Labels classes using given thresholds: - - 0. no precipitation, marked by the given min_dbz threshold. - - 1. stratiform, - - 2. transitional/intermediate regions, - - 3. intense/heavy convective. - - Following hard coded values are optimised and validated using C-band radars - over Darwin, Australia (2.5 km grid spacing) and tested for Solapur, India (1km grid spacing) [Raut et al. 2020]. - conv_wt_threshold = 5 # WT value more than this is strong convection - tran_wt_threshold = 2 # WT value for moderate convection - min_dbz_threshold = 10 # pixels below this value are not classified. - conv_dbz_threshold = 30 # pixel below this value are not convective. This works for most cases. -git push -u - Parameters: - =========== - wt_sum: ndarray - Integrated wavelet transform - vol_data: ndarray - Array, vector or matrix of data - - Returns: - ======== - wt_class: ndarray - Precipitation type classification. +def label_classes( + wt_sum, + dbz_data, + conv_wt_threshold, + tran_wt_threshold, + min_dbz_threshold, + conv_dbz_threshold, + conv_core_threshold, +): + """ + Labels classes using given thresholds: + - 0. no precipitation, marked by the given min_dbz threshold. + - 1. stratiform, + - 2. transitional/intermediate regions, + - 3. intense/heavy convective. + + Following hard coded values are optimised and validated using C-band radars + over Darwin, Australia (2.5 km grid spacing) and tested for Solapur, India (1km grid spacing) [Raut et al. 2020]. + conv_wt_threshold = 5 # WT value more than this is strong convection + tran_wt_threshold = 2 # WT value for moderate convection + min_dbz_threshold = 10 # pixels below this value are not classified. + conv_dbz_threshold = 30 # pixel below this value are not convective. This works for most cases. + git push -u + Parameters: + =========== + wt_sum: ndarray + Integrated wavelet transform + vol_data: ndarray + Array, vector or matrix of data + + Returns: + ======== + wt_class: ndarray + Precipitation type classification. """ - # I first used negative numbers to annotate the categories. Then multiply it by -1. - wt_class = np.where((wt_sum >= tran_wt_threshold) & (dbz_data >= conv_core_threshold), -3, 0) - wt_class = np.where((wt_sum >= conv_wt_threshold) & (dbz_data >= conv_dbz_threshold), -3, 0) - wt_class = np.where((wt_sum < conv_wt_threshold) & (wt_sum >= tran_wt_threshold) - & (dbz_data >= conv_dbz_threshold), -2, wt_class) + wt_class = np.where( + (wt_sum >= tran_wt_threshold) & (dbz_data >= conv_core_threshold), -3, 0 + ) + wt_class = np.where( + (wt_sum >= conv_wt_threshold) & (dbz_data >= conv_dbz_threshold), -3, 0 + ) + wt_class = np.where( + (wt_sum < conv_wt_threshold) + & (wt_sum >= tran_wt_threshold) + & (dbz_data >= conv_dbz_threshold), + -2, + wt_class, + ) wt_class = np.where((wt_class == 0) & (dbz_data >= min_dbz_threshold), -1, wt_class) wt_class = -1 * wt_class @@ -143,7 +156,6 @@ def label_classes(wt_sum, return wt_class.astype(np.int32) - def reflectivity_to_rainrate(dbz, acoeff, bcoeff): """ Uses standard values for ZRA=200 and ZRB=1.6. @@ -166,7 +178,6 @@ def reflectivity_to_rainrate(dbz, acoeff, bcoeff): return rr - def calc_scale_break(res_km, conv_scale_km): """ Compute scale break for convection and stratiform regions. WT will be @@ -188,7 +199,6 @@ def calc_scale_break(res_km, conv_scale_km): return int(round(scale_break)) - def sum_conv_wavelets(vol_data, conv_scale): """ Returns sum of WT upto given scale. Works with both 2d scans and @@ -225,7 +235,7 @@ def sum_conv_wavelets(vol_data, conv_scale): wt_sum[lev, :, :] = np.sum(wt, axis=(0)) """ # Only positive WT corresponds to convection in radar - # wt_sum[wt_sum<0] = 0 + # wt_sum[wt_sum<0] = 0 return wt_sum @@ -254,21 +264,21 @@ def atwt2d(data2d, max_scale=-1): tuple of ndarray ATWT of input image and the final smoothed image or background image. """ - + if not isinstance(data2d, np.ndarray): sys.exit("the input is not a numpy array") data2d = data2d.squeeze() - + dims = data2d.shape min_dims = np.min(dims) max_possible_scales = int(floor(log(min_dims) / log(2))) if max_scale < 0 or max_possible_scales <= max_scale: - max_scale = max_possible_scales - 1 + max_scale = max_possible_scales - 1 ny = dims[0] - nx = dims[1] + nx = dims[1] # For saving wt components wt = np.zeros((max_scale, ny, nx)) @@ -309,9 +319,11 @@ def atwt2d(data2d, max_scale=-1): left1 = data2d[j, prev1] right1 = data2d[j, next1] right2 = data2d[j, next2] - temp1[j, i] = (sf[0] * (left2 + right2) - + sf[1] * (left1 + right1) - + sf[2] * data2d[j, i]) + temp1[j, i] = ( + sf[0] * (left2 + right2) + + sf[1] * (left1 + right1) + + sf[2] * data2d[j, i] + ) # Column-wise smoothing for i in range(0, ny): @@ -334,12 +346,13 @@ def atwt2d(data2d, max_scale=-1): top1 = temp1[prev1, j] bottom1 = temp1[next1, j] bottom2 = temp1[next2, j] - temp2[i, j] = (sf[0] * (top2 + bottom2) - + sf[1] * (top1 + bottom1) - + sf[2] * temp1[i, j]) + temp2[i, j] = ( + sf[0] * (top2 + bottom2) + + sf[1] * (top1 + bottom1) + + sf[2] * temp1[i, j] + ) wt[scale - 1, :, :] = data2d - temp2 data2d[:] = temp2 - - return wt, data2d \ No newline at end of file + return wt, data2d From a02ed1065259dba6489322017bff57ee0e079556 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 11 Dec 2023 18:07:01 -0600 Subject: [PATCH 14/54] FORMAT:PEP8 --- pyart/retrieve/echo_class.py | 172 +++++++++++++++++++---------------- 1 file changed, 92 insertions(+), 80 deletions(-) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 0c3ca4d56b..28b59ff06f 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -981,87 +981,99 @@ def get_freq_band(freq): return None -def conv_strat_raut(grid, refl_field, cappi_level=0, zr_a=200, zr_b=1.6, - conv_wt_threshold=5, tran_wt_threshold=1.5, - conv_scale_km=20, min_dbz_threshold=5, - conv_dbz_threshold=25, conv_core_threshold=42): - """ - Classifies radar echoes into convective cores, mixed convection, and stratiform regions using the ATWT algorithm. - - This function applies the ATWT (A Trous Wavelet Transform) algorithm from Raut et al (2008) to classify - radar echoes using the scheme of Raut et al (2020). It differentiates between convective and stratiform precipitation, - identifying convective cores, moderate/intermediate mixed convection, and stratiform regions - based on wavelet transform and reflectivity thresholds. - - Parameters - ---------- - grid : Grid - Grid object containing radar data. - refl_field : str - Field name for reflectivity data in the Py-ART grid object. - zr_a : float, optional - Coefficient 'a' in the Z-R relationship Z = a*R^b for reflectivity to rain rate conversion. - Default is 200. The algorithm is not sensitive to precise values of 'zr_a' and 'zr_b'; however, - they must be adjusted based on the type of radar used. - zr_b : float, optional - Coefficient 'b' in the Z-R relationship Z = a*R^b. Default is 1.6. - conv_wt_threshold : float, optional - Threshold for sum of small scale wavelet components to identify strong convection. - Default is 5. Recommended values are between 4 and 6. - tran_wt_threshold : float, optional - Threshold for sum of small scale wavelet components to identify moderate/intermediate mixed convection. - Default is 1.5. Recommended values are between 1 and 2. - conv_scale_km : float, optional - Approximate scale break (in km) between convective and stratiform scales. - Scale break may vary between 15 and 30 km over different regions and seasons; however, - the algorithm is not sensitive to small variations in the scale break. - Default is 20 km taken from Raut et al (2018). - min_dbz_threshold : float, optional - Minimum reflectivity threshold. Reflectivities below this value are not classified. - Default is 5 dBZ. This value must be greater than or equal to '0'. - conv_dbz_threshold : float, optional - Reflectivities below this threshold will not be considered to be classified as convective. Default is 25 dBZ. - Recommended values are between 25 and 30 dBZ. - conv_core_threshold : float, optional - Reflectivity threshold to identify convective cores. Default is 42 dBZ. - Recommended value must be is greater than or equal to 40 dBZ. The algorithm is not sensitive to this value. - - Returns -------- - dict: - A dictionary structured as a Py-ART grid field, suitable for adding to a Py-ART Grid object. The dictionary - contains the classification data and associated metadata. The classification categories are as follows: - - 0: No precipitation or unclassified - - 1: Stratiform/non-convective regions - - 2: Transitional and mixed convective regions - - 3: Convective cores - References - ---------- - Raut, B. A., Karekar, R. N., & Puranik, D. M. (2008). Wavelet-based technique to extract convective clouds from - infrared satellite images. IEEE Geoscience and remote sensing letters, 5(3), 328-330. - - Raut, B. A., Seed, A. W., Reeder, M. J., & Jakob, C. (2018). A multiplicative cascade model for high‐resolution - space‐time downscaling of rainfall. Journal of Geophysical Research: Atmospheres, 123(4), 2050-2067. - - Raut, B. A., Louf, V., Gayatri, K., Murugavel, P., Konwar, M., & Prabhakaran, T. (2020). A multiresolution technique - for the classification of precipitation echoes in radar data. IEEE Transactions on Geoscience and Remote Sensing, - 58(8), 5409-5415. +def conv_strat_raut( + grid, + refl_field, + cappi_level=0, + zr_a=200, + zr_b=1.6, + conv_wt_threshold=5, + tran_wt_threshold=1.5, + conv_scale_km=20, + min_dbz_threshold=5, + conv_dbz_threshold=25, + conv_core_threshold=42, +): + """ + Classifies radar echoes into convective cores, mixed convection, and stratiform regions using the ATWT algorithm. + + This function applies the ATWT (A Trous Wavelet Transform) algorithm from Raut et al (2008) to classify + radar echoes using the scheme of Raut et al (2020). It differentiates between convective and stratiform precipitation, + identifying convective cores, moderate/intermediate mixed convection, and stratiform regions + based on wavelet transform and reflectivity thresholds. + + Parameters + ---------- + grid : Grid + Grid object containing radar data. + refl_field : str + Field name for reflectivity data in the Py-ART grid object. + zr_a : float, optional + Coefficient 'a' in the Z-R relationship Z = a*R^b for reflectivity to rain rate conversion. + Default is 200. The algorithm is not sensitive to precise values of 'zr_a' and 'zr_b'; however, + they must be adjusted based on the type of radar used. + zr_b : float, optional + Coefficient 'b' in the Z-R relationship Z = a*R^b. Default is 1.6. + conv_wt_threshold : float, optional + Threshold for sum of small scale wavelet components to identify strong convection. + Default is 5. Recommended values are between 4 and 6. + tran_wt_threshold : float, optional + Threshold for sum of small scale wavelet components to identify moderate/intermediate mixed convection. + Default is 1.5. Recommended values are between 1 and 2. + conv_scale_km : float, optional + Approximate scale break (in km) between convective and stratiform scales. + Scale break may vary between 15 and 30 km over different regions and seasons; however, + the algorithm is not sensitive to small variations in the scale break. + Default is 20 km taken from Raut et al (2018). + min_dbz_threshold : float, optional + Minimum reflectivity threshold. Reflectivities below this value are not classified. + Default is 5 dBZ. This value must be greater than or equal to '0'. + conv_dbz_threshold : float, optional + Reflectivities below this threshold will not be considered to be classified as convective. Default is 25 dBZ. + Recommended values are between 25 and 30 dBZ. + conv_core_threshold : float, optional + Reflectivity threshold to identify convective cores. Default is 42 dBZ. + Recommended value must be is greater than or equal to 40 dBZ. The algorithm is not sensitive to this value. + + Returns + ------- + dict: + A dictionary structured as a Py-ART grid field, suitable for adding to a Py-ART Grid object. The dictionary + contains the classification data and associated metadata. The classification categories are as follows: + - 0: No precipitation or unclassified + - 1: Stratiform/non-convective regions + - 2: Transitional and mixed convective regions + - 3: Convective cores + + References + ---------- + Raut, B. A., Karekar, R. N., & Puranik, D. M. (2008). Wavelet-based technique to extract convective clouds from + infrared satellite images. IEEE Geoscience and remote sensing letters, 5(3), 328-330. + + Raut, B. A., Seed, A. W., Reeder, M. J., & Jakob, C. (2018). A multiplicative cascade model for high‐resolution + space‐time downscaling of rainfall. Journal of Geophysical Research: Atmospheres, 123(4), 2050-2067. + + Raut, B. A., Louf, V., Gayatri, K., Murugavel, P., Konwar, M., & Prabhakaran, T. (2020). A multiresolution technique + for the classification of precipitation echoes in radar data. IEEE Transactions on Geoscience and Remote Sensing, + 58(8), 5409-5415. """ # Call the actual get_relass function to obtain radar echo classificatino - reclass = get_reclass(grid, - refl_field, - cappi_level, - zr_a, - zr_b, - conv_wt_threshold=conv_wt_threshold, - tran_wt_threshold=tran_wt_threshold, - conv_scale_km=conv_scale_km, - min_dbz_threshold=min_dbz_threshold, - conv_dbz_threshold=conv_dbz_threshold, - conv_core_threshold=conv_core_threshold) - + reclass = get_reclass( + grid, + refl_field, + cappi_level, + zr_a, + zr_b, + conv_wt_threshold=conv_wt_threshold, + tran_wt_threshold=tran_wt_threshold, + conv_scale_km=conv_scale_km, + min_dbz_threshold=min_dbz_threshold, + conv_dbz_threshold=conv_dbz_threshold, + conv_core_threshold=conv_core_threshold, + ) + reclass = np.expand_dims(reclass, axis=0) # put data into a dictionary to be added as a field @@ -1083,9 +1095,9 @@ def conv_strat_raut(grid, refl_field, cappi_level=0, zr_a=200, zr_b=1.6, "conv_scale_km": conv_scale_km, "min_dbz_threshold": min_dbz_threshold, "conv_dbz_threshold": conv_dbz_threshold, - "conv_core_threshold": conv_core_threshold - } + "conv_core_threshold": conv_core_threshold, + }, } } - return(reclass_dict) + return reclass_dict From 87b85acb1c204f2a73f1beff0b4683a111973089 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 11 Dec 2023 18:26:55 -0600 Subject: [PATCH 15/54] MOD:sanity check for conv core threshold --- pyart/retrieve/echo_class.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 28b59ff06f..03afdb97c8 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -1059,6 +1059,10 @@ def conv_strat_raut( 58(8), 5409-5415. """ + # Sanity checks for parameters + conv_core_threshold = max(conv_core_threshold, 40) # Ensure conv_core_threshold is at least 40 dBZ + + # Call the actual get_relass function to obtain radar echo classificatino reclass = get_reclass( grid, From 691dfdc33c8670a8900bcb530ffc52511e3a249f Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 11 Dec 2023 18:34:13 -0600 Subject: [PATCH 16/54] MOD:More sanity check for thresholds --- pyart/retrieve/echo_class.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 03afdb97c8..25d9bee00b 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -994,6 +994,7 @@ def conv_strat_raut( min_dbz_threshold=5, conv_dbz_threshold=25, conv_core_threshold=42, + override_checks=False ): """ Classifies radar echoes into convective cores, mixed convection, and stratiform regions using the ATWT algorithm. @@ -1059,8 +1060,15 @@ def conv_strat_raut( 58(8), 5409-5415. """ - # Sanity checks for parameters - conv_core_threshold = max(conv_core_threshold, 40) # Ensure conv_core_threshold is at least 40 dBZ + # Sanity checks for parameters if override_checks is False + if not override_checks: + conv_core_threshold = max(40, conv_core_threshold) # Ensure conv_core_threshold is at least 40 dBZ + conv_wt_threshold = max(4, min(conv_wt_threshold, 6)) # conv_wt_threshold should be between 4 and 6 + tran_wt_threshold = max(1, min(tran_wt_threshold, 2)) # tran_wt_threshold should be between 1 and 2 + conv_scale_km = max(15, min(conv_scale_km, 30)) # conv_scale_km should be between 15 and 30 km + min_dbz_threshold = max(0, min_dbz_threshold) # min_dbz_threshold should be non-negative + conv_dbz_threshold = max(25, min(conv_dbz_threshold, 30)) # conv_dbz_threshold should be between 25 and 30 dBZ + # Call the actual get_relass function to obtain radar echo classificatino From 5435665ac65810c6264e7858e91cc976fdac3b1d Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 11 Dec 2023 18:36:06 -0600 Subject: [PATCH 17/54] MOD:Added override to sanity check+documentation --- pyart/retrieve/echo_class.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 25d9bee00b..c657e2f5cd 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -1036,6 +1036,11 @@ def conv_strat_raut( conv_core_threshold : float, optional Reflectivity threshold to identify convective cores. Default is 42 dBZ. Recommended value must be is greater than or equal to 40 dBZ. The algorithm is not sensitive to this value. + override_checks : bool, optional + If set to True, the function will bypass the sanity checks for parameter values. + This allows the user to use custom values for parameters, even if they fall outside + the recommended or default ranges. The default is False, which means the function + will apply sanity checks to ensure parameter values are within specified limits. Returns ------- From b5aa79d2c604ef3f477d0345db76f193249ede0b Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 11 Dec 2023 18:49:08 -0600 Subject: [PATCH 18/54] MOD:minor --- pyart/retrieve/_echo_class_wt.py | 52 ++++++++++++++++---------------- pyart/retrieve/echo_class.py | 9 ++++-- 2 files changed, 33 insertions(+), 28 deletions(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index 45858b558e..23bc29d7a1 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -3,11 +3,11 @@ Created on Thu Oct 12 23:12:19 2017 @author: Bhupendra Raut -@modifed: 02/13/2020 +@modifed: 11/19/2023 @references: 10.1109/TGRS.2020.2965649 .. autosummary:: - getWTClass + get_reclass label_classes reflectivity_to_rainrate calc_scale_break @@ -108,30 +108,30 @@ def label_classes( conv_core_threshold, ): """ - Labels classes using given thresholds: - - 0. no precipitation, marked by the given min_dbz threshold. - - 1. stratiform, - - 2. transitional/intermediate regions, - - 3. intense/heavy convective. - - Following hard coded values are optimised and validated using C-band radars - over Darwin, Australia (2.5 km grid spacing) and tested for Solapur, India (1km grid spacing) [Raut et al. 2020]. - conv_wt_threshold = 5 # WT value more than this is strong convection - tran_wt_threshold = 2 # WT value for moderate convection - min_dbz_threshold = 10 # pixels below this value are not classified. - conv_dbz_threshold = 30 # pixel below this value are not convective. This works for most cases. - git push -u - Parameters: - =========== - wt_sum: ndarray - Integrated wavelet transform - vol_data: ndarray - Array, vector or matrix of data - - Returns: - ======== - wt_class: ndarray - Precipitation type classification. + Labels classes using given thresholds: + - 0: No precipitation or unclassified + - 1: Stratiform/non-convective regions + - 2: Transitional and mixed convective regions + - 3: Convective cores + + Following hard coded values are optimised and validated using C-band radars + over Darwin, Australia (2.5 km grid spacing) and tested for Solapur, India (1km grid spacing) [Raut et al. 2020]. + conv_wt_threshold = 5 # WT value more than this is strong convection + tran_wt_threshold = 2 # WT value for moderate convection + min_dbz_threshold = 10 # pixels below this value are not classified. + conv_dbz_threshold = 30 # pixel below this value are not convective. This works for most cases. + + Parameters: + =========== + wt_sum: ndarray + Integrated wavelet transform + vol_data: ndarray + Array, vector or matrix of data + + Returns: + ======== + wt_class: ndarray + Precipitation type classification. """ # I first used negative numbers to annotate the categories. Then multiply it by -1. diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index c657e2f5cd..4a2d7b2bce 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -1039,8 +1039,7 @@ def conv_strat_raut( override_checks : bool, optional If set to True, the function will bypass the sanity checks for parameter values. This allows the user to use custom values for parameters, even if they fall outside - the recommended or default ranges. The default is False, which means the function - will apply sanity checks to ensure parameter values are within specified limits. + the recommended or default ranges. The default is False. Returns ------- @@ -1065,6 +1064,10 @@ def conv_strat_raut( 58(8), 5409-5415. """ + # I don't know how to Check if the grid is a Py-ART Grid object + #if not isinstance(grid, pyart.core.Grid): + # raise TypeError("The 'grid' is not a Py-ART Grid object.") + # Sanity checks for parameters if override_checks is False if not override_checks: conv_core_threshold = max(40, conv_core_threshold) # Ensure conv_core_threshold is at least 40 dBZ @@ -1073,6 +1076,8 @@ def conv_strat_raut( conv_scale_km = max(15, min(conv_scale_km, 30)) # conv_scale_km should be between 15 and 30 km min_dbz_threshold = max(0, min_dbz_threshold) # min_dbz_threshold should be non-negative conv_dbz_threshold = max(25, min(conv_dbz_threshold, 30)) # conv_dbz_threshold should be between 25 and 30 dBZ + + From 71bda535142792c987d42a1dc446da84d9c3f872 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Tue, 12 Dec 2023 14:28:43 -0600 Subject: [PATCH 19/54] invalid grid raises exception --- pyart/retrieve/echo_class.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 4a2d7b2bce..829059470f 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -10,7 +10,7 @@ from ..config import get_field_name, get_fillvalue, get_metadata from ._echo_class import _feature_detection, steiner_class_buff from ._echo_class_wt import get_reclass - +from ..core import Grid def steiner_conv_strat( grid, @@ -984,7 +984,7 @@ def get_freq_band(freq): def conv_strat_raut( grid, - refl_field, + refl_field='reflectivity', cappi_level=0, zr_a=200, zr_b=1.6, @@ -1065,8 +1065,8 @@ def conv_strat_raut( """ # I don't know how to Check if the grid is a Py-ART Grid object - #if not isinstance(grid, pyart.core.Grid): - # raise TypeError("The 'grid' is not a Py-ART Grid object.") + if not isinstance(grid, Grid): + raise TypeError("The 'grid' is not a Py-ART Grid object.") # Sanity checks for parameters if override_checks is False if not override_checks: @@ -1076,8 +1076,6 @@ def conv_strat_raut( conv_scale_km = max(15, min(conv_scale_km, 30)) # conv_scale_km should be between 15 and 30 km min_dbz_threshold = max(0, min_dbz_threshold) # min_dbz_threshold should be non-negative conv_dbz_threshold = max(25, min(conv_dbz_threshold, 30)) # conv_dbz_threshold should be between 25 and 30 dBZ - - From 1d7f71eaa90e29f2e60987d6f112129dc8e6e98e Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Tue, 12 Dec 2023 14:32:53 -0600 Subject: [PATCH 20/54] first unit test added --- tests/retrieve/test_echo_class.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/tests/retrieve/test_echo_class.py b/tests/retrieve/test_echo_class.py index 288ca32dd9..bec0dfba50 100644 --- a/tests/retrieve/test_echo_class.py +++ b/tests/retrieve/test_echo_class.py @@ -300,3 +300,12 @@ def test_standardize(): assert_allclose(field_std_no_limits[0], [1.0, 1.0, 1.0, 1.0, 1.0], atol=1e-6) pytest.raises(ValueError, pyart.retrieve.echo_class._standardize, rhohv, "foo") + + +def test_conv_strat_raut_inGrid_validity(): + """ + Test that function raises `TypeError` with invalid grid object as input. + """ + pytest.raises(TypeError, pyart.retrieve.conv_strat_raut, None, "foo") + + From 5b04a81526a1bca94e4372c9c42b0f5d37480cce Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Tue, 12 Dec 2023 15:27:34 -0600 Subject: [PATCH 21/54] ADD:func make_gaussian_storm_grid --- pyart/testing/sample_objects.py | 56 +++++++++++++++++++++++++++++++++ 1 file changed, 56 insertions(+) diff --git a/pyart/testing/sample_objects.py b/pyart/testing/sample_objects.py index fb4f1f0212..2634618c8e 100644 --- a/pyart/testing/sample_objects.py +++ b/pyart/testing/sample_objects.py @@ -378,6 +378,62 @@ def make_normal_storm(sigma, mu): return test_grid +def make_gaussian_storm_grid(min_value=5, max_value=45, grid_len=32, + sigma=0.2, mu=0.0, masked_boundary=3): + """ + Make a 1 km resolution grid with a Gaussian storm pattern at the center, + with two layers having the same data and masked boundaries. + + Parameters: + min_value : float + Minimum value of the storm intensity. + max_value : float + Maximum value of the storm intensity. + grid_len : int + Size of the grid (grid will be grid_len x grid_len). + sigma : float + Standard deviation of the Gaussian distribution. + mu : float + Mean of the Gaussian distribution. + masked_boundary : int + Number of pixels around the edge to be masked. + + Returns: + A Py-ART grid with the Gaussian storm field added. + """ + + # Create an empty Py-ART grid + grid_shape = (2, grid_len, grid_len) + grid_limits = ((1000, 1000), (-grid_len*1000/2, grid_len*1000/2), (-grid_len*1000/2, grid_len*1000/2)) + grid = make_empty_grid(grid_shape, grid_limits) + + # Creating a grid with Gaussian distribution values + x, y = np.meshgrid(np.linspace(-1, 1, grid_len), np.linspace(-1, 1, grid_len)) + d = np.sqrt(x*x + y*y) + gaussian = np.exp(-((d - mu)**2 / (2.0 * sigma**2))) + + # Normalize and scale the Gaussian distribution + gaussian_normalized = (gaussian - np.min(gaussian)) / (np.max(gaussian) - np.min(gaussian)) + storm_intensity = gaussian_normalized * (max_value - min_value) + min_value + storm_intensity = np.stack([storm_intensity, storm_intensity]) + + + # Apply thresholds for storm intensity and masking + mask = np.zeros_like(storm_intensity, dtype=bool) + mask[:, :masked_boundary, :] = True + mask[:, -masked_boundary:, :] = True + mask[:, :, :masked_boundary] = True + mask[:, :, -masked_boundary:] = True + + + storm_intensity = np.ma.array(storm_intensity, mask=mask) + # Prepare dictionary for Py-ART grid fields + rdic = {"data": storm_intensity, "long_name": "reflectivity", "units": "dBz"} + grid.fields = {"reflectivity": rdic} + + return grid + + def make_empty_spectra_radar(nrays, ngates, npulses_max): """ Return a Spectra Radar object. From 19c6cb0ed438b718aabb312350f41d195810d4f4 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Tue, 12 Dec 2023 15:28:44 -0600 Subject: [PATCH 22/54] ADD: test for output validity --- tests/retrieve/test_echo_class.py | 32 +++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/tests/retrieve/test_echo_class.py b/tests/retrieve/test_echo_class.py index bec0dfba50..830787a285 100644 --- a/tests/retrieve/test_echo_class.py +++ b/tests/retrieve/test_echo_class.py @@ -308,4 +308,36 @@ def test_conv_strat_raut_inGrid_validity(): """ pytest.raises(TypeError, pyart.retrieve.conv_strat_raut, None, "foo") +def test_conv_strat_raut_valid_outDict(): + """ + Test that function returns a valid dictionary with all expected keys'. + """ + + # Create a Gaussian storm grid + gaussian_storm_2d = pyart.testing.make_gaussian_storm_grid() + wtclass = pyart.retrieve.conv_strat_raut(gaussian_storm_2d, "reflectivity", cappi_level=0) + + # First check that it's a pthon dictionary + assert isinstance(wtclass, dict), "Output is not a dictionary" + # then test 'wt_reclass' key exists in the dict + assert "wt_reclass" in wtclass.keys() + + # Now test other expectd expected keys + expected_keys = [ + "data", + "standard_name", + "long_name", + "valid_min", + "valid_max", + "classification_description", + "parameters", + ] + + # Get the keys of the 'wt_reclass' field + reclass_keys = wtclass["wt_reclass"].keys() + + # Check each expected key + for key in expected_keys: + assert key in reclass_keys + From 83155a73b79fb4078f4abb1c97ca879517aae88f Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Tue, 12 Dec 2023 16:42:43 -0600 Subject: [PATCH 23/54] ADD:utest for results of classification --- tests/retrieve/test_echo_class.py | 43 ++++++++++++++++++++++++++++++- 1 file changed, 42 insertions(+), 1 deletion(-) diff --git a/tests/retrieve/test_echo_class.py b/tests/retrieve/test_echo_class.py index 830787a285..2cdabab5a2 100644 --- a/tests/retrieve/test_echo_class.py +++ b/tests/retrieve/test_echo_class.py @@ -314,7 +314,8 @@ def test_conv_strat_raut_valid_outDict(): """ # Create a Gaussian storm grid - gaussian_storm_2d = pyart.testing.make_gaussian_storm_grid() + grid_len = 32 + gaussian_storm_2d = pyart.testing.make_gaussian_storm_grid(min_value=5, max_value=45, grid_len=grid_len, sigma=0.2, mu=0, masked_boundary=3) wtclass = pyart.retrieve.conv_strat_raut(gaussian_storm_2d, "reflectivity", cappi_level=0) # First check that it's a pthon dictionary @@ -340,4 +341,44 @@ def test_conv_strat_raut_valid_outDict(): for key in expected_keys: assert key in reclass_keys + #check grid shape + assert wtclass['wt_reclass']['data'].shape==(1, grid_len, grid_len) + + +def test_conv_strat_raut_valid_results(): + # Create a Gaussian storm grid + grid_len = 32 + mask_margin = 3 + gaussian_storm_2d = pyart.testing.make_gaussian_storm_grid(min_value=5, max_value=45, grid_len=grid_len, sigma=0.2, mu=0, masked_boundary=mask_margin) + wtclass = pyart.retrieve.conv_strat_raut(gaussian_storm_2d, "reflectivity", cappi_level=0) + + # Create a 32x32 array of ones + test_reclass = np.ones((grid_len, grid_len)) + + # Mask the edges + test_reclass[:mask_margin, :] = np.nan + test_reclass[-mask_margin:, :] = np.nan + test_reclass[:, :mask_margin] = np.nan + test_reclass[:, -mask_margin:] = np.nan + + # Define the center and create the 4x4 area + center = grid_len // 2 + + #these are actual rsults from sucessful run + test_reclass[center-3:center+3, center-3:center+3] = 2 + test_reclass[center-2:center+2, center-2:center+2] = 3 + + test_reclass[13, 13] = 1 + test_reclass[13, 18] = 1 + test_reclass[18, 13] = 1 + test_reclass[18, 18] = 1 + + # Creating a mask for NaN values + mask = np.isnan(test_reclass) + masked_reclass =np.ma.array(test_reclass, mask=mask).astype(np.int32) + masked_reclass = np.expand_dims(masked_reclass, axis=0) + + assert_allclose(masked_reclass, wtclass['wt_reclass']['data']) + + From 0977cd434abf2d188903be3e263a4db94c7657d6 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Tue, 12 Dec 2023 16:51:44 -0600 Subject: [PATCH 24/54] ENH: test documentation and apply black formatting --- tests/retrieve/test_echo_class.py | 53 +++++++++++++++++++++---------- 1 file changed, 36 insertions(+), 17 deletions(-) diff --git a/tests/retrieve/test_echo_class.py b/tests/retrieve/test_echo_class.py index 2cdabab5a2..8fce3e52e4 100644 --- a/tests/retrieve/test_echo_class.py +++ b/tests/retrieve/test_echo_class.py @@ -308,15 +308,20 @@ def test_conv_strat_raut_inGrid_validity(): """ pytest.raises(TypeError, pyart.retrieve.conv_strat_raut, None, "foo") + def test_conv_strat_raut_valid_outDict(): """ Test that function returns a valid dictionary with all expected keys'. """ - # Create a Gaussian storm grid + # Create a Gaussian storm grid grid_len = 32 - gaussian_storm_2d = pyart.testing.make_gaussian_storm_grid(min_value=5, max_value=45, grid_len=grid_len, sigma=0.2, mu=0, masked_boundary=3) - wtclass = pyart.retrieve.conv_strat_raut(gaussian_storm_2d, "reflectivity", cappi_level=0) + gaussian_storm_2d = pyart.testing.make_gaussian_storm_grid( + min_value=5, max_value=45, grid_len=grid_len, sigma=0.2, mu=0, masked_boundary=3 + ) + wtclass = pyart.retrieve.conv_strat_raut( + gaussian_storm_2d, "reflectivity", cappi_level=0 + ) # First check that it's a pthon dictionary assert isinstance(wtclass, dict), "Output is not a dictionary" @@ -341,16 +346,34 @@ def test_conv_strat_raut_valid_outDict(): for key in expected_keys: assert key in reclass_keys - #check grid shape - assert wtclass['wt_reclass']['data'].shape==(1, grid_len, grid_len) + # check grid shape + assert wtclass["wt_reclass"]["data"].shape == (1, grid_len, grid_len) def test_conv_strat_raut_valid_results(): - # Create a Gaussian storm grid + """ + Checks the correctness of the results from the function. + + I created a fixed Gaussian storm with masked boundaries as pyart grid and classifed it. + Then constructed manually the expected classification results and compared it to the actual output from the function. + """ + + # Create a Gaussian storm grid grid_len = 32 mask_margin = 3 - gaussian_storm_2d = pyart.testing.make_gaussian_storm_grid(min_value=5, max_value=45, grid_len=grid_len, sigma=0.2, mu=0, masked_boundary=mask_margin) - wtclass = pyart.retrieve.conv_strat_raut(gaussian_storm_2d, "reflectivity", cappi_level=0) + + gaussian_storm_2d = pyart.testing.make_gaussian_storm_grid( + min_value=5, + max_value=45, + grid_len=grid_len, + sigma=0.2, + mu=0, + masked_boundary=mask_margin, + ) + + wtclass = pyart.retrieve.conv_strat_raut( + gaussian_storm_2d, "reflectivity", cappi_level=0 + ) # Create a 32x32 array of ones test_reclass = np.ones((grid_len, grid_len)) @@ -363,10 +386,9 @@ def test_conv_strat_raut_valid_results(): # Define the center and create the 4x4 area center = grid_len // 2 - - #these are actual rsults from sucessful run - test_reclass[center-3:center+3, center-3:center+3] = 2 - test_reclass[center-2:center+2, center-2:center+2] = 3 + # these are actual rsults from sucessful run + test_reclass[center - 3 : center + 3, center - 3 : center + 3] = 2 + test_reclass[center - 2 : center + 2, center - 2 : center + 2] = 3 test_reclass[13, 13] = 1 test_reclass[13, 18] = 1 @@ -375,10 +397,7 @@ def test_conv_strat_raut_valid_results(): # Creating a mask for NaN values mask = np.isnan(test_reclass) - masked_reclass =np.ma.array(test_reclass, mask=mask).astype(np.int32) + masked_reclass = np.ma.array(test_reclass, mask=mask).astype(np.int32) masked_reclass = np.expand_dims(masked_reclass, axis=0) - assert_allclose(masked_reclass, wtclass['wt_reclass']['data']) - - - + assert_allclose(masked_reclass, wtclass["wt_reclass"]["data"]) From be97289ef5d5c492b35f3b9b7feaae389d4a8ef3 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Tue, 12 Dec 2023 17:32:06 -0600 Subject: [PATCH 25/54] ENH:small test func merged;name change --- tests/retrieve/test_echo_class.py | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/tests/retrieve/test_echo_class.py b/tests/retrieve/test_echo_class.py index 8fce3e52e4..6c1f720658 100644 --- a/tests/retrieve/test_echo_class.py +++ b/tests/retrieve/test_echo_class.py @@ -302,18 +302,15 @@ def test_standardize(): pytest.raises(ValueError, pyart.retrieve.echo_class._standardize, rhohv, "foo") -def test_conv_strat_raut_inGrid_validity(): - """ - Test that function raises `TypeError` with invalid grid object as input. - """ - pytest.raises(TypeError, pyart.retrieve.conv_strat_raut, None, "foo") - -def test_conv_strat_raut_valid_outDict(): +def test_conv_strat_raut_outDict_valid(): """ Test that function returns a valid dictionary with all expected keys'. """ + # Test that function raises `TypeError` with invalid grid object as input. + pytest.raises(TypeError, pyart.retrieve.conv_strat_raut, None, "foo") + # Create a Gaussian storm grid grid_len = 32 gaussian_storm_2d = pyart.testing.make_gaussian_storm_grid( @@ -350,10 +347,10 @@ def test_conv_strat_raut_valid_outDict(): assert wtclass["wt_reclass"]["data"].shape == (1, grid_len, grid_len) -def test_conv_strat_raut_valid_results(): +def test_conv_strat_raut_results_correct(): """ Checks the correctness of the results from the function. - + I created a fixed Gaussian storm with masked boundaries as pyart grid and classifed it. Then constructed manually the expected classification results and compared it to the actual output from the function. """ From d1f7aa0355c1a8b105b8736a190effc3c02352fe Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Tue, 12 Dec 2023 17:40:17 -0600 Subject: [PATCH 26/54] ADD: Stat-based tests for Gaussian storm grid function --- tests/testing/test_sample_objects.py | 58 ++++++++++++++++++++++++++++ 1 file changed, 58 insertions(+) create mode 100644 tests/testing/test_sample_objects.py diff --git a/tests/testing/test_sample_objects.py b/tests/testing/test_sample_objects.py new file mode 100644 index 0000000000..517ad8b9ea --- /dev/null +++ b/tests/testing/test_sample_objects.py @@ -0,0 +1,58 @@ +""" Unit Tests for Py-ART's testing/sample_objects.py module. """ + +import numpy as np +#import pytest +from numpy.testing import assert_allclose + +import pyart + +from pyart.testing.sample_objects import make_gaussian_storm_grid + + +def test_gaussian_storm_grid_results_correct(): + """ + Test for the make_gaussian_storm_grid function. + + Checks grid shape, limits, field data, and masking. + These test are focusing on statistical properties of the storm and not on comparing exact storm values. + """ + grid_len = 32 + min_value = 5 + max_value = 45 + sigma = 0.2 + mu = 0.0 + mask_margin = 3 + + expected_limits = ((1000, 1000), (-grid_len*1000/2, grid_len*1000/2), (-grid_len*1000/2, grid_len*1000/2)) + + # Create grid + gaussian_storm_2d = make_gaussian_storm_grid() + + # Test Shape + assert gaussian_storm_2d.fields['reflectivity']['data'].shape == (2, grid_len, grid_len), "Grid shape mismatch" + + # Test Data + assert gaussian_storm_2d.fields['reflectivity']['data'] is not None, "No data in reflectivity field" + + # Test Masking + mask = gaussian_storm_2d.fields['reflectivity']['data'].mask + assert np.all(mask[:, :mask_margin, :]), "Masking at the boundary is incorrect" + assert np.all(mask[:, -mask_margin:, :]), "Masking at the boundary is incorrect" + assert np.all(mask[:, :, :mask_margin]), "Masking at the boundary is incorrect" + assert np.all(mask[:, :, -mask_margin:]), "Masking at the boundary is incorrect" + + + storm_data = gaussian_storm_2d.fields['reflectivity']['data'] + + # Test for Max and Min + assert np.isclose(np.max(storm_data), max_value), "Maximum value does not match expected" + assert np.isclose(np.min(storm_data[storm_data.mask == False]), min_value), "Minimum value does not match expected" + + # Test Mean and SD + expected_mean = 8.666844653650797 + expected_std = 7.863066829145 + assert np.isclose(np.mean(storm_data), expected_mean, atol=5), "Mean value out of expected range" + assert np.isclose(np.std(storm_data), expected_std, atol=5), "Standard deviation out of expected range" + + # Test Central Value + assert storm_data[0, 15, 15] == max_value, "Maximum value is not at the center" From 4512f59e220818ac475186f57b1e5e3ace86999f Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Tue, 12 Dec 2023 18:34:09 -0600 Subject: [PATCH 27/54] ADD: example --- .../retrieve/wavelet_echo_class_example.ipynb | 474 ++++++++++++++++++ 1 file changed, 474 insertions(+) create mode 100644 examples/retrieve/wavelet_echo_class_example.ipynb diff --git a/examples/retrieve/wavelet_echo_class_example.ipynb b/examples/retrieve/wavelet_echo_class_example.ipynb new file mode 100644 index 0000000000..3890b4c352 --- /dev/null +++ b/examples/retrieve/wavelet_echo_class_example.ipynb @@ -0,0 +1,474 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fast Wavelet-Based Classification of Radar Echoes into Convection Core, Mixed-Intermediate and Stratiform Classes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This cookbook demonstrates the wavelet-based radar echo classification method from Raut et al. (2008 and 2020) [1, 2]. \n", + "\n", + "\n", + "## Introduction:\n", + "In radar data, convective regions are characterized by horizontal inhomogeneity, generally high reflectivity than surrounding echoes, and vertical growth, as opposed to the more horizontally homogeneous stratiform regions. Historically, radar echoes were classified into convective and stratiform categories using reflectivity thresholds, typically around 40 dBZ. However, this approach is not reliable as significant fraction of convection has reflectivities comparable to stratiform rain below the 40dBZ threshold. To address these challenges, algorithms that consider the horizontal reflectivity structure as an additinal criteria were developed although the threshold was still used as the primary criteria [3, 4, 5]. \n", + "\n", + "The rain exhibit a wide range of spatial frequencies, or scales, embedded within self-similar structures [6] and convection and stratiform can be identified by the scale analysis of the images. Fourier transform (FT), although can compute the power spectrum of these images to study the dominant frequencies, cannot identify localized structure within the image. A multiresolution approximation separates features of different scales within the image. Wavelets iteratively decompose the image into different resolutions or scales. The á trous wavelet transform (WT) is particularly prominent in astrophysics and medical imaging.\n", + "\n", + "## The Á Trous Wavelet Transform\n", + "The á trous wavelet transform, as proposed by Shensa [7] and further developed by Starck and Murtagh [8], is utilized in this algorithm. This algorithm employs a scaling function at dyadically increasing scales to approximate the original image at successively coarser resolutions and the wavelet coefficients at a given scale are the difference between two successive approximations.\n", + "\n", + "This has significant implications for meteorological analysis, particularly in the classification of convection from stratiform regions in radar and satellite data. The multiresolution analysis offers an objective classification scheme to classify embedded or isolated convection without the need for specific conditions and intensity thresholds.\n", + "\n", + "## Classification Scheme\n", + "\n", + "1. **Transform Reflectivity to Rain Field**: The first step involves transforming the reflectivity field into a rain field. The standard ZR relationship should work for most radars.\n", + "\n", + " 2. **Compute Wavelet Transform (WT) of the Rain Field**: \n", + "The WT is computed for the rain field across `n` different scales, where `n` can be 15-30 kilometers as discussed in Raut et al (2018) [9]. This process breaks down the rain field into various scales.\n", + "\n", + "3. **Sum of Wavelet Scales (wt_sum)**: \n", + "The next step is to sum up all these 'n' wavelet scales. \n", + "\n", + "4. The classification of the precipitation type is then determined based on `wt_sum` and the original dBZ values (`vol_data`):\n", + "\n", + " - **Unclassified**: If `reflectivity < min_dbz_threshold`, the precipitation is too low to be classified.\n", + " - **Convective Core**: If `wt_sum ≥ conv_wt_threshold AND reflectivity > conv_dbz_threshold`, the precipitation is classified as 'Convective Core'. This implies a higher intensity and potentially active collision and coalescence.\n", + " - **Mix or Intermediate**: If `conv_wt_threshold > wt_sum ≥ tran_wt_threshold AND reflectivity > conv_dbz_threshold`, the precipitation is categorized as 'Intermediate or Mix Convective'. This rain is not as intense as convective core but it has more significant liquid water content than stratiform.\n", + " - **Stratiform**: If `wt_sum < tran_wt_threshold AND reflectivity > min_dbz_threshold`, the precipitation is classified as 'Stratiform or non-convective'. This is typically more uniform and less intense than convective class.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test Examples" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "import pyart\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Case 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Reading and Preparing Radar Data\n", + "We load a sample radar data file using Py-ART, extracts the lowest sweep, and then interpolates this data onto a cartesian grid. The dx and dy variables represent the grid resolution in the x and y directions, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# read in the test file\n", + "filename = pyart.testing.get_test_data(\"swx_20120520_0641.nc\")\n", + "radar = pyart.io.read(filename)\n", + "\n", + "# extract the lowest sweep\n", + "radar = radar.extract_sweeps([0])\n", + "\n", + "# interpolate to grid\n", + "grid = pyart.map.grid_from_radars(\n", + " (radar,),\n", + " grid_shape=(1, 201, 201),\n", + " grid_limits=((0, 10000), (-50000.0, 50000.0), (-50000.0, 50000.0)),\n", + " fields=[\"reflectivity_horizontal\"],\n", + ")\n", + "\n", + "# get dx dy\n", + "dx = grid.x[\"data\"][1] - grid.x[\"data\"][0]\n", + "dy = grid.y[\"data\"][1] - grid.y[\"data\"][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Using `Peak Feature Detection`\n", + "Lets now performs convective-stratiform classification on the radar data using the Yuter method [4, 5], which is a part of Py-ART's retrieve module. The result is added to the grid as a new field for further analysis or visualization.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/bhupendra/anaconda3/envs/pyart/lib/python3.12/site-packages/scipy/ndimage/_filters.py:1769: RuntimeWarning: Mean of empty slice\n", + " _nd_image.generic_filter(input, function, footprint, output, mode,\n" + ] + } + ], + "source": [ + "# convective stratiform classification Yuter\n", + "convsf_dict = pyart.retrieve.conv_strat_yuter(\n", + " grid,\n", + " dx,\n", + " dy,\n", + " refl_field=\"reflectivity_horizontal\",\n", + " always_core_thres=40,\n", + " bkg_rad_km=20,\n", + " use_cosine=True,\n", + " max_diff=3,\n", + " zero_diff_cos_val=55,\n", + " weak_echo_thres=5,\n", + " max_conv_rad_km=2,\n", + " estimate_flag=False,\n", + ")\n", + "\n", + "grid.add_field(\"convsf\", convsf_dict[\"feature_detection\"], replace_existing=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Using `Wavelet Scale Analysis`\n", + "The function processes radar data using a Py-ART Grid object and a specified reflectivity field (`refl_field`). It offers options to adjust the Z-R relationship coefficients (`zr_a` and `zr_b`) and various thresholds for tailored classification. The output is a dictionary, `reclass_dict`, ready for integration into a Py-ART Grid. This dictionary includes the classification results, a description of the categories, and a record of the used parameters for transparency and reference.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/bhupendra/projects/pyart/pyart/retrieve/_echo_class_wt.py:156: RuntimeWarning: invalid value encountered in cast\n", + " return wt_class.astype(np.int32)\n" + ] + } + ], + "source": [ + "reclass_dict = pyart.retrieve.conv_strat_raut(\n", + " grid, \n", + " refl_field=\"reflectivity_horizontal\")\n", + "\n", + "# add field\n", + "grid.add_field(\"wt_reclass\", reclass_dict[\"wt_reclass\"], replace_existing=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting \n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Required imports\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as mcolors\n", + "import cartopy.crs as ccrs\n", + "\n", + "\n", + "display = pyart.graph.GridMapDisplay(grid)\n", + "\n", + "# Create a colormap for reflectivity\n", + "magma_r_cmap = plt.get_cmap(\"magma_r\")\n", + "ref_cmap = mcolors.LinearSegmentedColormap.from_list(\n", + " \"ref_cmap\", magma_r_cmap(np.linspace(0, 0.9, magma_r_cmap.N))\n", + ")\n", + "\n", + "# Define the projection\n", + "projection = ccrs.AlbersEqualArea(\n", + " central_latitude=radar.latitude[\"data\"][0],\n", + " central_longitude=radar.longitude[\"data\"][0],\n", + ")\n", + "\n", + "# Create a figure with a 2x2 layout\n", + "plt.figure(figsize=(18, 5))\n", + "\n", + "# First panel - Reflectivity (Top Left)\n", + "ax1 = plt.subplot(1, 3, 1, projection=projection)\n", + "display.plot_grid(\n", + " \"reflectivity_horizontal\", vmin=0, vmax=55, cmap=ref_cmap,\n", + " transform=ccrs.PlateCarree(), ax=ax1\n", + ")\n", + "\n", + "# Second panel - CSY (Top Right)\n", + "ax2 = plt.subplot(1, 3, 2, projection=projection)\n", + "display.plot_grid(\n", + " \"convsf\", vmin=0, vmax=3, cmap=plt.get_cmap(\"pyart_HomeyerRainbow\", 4), ax=ax2,\n", + " transform=ccrs.PlateCarree(), ticks=[1 / 3, 1, 5 / 3],\n", + " ticklabs=[\"< 5dBZ\", \"Stratiform\", \"Convective\"]\n", + ")\n", + "\n", + "# Third panel - WT (Bottom Left)\n", + "ax3 = plt.subplot(1, 3, 3, projection=projection)\n", + "display.plot_grid(\n", + " \"wt_reclass\", vmin=0, vmax=4, cmap=plt.get_cmap(\"pyart_HomeyerRainbow\", 4), ax=ax3,\n", + " transform=ccrs.PlateCarree(), ticks=[0.5, 1.5, 2.5, 3.5],\n", + " ticklabs=[\"< 5dBZ\", \"Non-Convective\", \"Convective (Mixed)\", \"Convective (Cores)\"]\n", + ")\n", + "\n", + "# Show the plot\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Case 2\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in file\n", + "nexrad_file = \"s3://noaa-nexrad-level2/2022/09/28/KTBW/KTBW20220928_190142_V06\"\n", + "radar = pyart.io.read_nexrad_archive(nexrad_file)\n", + "\n", + "# extract the lowest sweep\n", + "radar = radar.extract_sweeps([0])\n", + "\n", + "# interpolate to grid\n", + "grid = pyart.map.grid_from_radars(\n", + " (radar,),\n", + " grid_shape=(1, 201, 201),\n", + " grid_limits=((0, 10000), (-200000.0, 200000.0), (-200000.0, 200000.0)),\n", + " fields=[\"reflectivity\"],\n", + ")\n", + "\n", + "# get dx dy\n", + "dx = grid.x[\"data\"][1] - grid.x[\"data\"][0]\n", + "dy = grid.y[\"data\"][1] - grid.y[\"data\"][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/bhupendra/anaconda3/envs/pyart/lib/python3.12/site-packages/scipy/ndimage/_filters.py:1769: RuntimeWarning: Mean of empty slice\n", + " _nd_image.generic_filter(input, function, footprint, output, mode,\n" + ] + } + ], + "source": [ + "# convective stratiform classification Yuter\n", + "convsf_dict = pyart.retrieve.conv_strat_yuter(\n", + " grid,\n", + " dx,\n", + " dy,\n", + " refl_field=\"reflectivity\",\n", + " always_core_thres=40,\n", + " bkg_rad_km=20,\n", + " use_cosine=True,\n", + " max_diff=3,\n", + " zero_diff_cos_val=55,\n", + " weak_echo_thres=5,\n", + " max_conv_rad_km=2,\n", + " estimate_flag=False,\n", + ")\n", + "\n", + "\n", + "# add to grid object\n", + "# mask zero values (no surface echo)\n", + "convsf_masked = np.ma.masked_equal(convsf_dict[\"feature_detection\"][\"data\"], 0)\n", + "# mask 3 values (weak echo)\n", + "convsf_masked = np.ma.masked_equal(convsf_masked, 3)\n", + "# add dimension to array to add to grid object\n", + "convsf_dict[\"feature_detection\"][\"data\"] = convsf_masked\n", + "# add field\n", + "grid.add_field(\"convsf\", convsf_dict[\"feature_detection\"], replace_existing=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/bhupendra/projects/pyart/pyart/retrieve/_echo_class_wt.py:156: RuntimeWarning: invalid value encountered in cast\n", + " return wt_class.astype(np.int32)\n" + ] + } + ], + "source": [ + "reclass_dict = pyart.retrieve.conv_strat_raut(\n", + " grid, \n", + " refl_field=\"reflectivity\"\n", + ")\n", + "\n", + "# add field\n", + "grid.add_field(\"wt_reclass\", reclass_dict[\"wt_reclass\"], replace_existing=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Required imports\n", + "\n", + "display = pyart.graph.GridMapDisplay(grid)\n", + "\n", + "# Create a colormap for reflectivity\n", + "magma_r_cmap = plt.get_cmap(\"magma_r\")\n", + "ref_cmap = mcolors.LinearSegmentedColormap.from_list(\n", + " \"ref_cmap\", magma_r_cmap(np.linspace(0, 0.9, magma_r_cmap.N))\n", + ")\n", + "\n", + "# Define the projection\n", + "projection = ccrs.AlbersEqualArea(\n", + " central_latitude=radar.latitude[\"data\"][0],\n", + " central_longitude=radar.longitude[\"data\"][0],\n", + ")\n", + "\n", + "# Create a figure with a 2x2 layout\n", + "plt.figure(figsize=(18, 5))\n", + "\n", + "# First panel - Reflectivity (Top Left)\n", + "ax1 = plt.subplot(1, 3, 1, projection=projection)\n", + "display.plot_grid(\n", + " \"reflectivity\", vmin=0, vmax=55, cmap=ref_cmap,\n", + " transform=ccrs.PlateCarree(), ax=ax1\n", + ")\n", + "\n", + "# Second panel - csy (Bottom Left)\n", + "ax2 = plt.subplot(1, 3, 2, projection=projection)\n", + "display.plot_grid(\n", + " \"convsf\", vmin=0, vmax=3, cmap=plt.get_cmap(\"pyart_HomeyerRainbow\", 4), ax=ax2,\n", + " transform=ccrs.PlateCarree(), ticks=[1 / 3, 1, 2],\n", + " ticklabs=[\"< 5dBZ\", \"Stratiform\", \"Convective\"]\n", + ")\n", + "\n", + "# third panel - reclass (Bottom Right)\n", + "ax3 = plt.subplot(1, 3, 3, projection=projection)\n", + "display.plot_grid(\n", + " \"wt_reclass\", vmin=0, vmax=4, cmap=plt.get_cmap(\"pyart_HomeyerRainbow\", 4), ax=ax3,\n", + " transform=ccrs.PlateCarree(), ticks=[0.5, 1.5, 2.5, 3.5],\n", + " ticklabs=[\"< 5dBZ\", \"Non-Convective\", \"Convective (Mixed)\", \"Convective (Cores)\"]\n", + ")\n", + "\n", + "# Show the plot\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Remarks:\n", + "Both the methods primarily agree on the location of the convection; however, the wavelet transform reveals more intricate details in the shape of the identified convective regions. The further separation of convection into `cores` and `intermediate-mixed` category is particularly notable. The comparison of Drop Size Distributions (DSD) for these classes shows the WT method's efficiency in segregating radar rainfall regions that are microphysically distinct [2]. The stratiform or non-convective precipitation, characterized by smaller drops and the lowest drop density, contrasts with convective core precipitation, which exhibits a high drop density and abundance of large drops. The intermediate or mixed rain category, marked by a high concentration of small and medium size drops and a lack of large drops, provides further insights into the microphysical processes and the convective-stratiform organization." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References:\n", + "1. Raut, B. A., Karekar, R. N., & Puranik, D. M. (2008). \"Wavelet-based technique to extract convective clouds from infrared satellite images.\" IEEE Geosci. Remote Sens. Lett., 5(3), 328–330. [DOI](https://doi.org/10.1109/LGRS.2008.916072)\n", + "2. Raut, B. A., Louf, V., Gayatri, K., Murugavel, P., Konwar, M., & Prabhakaran, T. (2020). \"A Multiresolution Technique for the Classification of Precipitation Echoes in Radar Data.\" IEEE Trans. Geosci. Remote Sens., 58(8), 5409. [DOI](https://doi.org/10.1109/TGRS.2020.2965649)\n", + "3. Churchill, D. D., & Houze, R. A. (1984). \"Development and structure of winter monsoon cloud clusters on 10 December 1978.\" J. Atmos. Sci., 41(6), 933-960. [DOI](https://doi.org/10.1175/1520-0469(1984)041<0933:DASOWM>2.0.CO;2)\n", + "4. Steiner, M. R., Houze Jr., R. A., & Yuter, S. E. (1995). \"Climatological Characterization of Three-Dimensional Storm Structure from Operational Radar and Rain Gauge Data.\" J. Appl. Meteor., 34, 1978-2007. [DOI](https://doi.org/10.1175/1520-0450(1995)034<1978:CCOTDS>2.0.CO;2)\n", + "5. Yuter, S. E., & Houze Jr., R. A. (1997). \"Measurements of raindrop size distributions over the Pacific warm pool and implications for Z-R relations.\" J. Appl. Meteor., 36, 847-867. [DOI](https://doi.org/10.1175/1520-0450(1997)036<0847:MORSDO>2.0.CO;2)\n", + "6. Lovejoy, S., & Schertzer, D. (1985). \"Generalized scale invariance in the atmosphere and fractal models of rain.\" Water Resour. Res., 21(8), 1233–1250. [DOI](https://doi.org/10.1029/WR021i008p01233)\n", + "7. Starck, J.-L., Murtagh, F. D., & Bijaoui, A. (1998). \"Image Processing and Data Analysis: The Multiscale Approach.\" Cambridge Univ. Press.\n", + "8. Shensa, M. J. (1992). \"The discrete wavelet transform: Wedding the à trous and Mallat algorithms.\" IEEE Trans. Signal Process., 40(10), 2464–2482. [DOI](https://doi.org/10.1109/78.157290)\n", + "9. Raut, B. A., Seed, A. W., Reeder, M. J., & Jakob, C. (2018). \"A multiplicative cascade model for high-resolution space-time downscaling of rainfall.\" J. Geophys. Res. Atmos., 123(4), 2050–2067. [DOI](https://doi.org/10.1002/2017JD027148)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pyart", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 5401b353abedcc442dba20ad0f671fbb768603b0 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Tue, 12 Dec 2023 19:06:50 -0600 Subject: [PATCH 28/54] FORMAT:Black --- pyart/retrieve/echo_class.py | 38 +++++++++++++++++++++++------------- 1 file changed, 24 insertions(+), 14 deletions(-) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 829059470f..059d759b94 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -12,6 +12,7 @@ from ._echo_class_wt import get_reclass from ..core import Grid + def steiner_conv_strat( grid, dx=None, @@ -981,10 +982,9 @@ def get_freq_band(freq): return None - def conv_strat_raut( grid, - refl_field='reflectivity', + refl_field="reflectivity", cappi_level=0, zr_a=200, zr_b=1.6, @@ -994,10 +994,10 @@ def conv_strat_raut( min_dbz_threshold=5, conv_dbz_threshold=25, conv_core_threshold=42, - override_checks=False + override_checks=False, ): """ - Classifies radar echoes into convective cores, mixed convection, and stratiform regions using the ATWT algorithm. + A fast method to classify radar echoes into convective cores, mixed convection, and stratiform regions using the ATWT algorithm This function applies the ATWT (A Trous Wavelet Transform) algorithm from Raut et al (2008) to classify radar echoes using the scheme of Raut et al (2020). It differentiates between convective and stratiform precipitation, @@ -1037,8 +1037,8 @@ def conv_strat_raut( Reflectivity threshold to identify convective cores. Default is 42 dBZ. Recommended value must be is greater than or equal to 40 dBZ. The algorithm is not sensitive to this value. override_checks : bool, optional - If set to True, the function will bypass the sanity checks for parameter values. - This allows the user to use custom values for parameters, even if they fall outside + If set to True, the function will bypass the sanity checks for parameter values. + This allows the user to use custom values for parameters, even if they fall outside the recommended or default ranges. The default is False. Returns @@ -1070,14 +1070,24 @@ def conv_strat_raut( # Sanity checks for parameters if override_checks is False if not override_checks: - conv_core_threshold = max(40, conv_core_threshold) # Ensure conv_core_threshold is at least 40 dBZ - conv_wt_threshold = max(4, min(conv_wt_threshold, 6)) # conv_wt_threshold should be between 4 and 6 - tran_wt_threshold = max(1, min(tran_wt_threshold, 2)) # tran_wt_threshold should be between 1 and 2 - conv_scale_km = max(15, min(conv_scale_km, 30)) # conv_scale_km should be between 15 and 30 km - min_dbz_threshold = max(0, min_dbz_threshold) # min_dbz_threshold should be non-negative - conv_dbz_threshold = max(25, min(conv_dbz_threshold, 30)) # conv_dbz_threshold should be between 25 and 30 dBZ - - + conv_core_threshold = max( + 40, conv_core_threshold + ) # Ensure conv_core_threshold is at least 40 dBZ + conv_wt_threshold = max( + 4, min(conv_wt_threshold, 6) + ) # conv_wt_threshold should be between 4 and 6 + tran_wt_threshold = max( + 1, min(tran_wt_threshold, 2) + ) # tran_wt_threshold should be between 1 and 2 + conv_scale_km = max( + 15, min(conv_scale_km, 30) + ) # conv_scale_km should be between 15 and 30 km + min_dbz_threshold = max( + 0, min_dbz_threshold + ) # min_dbz_threshold should be non-negative + conv_dbz_threshold = max( + 25, min(conv_dbz_threshold, 30) + ) # conv_dbz_threshold should be between 25 and 30 dBZ # Call the actual get_relass function to obtain radar echo classificatino reclass = get_reclass( From ba25c6401caea0682528f2bafba016d02e268cf5 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Fri, 15 Dec 2023 13:05:59 -0600 Subject: [PATCH 29/54] FIX: issues for #1495 the code review - aded TypeError handling in `_echo_class_wt.py` - Left tab docstring in `echo_class.py` - Added blank line in `_echo_class_wt.py` - Did not check `reflectivity_to_rainrate` function for duplication - Spellcheck Done - Confirmed grid object check in `echo_class.py` - Added `#noqa` comment in `__init__.py` --- pyart/retrieve/__init__.py | 2 +- pyart/retrieve/_echo_class_wt.py | 24 ++---- pyart/retrieve/echo_class.py | 132 +++++++++++++++--------------- tests/retrieve/test_echo_class.py | 9 +- 4 files changed, 79 insertions(+), 88 deletions(-) diff --git a/pyart/retrieve/__init__.py b/pyart/retrieve/__init__.py index bd2354c19e..f78029876b 100644 --- a/pyart/retrieve/__init__.py +++ b/pyart/retrieve/__init__.py @@ -10,7 +10,7 @@ from .echo_class import get_freq_band # noqa from .echo_class import hydroclass_semisupervised # noqa from .echo_class import steiner_conv_strat # noqa -from .echo_class import conv_strat_raut +from .echo_class import conv_strat_raut #noqa from .gate_id import fetch_radar_time_profile, map_profile_to_gates # noqa from .kdp_proc import kdp_maesaka, kdp_schneebeli, kdp_vulpiani # noqa from .qpe import est_rain_rate_a # noqa diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index 23bc29d7a1..dbeb61a379 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -13,8 +13,10 @@ calc_scale_break sum_conv_wavelets atwt2d + """ + import numpy as np from numpy import log, floor import sys @@ -36,7 +38,7 @@ def get_reclass( """ Compute ATWT described as Raut et al (2008) and classify radar echoes using scheme of Raut et al (2020). - First, convert dBZ to rain rates using standard Z-R relationship or user given coefiecient. This is to + First, convert dBZ to rain rates using standard Z-R relationship or user given coefficients. This is to transform the normally distributed dBZ to gamma-like distribution, enhancing the structure of the field. Parameters: @@ -64,7 +66,7 @@ def get_reclass( radar_mask = np.ma.getmask(dbz_data) - # Warning: dx and dy are considred to be same (res_km). + # Warning: dx and dy are considered to be same (res_km). res_km = (grid.x["data"][1] - grid.x["data"][0]) / 1000 try: @@ -114,7 +116,7 @@ def label_classes( - 2: Transitional and mixed convective regions - 3: Convective cores - Following hard coded values are optimised and validated using C-band radars + Following hard coded values are optimized and validated using C-band radars over Darwin, Australia (2.5 km grid spacing) and tested for Solapur, India (1km grid spacing) [Raut et al. 2020]. conv_wt_threshold = 5 # WT value more than this is strong convection tran_wt_threshold = 2 # WT value for moderate convection @@ -218,21 +220,8 @@ def sum_conv_wavelets(vol_data, conv_scale): """ dims = vol_data.shape - # if data is 2d - # if len(dims) == 2 or any(dims==1): wt, bg = atwt2d(vol_data, max_scale=conv_scale) wt_sum = np.sum(wt, axis=(0)) - """ else: # else for volume data - num_levels = min(dims) # too many assumptions here for height of the volume. - wt_sum = np.zeros(dims) - - for lev in range(num_levels): - if vol_data[:, :, lev].max < 1: - next() # this needs reviewing - wt = atwt2d(vol_data[lev, :, :], max_scale=conv_scale) - - # sum all the WT scales. - wt_sum[lev, :, :] = np.sum(wt, axis=(0)) """ # Only positive WT corresponds to convection in radar # wt_sum[wt_sum<0] = 0 @@ -266,7 +255,7 @@ def atwt2d(data2d, max_scale=-1): """ if not isinstance(data2d, np.ndarray): - sys.exit("the input is not a numpy array") + raise TypeError("The input data2d must be a numpy array.") data2d = data2d.squeeze() @@ -356,3 +345,4 @@ def atwt2d(data2d, max_scale=-1): data2d[:] = temp2 return wt, data2d + diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 059d759b94..37baa24b7d 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -997,74 +997,74 @@ def conv_strat_raut( override_checks=False, ): """ - A fast method to classify radar echoes into convective cores, mixed convection, and stratiform regions using the ATWT algorithm - - This function applies the ATWT (A Trous Wavelet Transform) algorithm from Raut et al (2008) to classify - radar echoes using the scheme of Raut et al (2020). It differentiates between convective and stratiform precipitation, - identifying convective cores, moderate/intermediate mixed convection, and stratiform regions - based on wavelet transform and reflectivity thresholds. - - Parameters - ---------- - grid : Grid - Grid object containing radar data. - refl_field : str - Field name for reflectivity data in the Py-ART grid object. - zr_a : float, optional - Coefficient 'a' in the Z-R relationship Z = a*R^b for reflectivity to rain rate conversion. - Default is 200. The algorithm is not sensitive to precise values of 'zr_a' and 'zr_b'; however, - they must be adjusted based on the type of radar used. - zr_b : float, optional - Coefficient 'b' in the Z-R relationship Z = a*R^b. Default is 1.6. - conv_wt_threshold : float, optional - Threshold for sum of small scale wavelet components to identify strong convection. - Default is 5. Recommended values are between 4 and 6. - tran_wt_threshold : float, optional - Threshold for sum of small scale wavelet components to identify moderate/intermediate mixed convection. - Default is 1.5. Recommended values are between 1 and 2. - conv_scale_km : float, optional - Approximate scale break (in km) between convective and stratiform scales. - Scale break may vary between 15 and 30 km over different regions and seasons; however, - the algorithm is not sensitive to small variations in the scale break. - Default is 20 km taken from Raut et al (2018). - min_dbz_threshold : float, optional - Minimum reflectivity threshold. Reflectivities below this value are not classified. - Default is 5 dBZ. This value must be greater than or equal to '0'. - conv_dbz_threshold : float, optional - Reflectivities below this threshold will not be considered to be classified as convective. Default is 25 dBZ. - Recommended values are between 25 and 30 dBZ. - conv_core_threshold : float, optional - Reflectivity threshold to identify convective cores. Default is 42 dBZ. - Recommended value must be is greater than or equal to 40 dBZ. The algorithm is not sensitive to this value. - override_checks : bool, optional - If set to True, the function will bypass the sanity checks for parameter values. - This allows the user to use custom values for parameters, even if they fall outside - the recommended or default ranges. The default is False. - - Returns - ------- - dict: - A dictionary structured as a Py-ART grid field, suitable for adding to a Py-ART Grid object. The dictionary - contains the classification data and associated metadata. The classification categories are as follows: - - 0: No precipitation or unclassified - - 1: Stratiform/non-convective regions - - 2: Transitional and mixed convective regions - - 3: Convective cores - - References - ---------- - Raut, B. A., Karekar, R. N., & Puranik, D. M. (2008). Wavelet-based technique to extract convective clouds from - infrared satellite images. IEEE Geoscience and remote sensing letters, 5(3), 328-330. - - Raut, B. A., Seed, A. W., Reeder, M. J., & Jakob, C. (2018). A multiplicative cascade model for high‐resolution - space‐time downscaling of rainfall. Journal of Geophysical Research: Atmospheres, 123(4), 2050-2067. - - Raut, B. A., Louf, V., Gayatri, K., Murugavel, P., Konwar, M., & Prabhakaran, T. (2020). A multiresolution technique - for the classification of precipitation echoes in radar data. IEEE Transactions on Geoscience and Remote Sensing, - 58(8), 5409-5415. + A fast method to classify radar echoes into convective cores, mixed convection, and stratiform regions using the ATWT algorithm + + This function applies the ATWT (A Trous Wavelet Transform) algorithm from Raut et al (2008) to classify + radar echoes using the scheme of Raut et al (2020). It differentiates between convective and stratiform precipitation, + identifying convective cores, moderate/intermediate mixed convection, and stratiform regions + based on wavelet transform and reflectivity thresholds. + + Parameters + ---------- + grid : Grid + Grid object containing radar data. + refl_field : str + Field name for reflectivity data in the Py-ART grid object. + zr_a : float, optional + Coefficient 'a' in the Z-R relationship Z = a*R^b for reflectivity to rain rate conversion. + Default is 200. The algorithm is not sensitive to precise values of 'zr_a' and 'zr_b'; however, + they must be adjusted based on the type of radar used. + zr_b : float, optional + Coefficient 'b' in the Z-R relationship Z = a*R^b. Default is 1.6. + conv_wt_threshold : float, optional + Threshold for sum of small scale wavelet components to identify strong convection. + Default is 5. Recommended values are between 4 and 6. + tran_wt_threshold : float, optional + Threshold for sum of small scale wavelet components to identify moderate/intermediate mixed convection. + Default is 1.5. Recommended values are between 1 and 2. + conv_scale_km : float, optional + Approximate scale break (in km) between convective and stratiform scales. + Scale break may vary between 15 and 30 km over different regions and seasons; however, + the algorithm is not sensitive to small variations in the scale break. + Default is 20 km taken from Raut et al (2018). + min_dbz_threshold : float, optional + Minimum reflectivity threshold. Reflectivities below this value are not classified. + Default is 5 dBZ. This value must be greater than or equal to '0'. + conv_dbz_threshold : float, optional + Reflectivities below this threshold will not be considered to be classified as convective. Default is 25 dBZ. + Recommended values are between 25 and 30 dBZ. + conv_core_threshold : float, optional + Reflectivity threshold to identify convective cores. Default is 42 dBZ. + Recommended value must be is greater than or equal to 40 dBZ. The algorithm is not sensitive to this value. + override_checks : bool, optional + If set to True, the function will bypass the sanity checks for parameter values. + This allows the user to use custom values for parameters, even if they fall outside + the recommended or default ranges. The default is False. + + Returns +------- + dict: + A dictionary structured as a Py-ART grid field, suitable for adding to a Py-ART Grid object. The dictionary + contains the classification data and associated metadata. The classification categories are as follows: + - 0: No precipitation or unclassified + - 1: Stratiform/non-convective regions + - 2: Transitional and mixed convective regions + - 3: Convective cores + + References + ---------- + Raut, B. A., Karekar, R. N., & Puranik, D. M. (2008). Wavelet-based technique to extract convective clouds from + infrared satellite images. IEEE Geoscience and remote sensing letters, 5(3), 328-330. + + Raut, B. A., Seed, A. W., Reeder, M. J., & Jakob, C. (2018). A multiplicative cascade model for high‐resolution + space‐time downscaling of rainfall. Journal of Geophysical Research: Atmospheres, 123(4), 2050-2067. + + Raut, B. A., Louf, V., Gayatri, K., Murugavel, P., Konwar, M., & Prabhakaran, T. (2020). A multiresolution technique + for the classification of precipitation echoes in radar data. IEEE Transactions on Geoscience and Remote Sensing, + 58(8), 5409-5415. """ - # I don't know how to Check if the grid is a Py-ART Grid object + # Check if the grid is a Py-ART Grid object if not isinstance(grid, Grid): raise TypeError("The 'grid' is not a Py-ART Grid object.") diff --git a/tests/retrieve/test_echo_class.py b/tests/retrieve/test_echo_class.py index 6c1f720658..afaf762110 100644 --- a/tests/retrieve/test_echo_class.py +++ b/tests/retrieve/test_echo_class.py @@ -320,12 +320,12 @@ def test_conv_strat_raut_outDict_valid(): gaussian_storm_2d, "reflectivity", cappi_level=0 ) - # First check that it's a pthon dictionary + # First check that it's a Python dictionary assert isinstance(wtclass, dict), "Output is not a dictionary" # then test 'wt_reclass' key exists in the dict assert "wt_reclass" in wtclass.keys() - # Now test other expectd expected keys + # Now test other expected keys expected_keys = [ "data", "standard_name", @@ -351,7 +351,7 @@ def test_conv_strat_raut_results_correct(): """ Checks the correctness of the results from the function. - I created a fixed Gaussian storm with masked boundaries as pyart grid and classifed it. + I created a fixed Gaussian storm with masked boundaries as pyart grid and classified it. Then constructed manually the expected classification results and compared it to the actual output from the function. """ @@ -383,7 +383,7 @@ def test_conv_strat_raut_results_correct(): # Define the center and create the 4x4 area center = grid_len // 2 - # these are actual rsults from sucessful run + # these are actual results from successful run test_reclass[center - 3 : center + 3, center - 3 : center + 3] = 2 test_reclass[center - 2 : center + 2, center - 2 : center + 2] = 3 @@ -398,3 +398,4 @@ def test_conv_strat_raut_results_correct(): masked_reclass = np.expand_dims(masked_reclass, axis=0) assert_allclose(masked_reclass, wtclass["wt_reclass"]["data"]) + From cdc28d7351b68614513e5e34f956a618f3a747b2 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Fri, 15 Dec 2023 13:37:16 -0600 Subject: [PATCH 30/54] FIX:unused imports andvariables removed --- pyart/retrieve/__init__.py | 2 +- pyart/retrieve/_echo_class_wt.py | 17 +++++++---------- tests/testing/test_sample_objects.py | 8 -------- 3 files changed, 8 insertions(+), 19 deletions(-) diff --git a/pyart/retrieve/__init__.py b/pyart/retrieve/__init__.py index f78029876b..f189584fff 100644 --- a/pyart/retrieve/__init__.py +++ b/pyart/retrieve/__init__.py @@ -10,7 +10,7 @@ from .echo_class import get_freq_band # noqa from .echo_class import hydroclass_semisupervised # noqa from .echo_class import steiner_conv_strat # noqa -from .echo_class import conv_strat_raut #noqa +from .echo_class import conv_strat_raut #noqa from .gate_id import fetch_radar_time_profile, map_profile_to_gates # noqa from .kdp_proc import kdp_maesaka, kdp_schneebeli, kdp_vulpiani # noqa from .qpe import est_rain_rate_a # noqa diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index dbeb61a379..38df0a75cf 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -19,7 +19,6 @@ import numpy as np from numpy import log, floor -import sys def get_reclass( @@ -36,8 +35,7 @@ def get_reclass( conv_core_threshold, ): """ - Compute ATWT described as Raut et al (2008) and classify radar echoes - using scheme of Raut et al (2020). + Compute ATWT described as Raut et al (2008) and classify radar echoes using scheme of Raut et al (2020). First, convert dBZ to rain rates using standard Z-R relationship or user given coefficients. This is to transform the normally distributed dBZ to gamma-like distribution, enhancing the structure of the field. @@ -58,7 +56,7 @@ def get_reclass( regions. """ - # Extract grid data, save mask and get the resolution + # Extract grid data, save mask and get the resolution in km try: dbz_data = grid.fields[refl_field]["data"][level, :, :] except: @@ -69,18 +67,19 @@ def get_reclass( # Warning: dx and dy are considered to be same (res_km). res_km = (grid.x["data"][1] - grid.x["data"][0]) / 1000 + # In case it's a masked array. try: - dbz_data = dbz_data.filled(0) # In case it's a masked array. + dbz_data = dbz_data.filled(0) except Exception: pass - # save the mask for missing data. + # save the radar original mask for missing data. dbz_data[np.isnan(dbz_data)] = 0 dbz_data_t = reflectivity_to_rainrate( dbz_data, acoeff=zr_a, bcoeff=zr_b - ) # transform the dbz data + ) # transform the dbz data to rainrate - # get scale break in pixels + # get scale break in pixels or grid size scale_break = calc_scale_break(res_km, conv_scale_km) wt_sum = sum_conv_wavelets(dbz_data_t, scale_break) @@ -218,7 +217,6 @@ def sum_conv_wavelets(vol_data, conv_scale): wt_sum: ndarray Integrated wavelet transform. """ - dims = vol_data.shape wt, bg = atwt2d(vol_data, max_scale=conv_scale) wt_sum = np.sum(wt, axis=(0)) @@ -345,4 +343,3 @@ def atwt2d(data2d, max_scale=-1): data2d[:] = temp2 return wt, data2d - diff --git a/tests/testing/test_sample_objects.py b/tests/testing/test_sample_objects.py index 517ad8b9ea..d35289d210 100644 --- a/tests/testing/test_sample_objects.py +++ b/tests/testing/test_sample_objects.py @@ -1,10 +1,6 @@ """ Unit Tests for Py-ART's testing/sample_objects.py module. """ import numpy as np -#import pytest -from numpy.testing import assert_allclose - -import pyart from pyart.testing.sample_objects import make_gaussian_storm_grid @@ -19,12 +15,8 @@ def test_gaussian_storm_grid_results_correct(): grid_len = 32 min_value = 5 max_value = 45 - sigma = 0.2 - mu = 0.0 mask_margin = 3 - expected_limits = ((1000, 1000), (-grid_len*1000/2, grid_len*1000/2), (-grid_len*1000/2, grid_len*1000/2)) - # Create grid gaussian_storm_2d = make_gaussian_storm_grid() From ee0ea00de5dda08ca71befcee8fca04cb7e0907f Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Fri, 15 Dec 2023 15:29:53 -0600 Subject: [PATCH 31/54] Removed two line func sum_conv_wavelets(); --- pyart/retrieve/_echo_class_wt.py | 31 ++++--------------------------- 1 file changed, 4 insertions(+), 27 deletions(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index 38df0a75cf..984d5e3cd8 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -81,7 +81,10 @@ def get_reclass( # get scale break in pixels or grid size scale_break = calc_scale_break(res_km, conv_scale_km) - wt_sum = sum_conv_wavelets(dbz_data_t, scale_break) + + # Compute and sum convective scale WT components + wt, _ = atwt2d(dbz_data_t, max_scale=scale_break) + wt_sum = np.sum(wt, axis=(0)) wt_class = label_classes( wt_sum, @@ -200,32 +203,6 @@ def calc_scale_break(res_km, conv_scale_km): return int(round(scale_break)) -def sum_conv_wavelets(vol_data, conv_scale): - """ - Returns sum of WT upto given scale. Works with both 2d scans and - volumetric data. - - Parameters: - =========== - vol_data: ndarray - Array, vector or matrix of data. - conv_scale: float - Expected size of spatial variations due to convection. - - Returns: - ======== - wt_sum: ndarray - Integrated wavelet transform. - """ - - wt, bg = atwt2d(vol_data, max_scale=conv_scale) - wt_sum = np.sum(wt, axis=(0)) - - # Only positive WT corresponds to convection in radar - # wt_sum[wt_sum<0] = 0 - return wt_sum - - def atwt2d(data2d, max_scale=-1): """ Computes a trous wavelet transform (ATWT). Computes ATWT of the 2d array From edec4bf4b2c6394c5eb4df8c73ae8996acdfa4d0 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Fri, 15 Dec 2023 15:30:39 -0600 Subject: [PATCH 32/54] minor --- pyart/retrieve/_echo_class_wt.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index 984d5e3cd8..6ced0705ba 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -11,9 +11,7 @@ label_classes reflectivity_to_rainrate calc_scale_break - sum_conv_wavelets atwt2d - """ From 09ec0548fa87bd6236638a63066863ac8e5ac4f4 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Fri, 15 Dec 2023 15:46:47 -0600 Subject: [PATCH 33/54] Removed reflectivity_to_rainrate() --- pyart/retrieve/_echo_class_wt.py | 30 ++++++------------------------ 1 file changed, 6 insertions(+), 24 deletions(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index 6ced0705ba..cca0490c1f 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -73,15 +73,19 @@ def get_reclass( # save the radar original mask for missing data. dbz_data[np.isnan(dbz_data)] = 0 + dbz_data_t = reflectivity_to_rainrate( dbz_data, acoeff=zr_a, bcoeff=zr_b - ) # transform the dbz data to rainrate + ) + + # transform the dbz data to rainrate + rr_data = ((10.0 ** (dbz_data / 10.0)) / zr_a) ** (1.0 / zr_b) # get scale break in pixels or grid size scale_break = calc_scale_break(res_km, conv_scale_km) # Compute and sum convective scale WT components - wt, _ = atwt2d(dbz_data_t, max_scale=scale_break) + wt, _ = atwt2d(rr_data, max_scale=scale_break) wt_sum = np.sum(wt, axis=(0)) wt_class = label_classes( @@ -158,28 +162,6 @@ def label_classes( return wt_class.astype(np.int32) -def reflectivity_to_rainrate(dbz, acoeff, bcoeff): - """ - Uses standard values for ZRA=200 and ZRB=1.6. - - Parameters: - =========== - dbz: ndarray - Array, vector or matrix of reflectivity in dBZ. - acoeff: float - Z = a*R^b a coefficient. - bcoeff: float - Z = a*R^b b coefficient. - - Returns: - ======== - rr: ndarray - Rain rate in (mm/h) - """ - rr = ((10.0 ** (dbz / 10.0)) / acoeff) ** (1.0 / bcoeff) - return rr - - def calc_scale_break(res_km, conv_scale_km): """ Compute scale break for convection and stratiform regions. WT will be From 4d78ca640654d7e6c9b578bb7a3a94963cc99160 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Fri, 15 Dec 2023 15:50:14 -0600 Subject: [PATCH 34/54] Test PAssed reflectivity_to_rainrate(): --- pyart/retrieve/_echo_class_wt.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index cca0490c1f..dcd764aeb9 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -74,10 +74,6 @@ def get_reclass( # save the radar original mask for missing data. dbz_data[np.isnan(dbz_data)] = 0 - dbz_data_t = reflectivity_to_rainrate( - dbz_data, acoeff=zr_a, bcoeff=zr_b - ) - # transform the dbz data to rainrate rr_data = ((10.0 ** (dbz_data / 10.0)) / zr_a) ** (1.0 / zr_b) From 3eec39f586d6928fcd867d754714b68cee212ce8 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Fri, 15 Dec 2023 15:53:40 -0600 Subject: [PATCH 35/54] minor:autosummary --- pyart/retrieve/_echo_class_wt.py | 1 - 1 file changed, 1 deletion(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index dcd764aeb9..9a1a9ae968 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -9,7 +9,6 @@ .. autosummary:: get_reclass label_classes - reflectivity_to_rainrate calc_scale_break atwt2d """ From 76fb4419ef968905cbf48f93bd0de6df7e87fda9 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Fri, 15 Dec 2023 16:11:49 -0600 Subject: [PATCH 36/54] ADD:conv_wavelet_sum() --- pyart/retrieve/_echo_class_wt.py | 58 +++++++++++++++++++++----------- 1 file changed, 38 insertions(+), 20 deletions(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index 9a1a9ae968..29677e2eeb 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -39,7 +39,7 @@ def get_reclass( Parameters: =========== dbz_data: ndarray - 3D array containing radar data. Last dimension should be levels. + 2D array containing radar data. Last dimension should be levels. res_km: float Resolution of the radar data in km conv_scale_km: float @@ -59,29 +59,13 @@ def get_reclass( except: dbz_data = grid.fields[refl_field]["data"][:, :] + # save the radar original mask for missing data. radar_mask = np.ma.getmask(dbz_data) # Warning: dx and dy are considered to be same (res_km). res_km = (grid.x["data"][1] - grid.x["data"][0]) / 1000 - # In case it's a masked array. - try: - dbz_data = dbz_data.filled(0) - except Exception: - pass - - # save the radar original mask for missing data. - dbz_data[np.isnan(dbz_data)] = 0 - - # transform the dbz data to rainrate - rr_data = ((10.0 ** (dbz_data / 10.0)) / zr_a) ** (1.0 / zr_b) - - # get scale break in pixels or grid size - scale_break = calc_scale_break(res_km, conv_scale_km) - - # Compute and sum convective scale WT components - wt, _ = atwt2d(rr_data, max_scale=scale_break) - wt_sum = np.sum(wt, axis=(0)) + wt_sum = conv_wavelet_sum(dbz_data, zr_a, zr_b, res_km, conv_scale_km) wt_class = label_classes( wt_sum, @@ -99,6 +83,40 @@ def get_reclass( return wt_class_ma +def conv_wavelet_sum(dbz_data, zr_a, zr_b, res_km, conv_scale_km): + """ + Computes the sum of wavelet transform components for convective scales from dBZ data. + + Parameters: + =========== + dbz_data: ndarray + 2D array containing radar dBZ data. + zr_a, zr_b: float + Coefficients for the Z-R relationship. + res_km: float + Resolution of the radar data in km. + conv_scale_km: float + Approximate scale break (in km) between convective and stratiform scales. + + Returns: + ======== + wt_sum: ndarray + Sum of convective scale wavelet transform components. + """ + try: + dbz_data = dbz_data.filled(0) + except Exception: + pass + + dbz_data[np.isnan(dbz_data)] = 0 + rr_data = ((10.0 ** (dbz_data / 10.0)) / zr_a) ** (1.0 / zr_b) + scale_break = calc_scale_break(res_km, conv_scale_km) + wt, _ = atwt2d(rr_data, max_scale=scale_break) + wt_sum = np.sum(wt, axis=(0)) + + return wt_sum + + def label_classes( wt_sum, dbz_data, @@ -180,7 +198,7 @@ def calc_scale_break(res_km, conv_scale_km): def atwt2d(data2d, max_scale=-1): """ - Computes a trous wavelet transform (ATWT). Computes ATWT of the 2d array + Computes a trous wavelet transform (ATWT). Computes ATWT of the 2D array up to max_scale. If max_scale is outside the boundaries, number of scales will be reduced. From c3e03fa18d59653af8b8b0db9c37b3bd83b0834d Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Fri, 15 Dec 2023 16:37:11 -0600 Subject: [PATCH 37/54] Func Renamed: get_reclass -> wavelet_reclas --- pyart/retrieve/_echo_class_wt.py | 5 ++--- pyart/retrieve/echo_class.py | 6 +++--- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index 29677e2eeb..3cc2fffd64 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -7,7 +7,7 @@ @references: 10.1109/TGRS.2020.2965649 .. autosummary:: - get_reclass + wavelet_reclass label_classes calc_scale_break atwt2d @@ -17,8 +17,7 @@ import numpy as np from numpy import log, floor - -def get_reclass( +def wavelet_reclass( grid, refl_field, level, diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 37baa24b7d..18ad1db3e7 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -9,7 +9,7 @@ from ..config import get_field_name, get_fillvalue, get_metadata from ._echo_class import _feature_detection, steiner_class_buff -from ._echo_class_wt import get_reclass +from ._echo_class_wt import wavelet_reclass from ..core import Grid @@ -1089,8 +1089,8 @@ def conv_strat_raut( 25, min(conv_dbz_threshold, 30) ) # conv_dbz_threshold should be between 25 and 30 dBZ - # Call the actual get_relass function to obtain radar echo classificatino - reclass = get_reclass( + # Call the actual wavelet_relass function to obtain radar echo classificatino + reclass = wavelet_reclass( grid, refl_field, cappi_level, From c0bc43d80a1777b7ee00435b15e31eb150be8b0f Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Sat, 16 Dec 2023 13:30:18 -0600 Subject: [PATCH 38/54] Refined documentation and Descriptive user arguments --- pyart/retrieve/_echo_class_wt.py | 38 +++++++-------- pyart/retrieve/echo_class.py | 79 ++++++++++++++++++-------------- 2 files changed, 64 insertions(+), 53 deletions(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index 3cc2fffd64..ba133edbbc 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -23,11 +23,11 @@ def wavelet_reclass( level, zr_a, zr_b, + core_wt_threshold, conv_wt_threshold, - tran_wt_threshold, conv_scale_km, - min_dbz_threshold, - conv_dbz_threshold, + min_reflectivity, + conv_min_refl, conv_core_threshold, ): """ @@ -69,10 +69,10 @@ def wavelet_reclass( wt_class = label_classes( wt_sum, dbz_data, + core_wt_threshold, conv_wt_threshold, - tran_wt_threshold, - min_dbz_threshold, - conv_dbz_threshold, + min_reflectivity, + conv_min_refl, conv_core_threshold, ) @@ -119,10 +119,10 @@ def conv_wavelet_sum(dbz_data, zr_a, zr_b, res_km, conv_scale_km): def label_classes( wt_sum, dbz_data, + core_wt_threshold, conv_wt_threshold, - tran_wt_threshold, - min_dbz_threshold, - conv_dbz_threshold, + min_reflectivity, + conv_min_refl, conv_core_threshold, ): """ @@ -134,10 +134,10 @@ def label_classes( Following hard coded values are optimized and validated using C-band radars over Darwin, Australia (2.5 km grid spacing) and tested for Solapur, India (1km grid spacing) [Raut et al. 2020]. - conv_wt_threshold = 5 # WT value more than this is strong convection - tran_wt_threshold = 2 # WT value for moderate convection - min_dbz_threshold = 10 # pixels below this value are not classified. - conv_dbz_threshold = 30 # pixel below this value are not convective. This works for most cases. + core_wt_threshold = 5 # WT value more than this is strong convection + conv_wt_threshold = 2 # WT value for moderate convection + min_reflectivity = 10 # pixels below this value are not classified. + conv_min_refl = 30 # pixel below this value are not convective. This works for most cases. Parameters: =========== @@ -154,19 +154,19 @@ def label_classes( # I first used negative numbers to annotate the categories. Then multiply it by -1. wt_class = np.where( - (wt_sum >= tran_wt_threshold) & (dbz_data >= conv_core_threshold), -3, 0 + (wt_sum >= conv_wt_threshold) & (dbz_data >= conv_core_threshold), -3, 0 ) wt_class = np.where( - (wt_sum >= conv_wt_threshold) & (dbz_data >= conv_dbz_threshold), -3, 0 + (wt_sum >= core_wt_threshold) & (dbz_data >= conv_min_refl), -3, 0 ) wt_class = np.where( - (wt_sum < conv_wt_threshold) - & (wt_sum >= tran_wt_threshold) - & (dbz_data >= conv_dbz_threshold), + (wt_sum < core_wt_threshold) + & (wt_sum >= conv_wt_threshold) + & (dbz_data >= conv_min_refl), -2, wt_class, ) - wt_class = np.where((wt_class == 0) & (dbz_data >= min_dbz_threshold), -1, wt_class) + wt_class = np.where((wt_class == 0) & (dbz_data >= min_reflectivity), -1, wt_class) wt_class = -1 * wt_class wt_class = np.where((wt_class == 0), np.nan, wt_class) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 18ad1db3e7..af39f62bf4 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -988,21 +988,22 @@ def conv_strat_raut( cappi_level=0, zr_a=200, zr_b=1.6, - conv_wt_threshold=5, - tran_wt_threshold=1.5, + core_wt_threshold=5, + conv_wt_threshold=1.5, conv_scale_km=20, - min_dbz_threshold=5, - conv_dbz_threshold=25, + min_reflectivity=5, + conv_min_refl=25, conv_core_threshold=42, override_checks=False, ): """ - A fast method to classify radar echoes into convective cores, mixed convection, and stratiform regions using the ATWT algorithm + A fast method to classify radar echoes into convective cores, mixed convection, and stratiform regions using wavelets. This function applies the ATWT (A Trous Wavelet Transform) algorithm from Raut et al (2008) to classify radar echoes using the scheme of Raut et al (2020). It differentiates between convective and stratiform precipitation, identifying convective cores, moderate/intermediate mixed convection, and stratiform regions - based on wavelet transform and reflectivity thresholds. + based on intensity of wavelet components. The method is less sensitive to the refelectivity thresholds and primarily considers + the scale and structure of the precipitation for classification. Parameters ---------- @@ -1016,30 +1017,31 @@ def conv_strat_raut( they must be adjusted based on the type of radar used. zr_b : float, optional Coefficient 'b' in the Z-R relationship Z = a*R^b. Default is 1.6. - conv_wt_threshold : float, optional - Threshold for sum of small scale wavelet components to identify strong convection. + core_wt_threshold : float, optional + Threshold for wavelet components to separate convective cores from mix-intermediate type. Default is 5. Recommended values are between 4 and 6. - tran_wt_threshold : float, optional - Threshold for sum of small scale wavelet components to identify moderate/intermediate mixed convection. + conv_wt_threshold : float, optional + Threshold for significant wavelet components to separate all convection from stratiform. Default is 1.5. Recommended values are between 1 and 2. conv_scale_km : float, optional Approximate scale break (in km) between convective and stratiform scales. Scale break may vary between 15 and 30 km over different regions and seasons; however, the algorithm is not sensitive to small variations in the scale break. Default is 20 km taken from Raut et al (2018). - min_dbz_threshold : float, optional + min_reflectivity : float, optional Minimum reflectivity threshold. Reflectivities below this value are not classified. Default is 5 dBZ. This value must be greater than or equal to '0'. - conv_dbz_threshold : float, optional - Reflectivities below this threshold will not be considered to be classified as convective. Default is 25 dBZ. - Recommended values are between 25 and 30 dBZ. + conv_min_refl : float, optional + Reflectivity values lower than this threshold won't be categorized as convective. + Default is 25 dBZ. Recommended values are between 25 and 30 dBZ. conv_core_threshold : float, optional - Reflectivity threshold to identify convective cores. Default is 42 dBZ. + Reflectivities above this threshold are classified as convective cores if wavelet components are significant (See: conv_wt_threshold). + Default is 42 dBZ. Recommended value must be is greater than or equal to 40 dBZ. The algorithm is not sensitive to this value. override_checks : bool, optional - If set to True, the function will bypass the sanity checks for parameter values. + If set to True, the function will bypass the sanity checks for above parameter values. This allows the user to use custom values for parameters, even if they fall outside - the recommended or default ranges. The default is False. + the recommended ranges. The default is False. Returns ------- @@ -1067,27 +1069,36 @@ def conv_strat_raut( # Check if the grid is a Py-ART Grid object if not isinstance(grid, Grid): raise TypeError("The 'grid' is not a Py-ART Grid object.") + + # Check if dx and dy are the same, and warn if not + dx = (grid.x["data"][1] - grid.x["data"][0]) / 1000 + dy = (grid.y["data"][1] - grid.y["data"][0]) / 1000 + if dx != dy: + warn( + "Warning: Grid resolution `dx` and `dy` should be comparable for correct results.", + UserWarning + ) # Sanity checks for parameters if override_checks is False if not override_checks: conv_core_threshold = max( 40, conv_core_threshold ) # Ensure conv_core_threshold is at least 40 dBZ + core_wt_threshold = max( + 4, min(core_wt_threshold, 6) + ) # core_wt_threshold should be between 4 and 6 conv_wt_threshold = max( - 4, min(conv_wt_threshold, 6) - ) # conv_wt_threshold should be between 4 and 6 - tran_wt_threshold = max( - 1, min(tran_wt_threshold, 2) - ) # tran_wt_threshold should be between 1 and 2 + 1, min(conv_wt_threshold, 2) + ) # conv_wt_threshold should be between 1 and 2 conv_scale_km = max( 15, min(conv_scale_km, 30) ) # conv_scale_km should be between 15 and 30 km - min_dbz_threshold = max( - 0, min_dbz_threshold - ) # min_dbz_threshold should be non-negative - conv_dbz_threshold = max( - 25, min(conv_dbz_threshold, 30) - ) # conv_dbz_threshold should be between 25 and 30 dBZ + min_reflectivity = max( + 0, min_reflectivity + ) # min_reflectivity should be non-negative + conv_min_refl = max( + 25, min(conv_min_refl, 30) + ) #conv_min_refl should be between 25 and 30 dBZ # Call the actual wavelet_relass function to obtain radar echo classificatino reclass = wavelet_reclass( @@ -1096,11 +1107,11 @@ def conv_strat_raut( cappi_level, zr_a, zr_b, + core_wt_threshold=core_wt_threshold, conv_wt_threshold=conv_wt_threshold, - tran_wt_threshold=tran_wt_threshold, conv_scale_km=conv_scale_km, - min_dbz_threshold=min_dbz_threshold, - conv_dbz_threshold=conv_dbz_threshold, + min_reflectivity=min_reflectivity, + conv_min_refl=conv_min_refl, conv_core_threshold=conv_core_threshold, ) @@ -1120,11 +1131,11 @@ def conv_strat_raut( "cappi_level": cappi_level, "zr_a": zr_a, "zr_b": zr_b, + "core_wt_threshold": core_wt_threshold, "conv_wt_threshold": conv_wt_threshold, - "tran_wt_threshold": tran_wt_threshold, "conv_scale_km": conv_scale_km, - "min_dbz_threshold": min_dbz_threshold, - "conv_dbz_threshold": conv_dbz_threshold, + "min_reflectivity": min_reflectivity, + "conv_min_refl":conv_min_refl, "conv_core_threshold": conv_core_threshold, }, } From 7064af1af22214ccf8a8b4d845a6dbc5d32c9a72 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Sat, 16 Dec 2023 13:34:04 -0600 Subject: [PATCH 39/54] FMT: Black --- pyart/retrieve/_echo_class_wt.py | 9 +++++---- pyart/retrieve/echo_class.py | 12 ++++++------ 2 files changed, 11 insertions(+), 10 deletions(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index ba133edbbc..c7ba72e477 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -17,6 +17,7 @@ import numpy as np from numpy import log, floor + def wavelet_reclass( grid, refl_field, @@ -72,7 +73,7 @@ def wavelet_reclass( core_wt_threshold, conv_wt_threshold, min_reflectivity, - conv_min_refl, + conv_min_refl, conv_core_threshold, ) @@ -122,7 +123,7 @@ def label_classes( core_wt_threshold, conv_wt_threshold, min_reflectivity, - conv_min_refl, + conv_min_refl, conv_core_threshold, ): """ @@ -157,12 +158,12 @@ def label_classes( (wt_sum >= conv_wt_threshold) & (dbz_data >= conv_core_threshold), -3, 0 ) wt_class = np.where( - (wt_sum >= core_wt_threshold) & (dbz_data >= conv_min_refl), -3, 0 + (wt_sum >= core_wt_threshold) & (dbz_data >= conv_min_refl), -3, 0 ) wt_class = np.where( (wt_sum < core_wt_threshold) & (wt_sum >= conv_wt_threshold) - & (dbz_data >= conv_min_refl), + & (dbz_data >= conv_min_refl), -2, wt_class, ) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index af39f62bf4..4059dd68ec 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -1035,7 +1035,7 @@ def conv_strat_raut( Reflectivity values lower than this threshold won't be categorized as convective. Default is 25 dBZ. Recommended values are between 25 and 30 dBZ. conv_core_threshold : float, optional - Reflectivities above this threshold are classified as convective cores if wavelet components are significant (See: conv_wt_threshold). + Reflectivities above this threshold are classified as convective cores if wavelet components are significant (See: conv_wt_threshold). Default is 42 dBZ. Recommended value must be is greater than or equal to 40 dBZ. The algorithm is not sensitive to this value. override_checks : bool, optional @@ -1069,14 +1069,14 @@ def conv_strat_raut( # Check if the grid is a Py-ART Grid object if not isinstance(grid, Grid): raise TypeError("The 'grid' is not a Py-ART Grid object.") - + # Check if dx and dy are the same, and warn if not dx = (grid.x["data"][1] - grid.x["data"][0]) / 1000 dy = (grid.y["data"][1] - grid.y["data"][0]) / 1000 if dx != dy: warn( "Warning: Grid resolution `dx` and `dy` should be comparable for correct results.", - UserWarning + UserWarning, ) # Sanity checks for parameters if override_checks is False @@ -1098,7 +1098,7 @@ def conv_strat_raut( ) # min_reflectivity should be non-negative conv_min_refl = max( 25, min(conv_min_refl, 30) - ) #conv_min_refl should be between 25 and 30 dBZ + ) # conv_min_refl should be between 25 and 30 dBZ # Call the actual wavelet_relass function to obtain radar echo classificatino reclass = wavelet_reclass( @@ -1111,7 +1111,7 @@ def conv_strat_raut( conv_wt_threshold=conv_wt_threshold, conv_scale_km=conv_scale_km, min_reflectivity=min_reflectivity, - conv_min_refl=conv_min_refl, + conv_min_refl=conv_min_refl, conv_core_threshold=conv_core_threshold, ) @@ -1135,7 +1135,7 @@ def conv_strat_raut( "conv_wt_threshold": conv_wt_threshold, "conv_scale_km": conv_scale_km, "min_reflectivity": min_reflectivity, - "conv_min_refl":conv_min_refl, + "conv_min_refl": conv_min_refl, "conv_core_threshold": conv_core_threshold, }, } From e4efd93b094f0c362d833a60cb0a8eb25fff00f1 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Sat, 16 Dec 2023 14:12:11 -0600 Subject: [PATCH 40/54] ENH:Docstring --- pyart/retrieve/echo_class.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 4059dd68ec..7a7c1b8a55 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -997,13 +997,10 @@ def conv_strat_raut( override_checks=False, ): """ - A fast method to classify radar echoes into convective cores, mixed convection, and stratiform regions using wavelets. + A fast method to classify radar echoes into convective cores, mixed convection, and stratiform regions. - This function applies the ATWT (A Trous Wavelet Transform) algorithm from Raut et al (2008) to classify - radar echoes using the scheme of Raut et al (2020). It differentiates between convective and stratiform precipitation, - identifying convective cores, moderate/intermediate mixed convection, and stratiform regions - based on intensity of wavelet components. The method is less sensitive to the refelectivity thresholds and primarily considers - the scale and structure of the precipitation for classification. + This function uses à trous wavelet transform (ATWT) for multiresolution (scale) analysis of radar field, + focusing on precipitation structure over reflectivity thresholds for classification (Raut et al 2008, 2020). Parameters ---------- @@ -1013,8 +1010,9 @@ def conv_strat_raut( Field name for reflectivity data in the Py-ART grid object. zr_a : float, optional Coefficient 'a' in the Z-R relationship Z = a*R^b for reflectivity to rain rate conversion. - Default is 200. The algorithm is not sensitive to precise values of 'zr_a' and 'zr_b'; however, + The algorithm is not sensitive to precise values of 'zr_a' and 'zr_b'; however, they must be adjusted based on the type of radar used. + Default is 200. zr_b : float, optional Coefficient 'b' in the Z-R relationship Z = a*R^b. Default is 1.6. core_wt_threshold : float, optional @@ -1025,9 +1023,11 @@ def conv_strat_raut( Default is 1.5. Recommended values are between 1 and 2. conv_scale_km : float, optional Approximate scale break (in km) between convective and stratiform scales. - Scale break may vary between 15 and 30 km over different regions and seasons; however, - the algorithm is not sensitive to small variations in the scale break. - Default is 20 km taken from Raut et al (2018). + Scale break may vary between 15 and 30 km over different regions and seasons + (Refere to Raut et al 2018 for more discussion on scale-breaks). Note that the + algorithm is insensitive to small variations in the scale break due to the + dyadic nature of the scaling. + Default is 20 km. min_reflectivity : float, optional Minimum reflectivity threshold. Reflectivities below this value are not classified. Default is 5 dBZ. This value must be greater than or equal to '0'. From b491bc78db7cff7844584af0f52d48f27175c943 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Sat, 16 Dec 2023 15:36:25 -0600 Subject: [PATCH 41/54] DOC:Better description & ordering of classification categories --- pyart/retrieve/echo_class.py | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 7a7c1b8a55..b389da8a27 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -1023,7 +1023,7 @@ def conv_strat_raut( Default is 1.5. Recommended values are between 1 and 2. conv_scale_km : float, optional Approximate scale break (in km) between convective and stratiform scales. - Scale break may vary between 15 and 30 km over different regions and seasons + Scale break may vary between 15 and 30 km over different regions and seasons (Refere to Raut et al 2018 for more discussion on scale-breaks). Note that the algorithm is insensitive to small variations in the scale break due to the dyadic nature of the scaling. @@ -1032,7 +1032,7 @@ def conv_strat_raut( Minimum reflectivity threshold. Reflectivities below this value are not classified. Default is 5 dBZ. This value must be greater than or equal to '0'. conv_min_refl : float, optional - Reflectivity values lower than this threshold won't be categorized as convective. + Reflectivity values lower than this threshold will be always considered as non-convective. Default is 25 dBZ. Recommended values are between 25 and 30 dBZ. conv_core_threshold : float, optional Reflectivities above this threshold are classified as convective cores if wavelet components are significant (See: conv_wt_threshold). @@ -1045,25 +1045,26 @@ def conv_strat_raut( Returns ------- + dict: A dictionary structured as a Py-ART grid field, suitable for adding to a Py-ART Grid object. The dictionary contains the classification data and associated metadata. The classification categories are as follows: - - 0: No precipitation or unclassified - - 1: Stratiform/non-convective regions - - 2: Transitional and mixed convective regions - - 3: Convective cores + - 3: Convective Cores: associated with strong updrafts and active collision-coalescence. + - 2: Mixed-Intermediate: capturing a wide range of convective activities, excluding the convective cores. + - 1: Stratiform: remaining areas with more uniform and less intense precipitation. + - 0: Unclassified: for reflectivity below the minimum threshold. + References ---------- Raut, B. A., Karekar, R. N., & Puranik, D. M. (2008). Wavelet-based technique to extract convective clouds from - infrared satellite images. IEEE Geoscience and remote sensing letters, 5(3), 328-330. + infrared satellite images. IEEE Geosci. Remote Sens. Lett., 5(3), 328-330. Raut, B. A., Seed, A. W., Reeder, M. J., & Jakob, C. (2018). A multiplicative cascade model for high‐resolution - space‐time downscaling of rainfall. Journal of Geophysical Research: Atmospheres, 123(4), 2050-2067. + space‐time downscaling of rainfall. J. Geophys. Res. Atmos., 123(4), 2050-2067. Raut, B. A., Louf, V., Gayatri, K., Murugavel, P., Konwar, M., & Prabhakaran, T. (2020). A multiresolution technique - for the classification of precipitation echoes in radar data. IEEE Transactions on Geoscience and Remote Sensing, - 58(8), 5409-5415. + for the classification of precipitation echoes in radar data. IEEE Trans. Geosci. Remote Sens., 58(8), 5409-5415. """ # Check if the grid is a Py-ART Grid object @@ -1125,7 +1126,7 @@ def conv_strat_raut( "long_name": "Wavelet-based multiresolution radar echo classification", "valid_min": 0, "valid_max": 3, - "classification_description": "0: No precipitation or unclassified, 1: Stratiform/non-convective, 2: Mixed intermediate convection, 3: Convective cores", + "classification_description": "0: Unclassified, 1: Stratiform, 2: Mixed-Intermediate, 3: Convective Cores", "parameters": { "refl_field": refl_field, "cappi_level": cappi_level, From 114e99cf75b281cf47aed9af7de98e9fbdf0d33f Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Sun, 17 Dec 2023 20:34:39 -0600 Subject: [PATCH 42/54] REFACTOR:scale_break now computed in calling function Moved scale break calculation to calling function, outputting scale_break as a parameter for the user. Adjusted related functions and variables. --- pyart/retrieve/_echo_class_wt.py | 25 +++++++++++++------------ pyart/retrieve/echo_class.py | 14 ++++++++++---- 2 files changed, 23 insertions(+), 16 deletions(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index c7ba72e477..a7c5027fe6 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -26,7 +26,7 @@ def wavelet_reclass( zr_b, core_wt_threshold, conv_wt_threshold, - conv_scale_km, + scale_break, min_reflectivity, conv_min_refl, conv_core_threshold, @@ -42,8 +42,8 @@ def wavelet_reclass( 2D array containing radar data. Last dimension should be levels. res_km: float Resolution of the radar data in km - conv_scale_km: float - Approximate scale break (in km) between convective and stratiform scales + scale_break: int + Calculated scale break (in pixels) between convective and stratiform scales Returns: ======== @@ -62,10 +62,10 @@ def wavelet_reclass( # save the radar original mask for missing data. radar_mask = np.ma.getmask(dbz_data) - # Warning: dx and dy are considered to be same (res_km). - res_km = (grid.x["data"][1] - grid.x["data"][0]) / 1000 + # dx and dy are considered to be same (res_km). + res_meters = (grid.x["data"][1] - grid.x["data"][0]) - wt_sum = conv_wavelet_sum(dbz_data, zr_a, zr_b, res_km, conv_scale_km) + wt_sum = conv_wavelet_sum(dbz_data, zr_a, zr_b, scale_break) wt_class = label_classes( wt_sum, @@ -83,7 +83,7 @@ def wavelet_reclass( return wt_class_ma -def conv_wavelet_sum(dbz_data, zr_a, zr_b, res_km, conv_scale_km): +def conv_wavelet_sum(dbz_data, zr_a, zr_b, scale_break): """ Computes the sum of wavelet transform components for convective scales from dBZ data. @@ -95,8 +95,8 @@ def conv_wavelet_sum(dbz_data, zr_a, zr_b, res_km, conv_scale_km): Coefficients for the Z-R relationship. res_km: float Resolution of the radar data in km. - conv_scale_km: float - Approximate scale break (in km) between convective and stratiform scales. + scale_break: int + Calculated scale break (in pixels) between convective and stratiform scales Returns: ======== @@ -110,7 +110,7 @@ def conv_wavelet_sum(dbz_data, zr_a, zr_b, res_km, conv_scale_km): dbz_data[np.isnan(dbz_data)] = 0 rr_data = ((10.0 ** (dbz_data / 10.0)) / zr_a) ** (1.0 / zr_b) - scale_break = calc_scale_break(res_km, conv_scale_km) + wt, _ = atwt2d(rr_data, max_scale=scale_break) wt_sum = np.sum(wt, axis=(0)) @@ -175,14 +175,14 @@ def label_classes( return wt_class.astype(np.int32) -def calc_scale_break(res_km, conv_scale_km): +def calc_scale_break(res_meters, conv_scale_km): """ Compute scale break for convection and stratiform regions. WT will be computed upto this scale and features will be designated as convection. Parameters: =========== - res_km: float + res_meters: float resolution of the image. conv_scale_km: float expected size of spatial variations due to convection. @@ -192,6 +192,7 @@ def calc_scale_break(res_km, conv_scale_km): dyadic: int integer scale break in pixels. """ + res_km = res_meters / 1000 scale_break = log((conv_scale_km / res_km)) / log(2) + 1 return int(round(scale_break)) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index b389da8a27..25e6bb7d55 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -9,7 +9,7 @@ from ..config import get_field_name, get_fillvalue, get_metadata from ._echo_class import _feature_detection, steiner_class_buff -from ._echo_class_wt import wavelet_reclass +from ._echo_class_wt import wavelet_reclass, calc_scale_break from ..core import Grid @@ -1026,8 +1026,9 @@ def conv_strat_raut( Scale break may vary between 15 and 30 km over different regions and seasons (Refere to Raut et al 2018 for more discussion on scale-breaks). Note that the algorithm is insensitive to small variations in the scale break due to the - dyadic nature of the scaling. - Default is 20 km. + dyadic nature of the scaling. The actual scale break used in the calculation of wavelets + is return in the output dictionary by parameter `scale_break_used`. + Default is 20 km. min_reflectivity : float, optional Minimum reflectivity threshold. Reflectivities below this value are not classified. Default is 5 dBZ. This value must be greater than or equal to '0'. @@ -1080,6 +1081,10 @@ def conv_strat_raut( UserWarning, ) + #Compure scale break (km) here to paas it on as parameter to user dictionary + scale_break = calc_scale_break(res_meters=dx, conv_scale_km=conv_scale_km) + scale_break_km = scale_break * dx / 1000 + # Sanity checks for parameters if override_checks is False if not override_checks: conv_core_threshold = max( @@ -1110,7 +1115,7 @@ def conv_strat_raut( zr_b, core_wt_threshold=core_wt_threshold, conv_wt_threshold=conv_wt_threshold, - conv_scale_km=conv_scale_km, + scale_break=scale_break, min_reflectivity=min_reflectivity, conv_min_refl=conv_min_refl, conv_core_threshold=conv_core_threshold, @@ -1135,6 +1140,7 @@ def conv_strat_raut( "core_wt_threshold": core_wt_threshold, "conv_wt_threshold": conv_wt_threshold, "conv_scale_km": conv_scale_km, + "scale_break_used": scale_break_km, "min_reflectivity": min_reflectivity, "conv_min_refl": conv_min_refl, "conv_core_threshold": conv_core_threshold, From 07be2d139f6f4cc695046608d957ebd6de11401c Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 18 Dec 2023 11:53:39 -0600 Subject: [PATCH 43/54] ENH:scale_break is adaptive to resolution --- pyart/retrieve/_echo_class_wt.py | 4 ++-- pyart/retrieve/echo_class.py | 22 ++++++++++++---------- 2 files changed, 14 insertions(+), 12 deletions(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index a7c5027fe6..526e800bc2 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -43,7 +43,7 @@ def wavelet_reclass( res_km: float Resolution of the radar data in km scale_break: int - Calculated scale break (in pixels) between convective and stratiform scales + Calculated scale break between convective and stratiform scales. Dyadically spaced in grid pixels. Returns: ======== @@ -190,7 +190,7 @@ def calc_scale_break(res_meters, conv_scale_km): Returns: ======== dyadic: int - integer scale break in pixels. + integer scale break in dyadic scale. """ res_km = res_meters / 1000 scale_break = log((conv_scale_km / res_km)) / log(2) + 1 diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 25e6bb7d55..5071defe6d 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -990,7 +990,7 @@ def conv_strat_raut( zr_b=1.6, core_wt_threshold=5, conv_wt_threshold=1.5, - conv_scale_km=20, + conv_scale_km=25, min_reflectivity=5, conv_min_refl=25, conv_core_threshold=42, @@ -1023,12 +1023,12 @@ def conv_strat_raut( Default is 1.5. Recommended values are between 1 and 2. conv_scale_km : float, optional Approximate scale break (in km) between convective and stratiform scales. - Scale break may vary between 15 and 30 km over different regions and seasons + Scale break may vary over different regions and seasons (Refere to Raut et al 2018 for more discussion on scale-breaks). Note that the algorithm is insensitive to small variations in the scale break due to the dyadic nature of the scaling. The actual scale break used in the calculation of wavelets - is return in the output dictionary by parameter `scale_break_used`. - Default is 20 km. + is returned in the output dictionary by parameter `scale_break_used`. + Default is 25 km. Recommended values are between 16 and 32 km. min_reflectivity : float, optional Minimum reflectivity threshold. Reflectivities below this value are not classified. Default is 5 dBZ. This value must be greater than or equal to '0'. @@ -1073,17 +1073,19 @@ def conv_strat_raut( raise TypeError("The 'grid' is not a Py-ART Grid object.") # Check if dx and dy are the same, and warn if not - dx = (grid.x["data"][1] - grid.x["data"][0]) / 1000 - dy = (grid.y["data"][1] - grid.y["data"][0]) / 1000 + dx = (grid.x["data"][1] - grid.x["data"][0]) + dy = (grid.y["data"][1] - grid.y["data"][0]) if dx != dy: warn( "Warning: Grid resolution `dx` and `dy` should be comparable for correct results.", UserWarning, ) - #Compure scale break (km) here to paas it on as parameter to user dictionary + # Compute scale break (dyadic) here to paas it on as parameter to user dictionary scale_break = calc_scale_break(res_meters=dx, conv_scale_km=conv_scale_km) - scale_break_km = scale_break * dx / 1000 + + # From dyadic scale to km + scale_break_km = (2 ** (scale_break-1)) * dx / 1000 # Sanity checks for parameters if override_checks is False if not override_checks: @@ -1097,7 +1099,7 @@ def conv_strat_raut( 1, min(conv_wt_threshold, 2) ) # conv_wt_threshold should be between 1 and 2 conv_scale_km = max( - 15, min(conv_scale_km, 30) + 16, min(conv_scale_km, 32) ) # conv_scale_km should be between 15 and 30 km min_reflectivity = max( 0, min_reflectivity @@ -1140,7 +1142,7 @@ def conv_strat_raut( "core_wt_threshold": core_wt_threshold, "conv_wt_threshold": conv_wt_threshold, "conv_scale_km": conv_scale_km, - "scale_break_used": scale_break_km, + "scale_break_used": int(scale_break_km), "min_reflectivity": min_reflectivity, "conv_min_refl": conv_min_refl, "conv_core_threshold": conv_core_threshold, From 9fff87e5b6b05fa3494c0d0f290684af4d2bb553 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 18 Dec 2023 11:54:29 -0600 Subject: [PATCH 44/54] ENH:atol added, tests default function arguments --- tests/retrieve/test_echo_class.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/tests/retrieve/test_echo_class.py b/tests/retrieve/test_echo_class.py index afaf762110..ef265b3346 100644 --- a/tests/retrieve/test_echo_class.py +++ b/tests/retrieve/test_echo_class.py @@ -368,9 +368,7 @@ def test_conv_strat_raut_results_correct(): masked_boundary=mask_margin, ) - wtclass = pyart.retrieve.conv_strat_raut( - gaussian_storm_2d, "reflectivity", cappi_level=0 - ) + wtclass = pyart.retrieve.conv_strat_raut(gaussian_storm_2d, "reflectivity") # Create a 32x32 array of ones test_reclass = np.ones((grid_len, grid_len)) @@ -397,5 +395,5 @@ def test_conv_strat_raut_results_correct(): masked_reclass = np.ma.array(test_reclass, mask=mask).astype(np.int32) masked_reclass = np.expand_dims(masked_reclass, axis=0) - assert_allclose(masked_reclass, wtclass["wt_reclass"]["data"]) + assert_allclose(masked_reclass, wtclass["wt_reclass"]["data"], atol=0.1) From 649e3fb16d2ff69db6706a78e42b8865970cca49 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 18 Dec 2023 11:56:08 -0600 Subject: [PATCH 45/54] FROMAT:black --- pyart/retrieve/_echo_class_wt.py | 2 +- pyart/retrieve/echo_class.py | 134 +++++++++++++++---------------- 2 files changed, 68 insertions(+), 68 deletions(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index 526e800bc2..fd34ec5f64 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -63,7 +63,7 @@ def wavelet_reclass( radar_mask = np.ma.getmask(dbz_data) # dx and dy are considered to be same (res_km). - res_meters = (grid.x["data"][1] - grid.x["data"][0]) + res_meters = grid.x["data"][1] - grid.x["data"][0] wt_sum = conv_wavelet_sum(dbz_data, zr_a, zr_b, scale_break) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 5071defe6d..ff6cd3dac3 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -997,75 +997,75 @@ def conv_strat_raut( override_checks=False, ): """ - A fast method to classify radar echoes into convective cores, mixed convection, and stratiform regions. - - This function uses à trous wavelet transform (ATWT) for multiresolution (scale) analysis of radar field, - focusing on precipitation structure over reflectivity thresholds for classification (Raut et al 2008, 2020). - - Parameters - ---------- - grid : Grid - Grid object containing radar data. - refl_field : str - Field name for reflectivity data in the Py-ART grid object. - zr_a : float, optional - Coefficient 'a' in the Z-R relationship Z = a*R^b for reflectivity to rain rate conversion. - The algorithm is not sensitive to precise values of 'zr_a' and 'zr_b'; however, - they must be adjusted based on the type of radar used. - Default is 200. - zr_b : float, optional - Coefficient 'b' in the Z-R relationship Z = a*R^b. Default is 1.6. - core_wt_threshold : float, optional - Threshold for wavelet components to separate convective cores from mix-intermediate type. - Default is 5. Recommended values are between 4 and 6. - conv_wt_threshold : float, optional - Threshold for significant wavelet components to separate all convection from stratiform. - Default is 1.5. Recommended values are between 1 and 2. - conv_scale_km : float, optional - Approximate scale break (in km) between convective and stratiform scales. - Scale break may vary over different regions and seasons - (Refere to Raut et al 2018 for more discussion on scale-breaks). Note that the - algorithm is insensitive to small variations in the scale break due to the - dyadic nature of the scaling. The actual scale break used in the calculation of wavelets - is returned in the output dictionary by parameter `scale_break_used`. - Default is 25 km. Recommended values are between 16 and 32 km. - min_reflectivity : float, optional - Minimum reflectivity threshold. Reflectivities below this value are not classified. - Default is 5 dBZ. This value must be greater than or equal to '0'. - conv_min_refl : float, optional - Reflectivity values lower than this threshold will be always considered as non-convective. - Default is 25 dBZ. Recommended values are between 25 and 30 dBZ. - conv_core_threshold : float, optional - Reflectivities above this threshold are classified as convective cores if wavelet components are significant (See: conv_wt_threshold). - Default is 42 dBZ. - Recommended value must be is greater than or equal to 40 dBZ. The algorithm is not sensitive to this value. - override_checks : bool, optional - If set to True, the function will bypass the sanity checks for above parameter values. - This allows the user to use custom values for parameters, even if they fall outside - the recommended ranges. The default is False. - - Returns -------- + A fast method to classify radar echoes into convective cores, mixed convection, and stratiform regions. + + This function uses à trous wavelet transform (ATWT) for multiresolution (scale) analysis of radar field, + focusing on precipitation structure over reflectivity thresholds for classification (Raut et al 2008, 2020). + + Parameters + ---------- + grid : Grid + Grid object containing radar data. + refl_field : str + Field name for reflectivity data in the Py-ART grid object. + zr_a : float, optional + Coefficient 'a' in the Z-R relationship Z = a*R^b for reflectivity to rain rate conversion. + The algorithm is not sensitive to precise values of 'zr_a' and 'zr_b'; however, + they must be adjusted based on the type of radar used. + Default is 200. + zr_b : float, optional + Coefficient 'b' in the Z-R relationship Z = a*R^b. Default is 1.6. + core_wt_threshold : float, optional + Threshold for wavelet components to separate convective cores from mix-intermediate type. + Default is 5. Recommended values are between 4 and 6. + conv_wt_threshold : float, optional + Threshold for significant wavelet components to separate all convection from stratiform. + Default is 1.5. Recommended values are between 1 and 2. + conv_scale_km : float, optional + Approximate scale break (in km) between convective and stratiform scales. + Scale break may vary over different regions and seasons + (Refere to Raut et al 2018 for more discussion on scale-breaks). Note that the + algorithm is insensitive to small variations in the scale break due to the + dyadic nature of the scaling. The actual scale break used in the calculation of wavelets + is returned in the output dictionary by parameter `scale_break_used`. + Default is 25 km. Recommended values are between 16 and 32 km. + min_reflectivity : float, optional + Minimum reflectivity threshold. Reflectivities below this value are not classified. + Default is 5 dBZ. This value must be greater than or equal to '0'. + conv_min_refl : float, optional + Reflectivity values lower than this threshold will be always considered as non-convective. + Default is 25 dBZ. Recommended values are between 25 and 30 dBZ. + conv_core_threshold : float, optional + Reflectivities above this threshold are classified as convective cores if wavelet components are significant (See: conv_wt_threshold). + Default is 42 dBZ. + Recommended value must be is greater than or equal to 40 dBZ. The algorithm is not sensitive to this value. + override_checks : bool, optional + If set to True, the function will bypass the sanity checks for above parameter values. + This allows the user to use custom values for parameters, even if they fall outside + the recommended ranges. The default is False. + + Returns + ------- - dict: - A dictionary structured as a Py-ART grid field, suitable for adding to a Py-ART Grid object. The dictionary - contains the classification data and associated metadata. The classification categories are as follows: - - 3: Convective Cores: associated with strong updrafts and active collision-coalescence. - - 2: Mixed-Intermediate: capturing a wide range of convective activities, excluding the convective cores. - - 1: Stratiform: remaining areas with more uniform and less intense precipitation. - - 0: Unclassified: for reflectivity below the minimum threshold. + dict: + A dictionary structured as a Py-ART grid field, suitable for adding to a Py-ART Grid object. The dictionary + contains the classification data and associated metadata. The classification categories are as follows: + - 3: Convective Cores: associated with strong updrafts and active collision-coalescence. + - 2: Mixed-Intermediate: capturing a wide range of convective activities, excluding the convective cores. + - 1: Stratiform: remaining areas with more uniform and less intense precipitation. + - 0: Unclassified: for reflectivity below the minimum threshold. - References - ---------- - Raut, B. A., Karekar, R. N., & Puranik, D. M. (2008). Wavelet-based technique to extract convective clouds from - infrared satellite images. IEEE Geosci. Remote Sens. Lett., 5(3), 328-330. + References + ---------- + Raut, B. A., Karekar, R. N., & Puranik, D. M. (2008). Wavelet-based technique to extract convective clouds from + infrared satellite images. IEEE Geosci. Remote Sens. Lett., 5(3), 328-330. - Raut, B. A., Seed, A. W., Reeder, M. J., & Jakob, C. (2018). A multiplicative cascade model for high‐resolution - space‐time downscaling of rainfall. J. Geophys. Res. Atmos., 123(4), 2050-2067. + Raut, B. A., Seed, A. W., Reeder, M. J., & Jakob, C. (2018). A multiplicative cascade model for high‐resolution + space‐time downscaling of rainfall. J. Geophys. Res. Atmos., 123(4), 2050-2067. - Raut, B. A., Louf, V., Gayatri, K., Murugavel, P., Konwar, M., & Prabhakaran, T. (2020). A multiresolution technique - for the classification of precipitation echoes in radar data. IEEE Trans. Geosci. Remote Sens., 58(8), 5409-5415. + Raut, B. A., Louf, V., Gayatri, K., Murugavel, P., Konwar, M., & Prabhakaran, T. (2020). A multiresolution technique + for the classification of precipitation echoes in radar data. IEEE Trans. Geosci. Remote Sens., 58(8), 5409-5415. """ # Check if the grid is a Py-ART Grid object @@ -1073,8 +1073,8 @@ def conv_strat_raut( raise TypeError("The 'grid' is not a Py-ART Grid object.") # Check if dx and dy are the same, and warn if not - dx = (grid.x["data"][1] - grid.x["data"][0]) - dy = (grid.y["data"][1] - grid.y["data"][0]) + dx = grid.x["data"][1] - grid.x["data"][0] + dy = grid.y["data"][1] - grid.y["data"][0] if dx != dy: warn( "Warning: Grid resolution `dx` and `dy` should be comparable for correct results.", @@ -1085,7 +1085,7 @@ def conv_strat_raut( scale_break = calc_scale_break(res_meters=dx, conv_scale_km=conv_scale_km) # From dyadic scale to km - scale_break_km = (2 ** (scale_break-1)) * dx / 1000 + scale_break_km = (2 ** (scale_break - 1)) * dx / 1000 # Sanity checks for parameters if override_checks is False if not override_checks: From 6b778439dd205db281a0b2f07488e7e7f6d55e26 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 18 Dec 2023 11:58:23 -0600 Subject: [PATCH 46/54] MINOR:corrected tab in docstring --- pyart/retrieve/echo_class.py | 128 +++++++++++++++++------------------ 1 file changed, 64 insertions(+), 64 deletions(-) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index ff6cd3dac3..d5b6e07fb0 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -997,75 +997,75 @@ def conv_strat_raut( override_checks=False, ): """ - A fast method to classify radar echoes into convective cores, mixed convection, and stratiform regions. - - This function uses à trous wavelet transform (ATWT) for multiresolution (scale) analysis of radar field, - focusing on precipitation structure over reflectivity thresholds for classification (Raut et al 2008, 2020). - - Parameters - ---------- - grid : Grid - Grid object containing radar data. - refl_field : str - Field name for reflectivity data in the Py-ART grid object. - zr_a : float, optional - Coefficient 'a' in the Z-R relationship Z = a*R^b for reflectivity to rain rate conversion. - The algorithm is not sensitive to precise values of 'zr_a' and 'zr_b'; however, - they must be adjusted based on the type of radar used. - Default is 200. - zr_b : float, optional - Coefficient 'b' in the Z-R relationship Z = a*R^b. Default is 1.6. - core_wt_threshold : float, optional - Threshold for wavelet components to separate convective cores from mix-intermediate type. - Default is 5. Recommended values are between 4 and 6. - conv_wt_threshold : float, optional - Threshold for significant wavelet components to separate all convection from stratiform. - Default is 1.5. Recommended values are between 1 and 2. - conv_scale_km : float, optional - Approximate scale break (in km) between convective and stratiform scales. - Scale break may vary over different regions and seasons - (Refere to Raut et al 2018 for more discussion on scale-breaks). Note that the - algorithm is insensitive to small variations in the scale break due to the - dyadic nature of the scaling. The actual scale break used in the calculation of wavelets - is returned in the output dictionary by parameter `scale_break_used`. - Default is 25 km. Recommended values are between 16 and 32 km. - min_reflectivity : float, optional - Minimum reflectivity threshold. Reflectivities below this value are not classified. - Default is 5 dBZ. This value must be greater than or equal to '0'. - conv_min_refl : float, optional - Reflectivity values lower than this threshold will be always considered as non-convective. - Default is 25 dBZ. Recommended values are between 25 and 30 dBZ. - conv_core_threshold : float, optional - Reflectivities above this threshold are classified as convective cores if wavelet components are significant (See: conv_wt_threshold). - Default is 42 dBZ. - Recommended value must be is greater than or equal to 40 dBZ. The algorithm is not sensitive to this value. - override_checks : bool, optional - If set to True, the function will bypass the sanity checks for above parameter values. - This allows the user to use custom values for parameters, even if they fall outside - the recommended ranges. The default is False. - - Returns - ------- + A fast method to classify radar echoes into convective cores, mixed convection, and stratiform regions. + + This function uses à trous wavelet transform (ATWT) for multiresolution (scale) analysis of radar field, + focusing on precipitation structure over reflectivity thresholds for classification (Raut et al 2008, 2020). + + Parameters + ---------- + grid : Grid + Grid object containing radar data. + refl_field : str + Field name for reflectivity data in the Py-ART grid object. + zr_a : float, optional + Coefficient 'a' in the Z-R relationship Z = a*R^b for reflectivity to rain rate conversion. + The algorithm is not sensitive to precise values of 'zr_a' and 'zr_b'; however, + they must be adjusted based on the type of radar used. + Default is 200. + zr_b : float, optional + Coefficient 'b' in the Z-R relationship Z = a*R^b. Default is 1.6. + core_wt_threshold : float, optional + Threshold for wavelet components to separate convective cores from mix-intermediate type. + Default is 5. Recommended values are between 4 and 6. + conv_wt_threshold : float, optional + Threshold for significant wavelet components to separate all convection from stratiform. + Default is 1.5. Recommended values are between 1 and 2. + conv_scale_km : float, optional + Approximate scale break (in km) between convective and stratiform scales. + Scale break may vary over different regions and seasons + (Refere to Raut et al 2018 for more discussion on scale-breaks). Note that the + algorithm is insensitive to small variations in the scale break due to the + dyadic nature of the scaling. The actual scale break used in the calculation of wavelets + is returned in the output dictionary by parameter `scale_break_used`. + Default is 25 km. Recommended values are between 16 and 32 km. + min_reflectivity : float, optional + Minimum reflectivity threshold. Reflectivities below this value are not classified. + Default is 5 dBZ. This value must be greater than or equal to '0'. + conv_min_refl : float, optional + Reflectivity values lower than this threshold will be always considered as non-convective. + Default is 25 dBZ. Recommended values are between 25 and 30 dBZ. + conv_core_threshold : float, optional + Reflectivities above this threshold are classified as convective cores if wavelet components are significant (See: conv_wt_threshold). + Default is 42 dBZ. + Recommended value must be is greater than or equal to 40 dBZ. The algorithm is not sensitive to this value. + override_checks : bool, optional + If set to True, the function will bypass the sanity checks for above parameter values. + This allows the user to use custom values for parameters, even if they fall outside + the recommended ranges. The default is False. + + Returns +------- - dict: - A dictionary structured as a Py-ART grid field, suitable for adding to a Py-ART Grid object. The dictionary - contains the classification data and associated metadata. The classification categories are as follows: - - 3: Convective Cores: associated with strong updrafts and active collision-coalescence. - - 2: Mixed-Intermediate: capturing a wide range of convective activities, excluding the convective cores. - - 1: Stratiform: remaining areas with more uniform and less intense precipitation. - - 0: Unclassified: for reflectivity below the minimum threshold. + dict: + A dictionary structured as a Py-ART grid field, suitable for adding to a Py-ART Grid object. The dictionary + contains the classification data and associated metadata. The classification categories are as follows: + - 3: Convective Cores: associated with strong updrafts and active collision-coalescence. + - 2: Mixed-Intermediate: capturing a wide range of convective activities, excluding the convective cores. + - 1: Stratiform: remaining areas with more uniform and less intense precipitation. + - 0: Unclassified: for reflectivity below the minimum threshold. - References - ---------- - Raut, B. A., Karekar, R. N., & Puranik, D. M. (2008). Wavelet-based technique to extract convective clouds from - infrared satellite images. IEEE Geosci. Remote Sens. Lett., 5(3), 328-330. + References + ---------- + Raut, B. A., Karekar, R. N., & Puranik, D. M. (2008). Wavelet-based technique to extract convective clouds from + infrared satellite images. IEEE Geosci. Remote Sens. Lett., 5(3), 328-330. - Raut, B. A., Seed, A. W., Reeder, M. J., & Jakob, C. (2018). A multiplicative cascade model for high‐resolution - space‐time downscaling of rainfall. J. Geophys. Res. Atmos., 123(4), 2050-2067. + Raut, B. A., Seed, A. W., Reeder, M. J., & Jakob, C. (2018). A multiplicative cascade model for high‐resolution + space‐time downscaling of rainfall. J. Geophys. Res. Atmos., 123(4), 2050-2067. - Raut, B. A., Louf, V., Gayatri, K., Murugavel, P., Konwar, M., & Prabhakaran, T. (2020). A multiresolution technique - for the classification of precipitation echoes in radar data. IEEE Trans. Geosci. Remote Sens., 58(8), 5409-5415. + Raut, B. A., Louf, V., Gayatri, K., Murugavel, P., Konwar, M., & Prabhakaran, T. (2020). A multiresolution technique + for the classification of precipitation echoes in radar data. IEEE Trans. Geosci. Remote Sens., 58(8), 5409-5415. """ # Check if the grid is a Py-ART Grid object From b78919202fcdcf1d6d32fd353c7ea1d2b660e2e0 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 18 Dec 2023 12:08:55 -0600 Subject: [PATCH 47/54] used new scaling and added description --- .../retrieve/wavelet_echo_class_example.ipynb | 141 ++++++++++++++++-- 1 file changed, 126 insertions(+), 15 deletions(-) diff --git a/examples/retrieve/wavelet_echo_class_example.ipynb b/examples/retrieve/wavelet_echo_class_example.ipynb index 3890b4c352..24ed5a1ed6 100644 --- a/examples/retrieve/wavelet_echo_class_example.ipynb +++ b/examples/retrieve/wavelet_echo_class_example.ipynb @@ -52,9 +52,27 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n", + "## library for working with weather radar data. Py-ART is partly\n", + "## supported by the U.S. Department of Energy as part of the Atmospheric\n", + "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n", + "## Science user facility.\n", + "##\n", + "## If you use this software to prepare a publication, please cite:\n", + "##\n", + "## JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n", + "\n" + ] + } + ], "source": [ "import pyart\n", "import numpy as np" @@ -83,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -124,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -166,16 +184,58 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/bhupendra/projects/pyart/pyart/retrieve/_echo_class_wt.py:156: RuntimeWarning: invalid value encountered in cast\n", + "/Users/bhupendra/projects/pyart/pyart/retrieve/_echo_class_wt.py:175: RuntimeWarning: invalid value encountered in cast\n", " return wt_class.astype(np.int32)\n" ] + }, + { + "data": { + "text/plain": [ + "{'data': masked_array(\n", + " data=[[[--, --, --, ..., --, --, --],\n", + " [--, --, --, ..., --, --, --],\n", + " [--, --, --, ..., --, --, --],\n", + " ...,\n", + " [--, --, --, ..., --, --, --],\n", + " [--, --, --, ..., --, --, --],\n", + " [--, --, --, ..., --, --, --]]],\n", + " mask=[[[ True, True, True, ..., True, True, True],\n", + " [ True, True, True, ..., True, True, True],\n", + " [ True, True, True, ..., True, True, True],\n", + " ...,\n", + " [ True, True, True, ..., True, True, True],\n", + " [ True, True, True, ..., True, True, True],\n", + " [ True, True, True, ..., True, True, True]]],\n", + " fill_value=999999,\n", + " dtype=int32),\n", + " 'standard_name': 'wavelet_echo_class',\n", + " 'long_name': 'Wavelet-based multiresolution radar echo classification',\n", + " 'valid_min': 0,\n", + " 'valid_max': 3,\n", + " 'classification_description': '0: Unclassified, 1: Stratiform, 2: Mixed-Intermediate, 3: Convective Cores',\n", + " 'parameters': {'refl_field': 'reflectivity_horizontal',\n", + " 'cappi_level': 0,\n", + " 'zr_a': 200,\n", + " 'zr_b': 1.6,\n", + " 'core_wt_threshold': 5,\n", + " 'conv_wt_threshold': 1.5,\n", + " 'conv_scale_km': 25,\n", + " 'scale_break_used': 32,\n", + " 'min_reflectivity': 5,\n", + " 'conv_min_refl': 25,\n", + " 'conv_core_threshold': 42}}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -184,7 +244,15 @@ " refl_field=\"reflectivity_horizontal\")\n", "\n", "# add field\n", - "grid.add_field(\"wt_reclass\", reclass_dict[\"wt_reclass\"], replace_existing=True)\n" + "grid.add_field(\"wt_reclass\", reclass_dict[\"wt_reclass\"], replace_existing=True)\n", + "reclass_dict['wt_reclass']\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The classification parameters are returned in the dictionary along with the masked array. Although `conv_scale_km` was set to `25`, the algorithm calculated the `scale_break` as `32km`. This variation depends on the data resolution. Parameters outside the specified range will automatically be adjusted to fall within the permissible range. To disable this automatic adjustment and override the range checks, set `override_checks` to `True`.\n" ] }, { @@ -196,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -271,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -297,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -340,16 +408,58 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/bhupendra/projects/pyart/pyart/retrieve/_echo_class_wt.py:156: RuntimeWarning: invalid value encountered in cast\n", + "/Users/bhupendra/projects/pyart/pyart/retrieve/_echo_class_wt.py:175: RuntimeWarning: invalid value encountered in cast\n", " return wt_class.astype(np.int32)\n" ] + }, + { + "data": { + "text/plain": [ + "{'wt_reclass': {'data': masked_array(\n", + " data=[[[--, --, --, ..., --, --, --],\n", + " [--, --, --, ..., --, --, --],\n", + " [--, --, --, ..., --, --, --],\n", + " ...,\n", + " [--, --, --, ..., 1, 1, 1],\n", + " [--, --, --, ..., 1, 1, 1],\n", + " [--, --, --, ..., 1, 1, 1]]],\n", + " mask=[[[ True, True, True, ..., True, True, True],\n", + " [ True, True, True, ..., True, True, True],\n", + " [ True, True, True, ..., True, True, True],\n", + " ...,\n", + " [ True, True, True, ..., False, False, False],\n", + " [ True, True, True, ..., False, False, False],\n", + " [ True, True, True, ..., False, False, False]]],\n", + " fill_value=999999,\n", + " dtype=int32),\n", + " 'standard_name': 'wavelet_echo_class',\n", + " 'long_name': 'Wavelet-based multiresolution radar echo classification',\n", + " 'valid_min': 0,\n", + " 'valid_max': 3,\n", + " 'classification_description': '0: Unclassified, 1: Stratiform, 2: Mixed-Intermediate, 3: Convective Cores',\n", + " 'parameters': {'refl_field': 'reflectivity',\n", + " 'cappi_level': 0,\n", + " 'zr_a': 200,\n", + " 'zr_b': 1.6,\n", + " 'core_wt_threshold': 5,\n", + " 'conv_wt_threshold': 1.5,\n", + " 'conv_scale_km': 25,\n", + " 'scale_break_used': 32,\n", + " 'min_reflectivity': 5,\n", + " 'conv_min_refl': 25,\n", + " 'conv_core_threshold': 42}}}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -359,17 +469,18 @@ ")\n", "\n", "# add field\n", - "grid.add_field(\"wt_reclass\", reclass_dict[\"wt_reclass\"], replace_existing=True)" + "grid.add_field(\"wt_reclass\", reclass_dict[\"wt_reclass\"], replace_existing=True)\n", + "reclass_dict" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] From 8c11c5d5759e5b08d5cab16c3121067db69a26ee Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 18 Dec 2023 13:18:09 -0600 Subject: [PATCH 48/54] MINOR: alignment in docstring --- pyart/retrieve/echo_class.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index d5b6e07fb0..3df8bc6cc1 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -1045,7 +1045,7 @@ def conv_strat_raut( the recommended ranges. The default is False. Returns -------- + ------- dict: A dictionary structured as a Py-ART grid field, suitable for adding to a Py-ART Grid object. The dictionary From 48f66a947850c6821d7969003cc57b1b78c4e3fe Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Mon, 18 Dec 2023 14:07:30 -0600 Subject: [PATCH 49/54] FORAMT:linting error corrected --- pyart/retrieve/_echo_class_wt.py | 10 +++------- pyart/retrieve/echo_class.py | 2 +- pyart/testing/sample_objects.py | 4 ++-- tests/retrieve/test_echo_class.py | 2 +- tests/testing/test_sample_objects.py | 4 ++-- 5 files changed, 9 insertions(+), 13 deletions(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index fd34ec5f64..7b99b554bc 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -1,5 +1,5 @@ """ -Classification of Precipitation Echoes in Radar Data. +Classification of Precipitation Echoes in Radar Data. Created on Thu Oct 12 23:12:19 2017 @author: Bhupendra Raut @@ -15,7 +15,6 @@ import numpy as np -from numpy import log, floor def wavelet_reclass( @@ -62,9 +61,6 @@ def wavelet_reclass( # save the radar original mask for missing data. radar_mask = np.ma.getmask(dbz_data) - # dx and dy are considered to be same (res_km). - res_meters = grid.x["data"][1] - grid.x["data"][0] - wt_sum = conv_wavelet_sum(dbz_data, zr_a, zr_b, scale_break) wt_class = label_classes( @@ -193,7 +189,7 @@ def calc_scale_break(res_meters, conv_scale_km): integer scale break in dyadic scale. """ res_km = res_meters / 1000 - scale_break = log((conv_scale_km / res_km)) / log(2) + 1 + scale_break = np.log((conv_scale_km / res_km)) / np.log(2) + 1 return int(round(scale_break)) @@ -230,7 +226,7 @@ def atwt2d(data2d, max_scale=-1): dims = data2d.shape min_dims = np.min(dims) - max_possible_scales = int(floor(log(min_dims) / log(2))) + max_possible_scales = int(np.floor(np.log(min_dims) / np.log(2))) if max_scale < 0 or max_possible_scales <= max_scale: max_scale = max_possible_scales - 1 diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 3df8bc6cc1..de2ae7baef 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -984,7 +984,7 @@ def get_freq_band(freq): def conv_strat_raut( grid, - refl_field="reflectivity", + refl_field, cappi_level=0, zr_a=200, zr_b=1.6, diff --git a/pyart/testing/sample_objects.py b/pyart/testing/sample_objects.py index 2634618c8e..31483841a3 100644 --- a/pyart/testing/sample_objects.py +++ b/pyart/testing/sample_objects.py @@ -378,7 +378,7 @@ def make_normal_storm(sigma, mu): return test_grid -def make_gaussian_storm_grid(min_value=5, max_value=45, grid_len=32, +def make_gaussian_storm_grid(min_value=5, max_value=45, grid_len=32, sigma=0.2, mu=0.0, masked_boundary=3): """ Make a 1 km resolution grid with a Gaussian storm pattern at the center, @@ -416,7 +416,7 @@ def make_gaussian_storm_grid(min_value=5, max_value=45, grid_len=32, gaussian_normalized = (gaussian - np.min(gaussian)) / (np.max(gaussian) - np.min(gaussian)) storm_intensity = gaussian_normalized * (max_value - min_value) + min_value storm_intensity = np.stack([storm_intensity, storm_intensity]) - + # Apply thresholds for storm intensity and masking mask = np.zeros_like(storm_intensity, dtype=bool) diff --git a/tests/retrieve/test_echo_class.py b/tests/retrieve/test_echo_class.py index ef265b3346..8249f84913 100644 --- a/tests/retrieve/test_echo_class.py +++ b/tests/retrieve/test_echo_class.py @@ -352,7 +352,7 @@ def test_conv_strat_raut_results_correct(): Checks the correctness of the results from the function. I created a fixed Gaussian storm with masked boundaries as pyart grid and classified it. - Then constructed manually the expected classification results and compared it to the actual output from the function. + Then constructed manually the expected classification results and compared it to the actual output from the function. """ # Create a Gaussian storm grid diff --git a/tests/testing/test_sample_objects.py b/tests/testing/test_sample_objects.py index d35289d210..10f85b1a94 100644 --- a/tests/testing/test_sample_objects.py +++ b/tests/testing/test_sample_objects.py @@ -2,7 +2,7 @@ import numpy as np -from pyart.testing.sample_objects import make_gaussian_storm_grid +from pyart.testing.sample_objects import make_gaussian_storm_grid def test_gaussian_storm_grid_results_correct(): @@ -38,7 +38,7 @@ def test_gaussian_storm_grid_results_correct(): # Test for Max and Min assert np.isclose(np.max(storm_data), max_value), "Maximum value does not match expected" - assert np.isclose(np.min(storm_data[storm_data.mask == False]), min_value), "Minimum value does not match expected" + assert np.isclose(np.min(storm_data[~storm_data.mask]), min_value), "Minimum value does not match expected" # Test Mean and SD expected_mean = 8.666844653650797 From f3915fec8c4e9ea86c4faa9f849d215d8dc5ff70 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Tue, 26 Dec 2023 14:39:10 -0600 Subject: [PATCH 50/54] 'LINTING ERR:import-block sorted;extra'()' removed' Follwing errors are fixed: pyart/retrieve/_echo_class_wt.py:192:26: UP034 [*] Avoid extraneous parentheses pyart/retrieve/echo_class.py:6:1: I001 [*] Import block is un-sorted or un-formatted Found 2 errors. [*] 2 fixable with the `--fix` option. Error: Process completed with exit code 1. --- pyart/retrieve/_echo_class_wt.py | 3 ++- pyart/retrieve/echo_class.py | 5 +++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index 7b99b554bc..7039304189 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -189,7 +189,8 @@ def calc_scale_break(res_meters, conv_scale_km): integer scale break in dyadic scale. """ res_km = res_meters / 1000 - scale_break = np.log((conv_scale_km / res_km)) / np.log(2) + 1 + scale_break = np.log(conv_scale_km / res_km) / np.log(2) + 1 + return int(round(scale_break)) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index de2ae7baef..a2149617db 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -7,10 +7,11 @@ import numpy as np +# Local imports from ..config import get_field_name, get_fillvalue, get_metadata -from ._echo_class import _feature_detection, steiner_class_buff -from ._echo_class_wt import wavelet_reclass, calc_scale_break from ..core import Grid +from ._echo_class_wt import wavelet_reclass, calc_scale_break +from ._echo_class import _feature_detection, steiner_class_buff def steiner_conv_strat( From 54b3436e2e28d1a4ed15710ea664d8d037bcbe34 Mon Sep 17 00:00:00 2001 From: mgrover1 Date: Thu, 4 Jan 2024 15:59:33 -0600 Subject: [PATCH 51/54] FIX: Fix linting error --- pyart/retrieve/echo_class.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index a2149617db..dd085b3b95 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -10,8 +10,8 @@ # Local imports from ..config import get_field_name, get_fillvalue, get_metadata from ..core import Grid -from ._echo_class_wt import wavelet_reclass, calc_scale_break from ._echo_class import _feature_detection, steiner_class_buff +from ._echo_class_wt import calc_scale_break, wavelet_reclass def steiner_conv_strat( From 5ce8e8e3b8d20a211b9e10852e85e226567bce34 Mon Sep 17 00:00:00 2001 From: mgrover1 Date: Thu, 4 Jan 2024 16:04:55 -0600 Subject: [PATCH 52/54] FIX: Fix more linting errors --- .../retrieve/wavelet_echo_class_example.ipynb | 74 ++++++++++++------- pyart/retrieve/__init__.py | 2 +- pyart/testing/sample_objects.py | 21 ++++-- tests/retrieve/test_echo_class.py | 2 - tests/testing/test_sample_objects.py | 31 +++++--- 5 files changed, 83 insertions(+), 47 deletions(-) diff --git a/examples/retrieve/wavelet_echo_class_example.ipynb b/examples/retrieve/wavelet_echo_class_example.ipynb index 24ed5a1ed6..1b92486415 100644 --- a/examples/retrieve/wavelet_echo_class_example.ipynb +++ b/examples/retrieve/wavelet_echo_class_example.ipynb @@ -171,7 +171,7 @@ " estimate_flag=False,\n", ")\n", "\n", - "grid.add_field(\"convsf\", convsf_dict[\"feature_detection\"], replace_existing=True)\n" + "grid.add_field(\"convsf\", convsf_dict[\"feature_detection\"], replace_existing=True)" ] }, { @@ -240,12 +240,12 @@ ], "source": [ "reclass_dict = pyart.retrieve.conv_strat_raut(\n", - " grid, \n", - " refl_field=\"reflectivity_horizontal\")\n", + " grid, refl_field=\"reflectivity_horizontal\"\n", + ")\n", "\n", "# add field\n", "grid.add_field(\"wt_reclass\", reclass_dict[\"wt_reclass\"], replace_existing=True)\n", - "reclass_dict['wt_reclass']\n" + "reclass_dict[\"wt_reclass\"]" ] }, { @@ -306,28 +306,42 @@ "# First panel - Reflectivity (Top Left)\n", "ax1 = plt.subplot(1, 3, 1, projection=projection)\n", "display.plot_grid(\n", - " \"reflectivity_horizontal\", vmin=0, vmax=55, cmap=ref_cmap,\n", - " transform=ccrs.PlateCarree(), ax=ax1\n", + " \"reflectivity_horizontal\",\n", + " vmin=0,\n", + " vmax=55,\n", + " cmap=ref_cmap,\n", + " transform=ccrs.PlateCarree(),\n", + " ax=ax1,\n", ")\n", "\n", "# Second panel - CSY (Top Right)\n", "ax2 = plt.subplot(1, 3, 2, projection=projection)\n", "display.plot_grid(\n", - " \"convsf\", vmin=0, vmax=3, cmap=plt.get_cmap(\"pyart_HomeyerRainbow\", 4), ax=ax2,\n", - " transform=ccrs.PlateCarree(), ticks=[1 / 3, 1, 5 / 3],\n", - " ticklabs=[\"< 5dBZ\", \"Stratiform\", \"Convective\"]\n", + " \"convsf\",\n", + " vmin=0,\n", + " vmax=3,\n", + " cmap=plt.get_cmap(\"pyart_HomeyerRainbow\", 4),\n", + " ax=ax2,\n", + " transform=ccrs.PlateCarree(),\n", + " ticks=[1 / 3, 1, 5 / 3],\n", + " ticklabs=[\"< 5dBZ\", \"Stratiform\", \"Convective\"],\n", ")\n", "\n", "# Third panel - WT (Bottom Left)\n", "ax3 = plt.subplot(1, 3, 3, projection=projection)\n", "display.plot_grid(\n", - " \"wt_reclass\", vmin=0, vmax=4, cmap=plt.get_cmap(\"pyart_HomeyerRainbow\", 4), ax=ax3,\n", - " transform=ccrs.PlateCarree(), ticks=[0.5, 1.5, 2.5, 3.5],\n", - " ticklabs=[\"< 5dBZ\", \"Non-Convective\", \"Convective (Mixed)\", \"Convective (Cores)\"]\n", + " \"wt_reclass\",\n", + " vmin=0,\n", + " vmax=4,\n", + " cmap=plt.get_cmap(\"pyart_HomeyerRainbow\", 4),\n", + " ax=ax3,\n", + " transform=ccrs.PlateCarree(),\n", + " ticks=[0.5, 1.5, 2.5, 3.5],\n", + " ticklabs=[\"< 5dBZ\", \"Non-Convective\", \"Convective (Mixed)\", \"Convective (Cores)\"],\n", ")\n", "\n", "# Show the plot\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -403,7 +417,7 @@ "# add dimension to array to add to grid object\n", "convsf_dict[\"feature_detection\"][\"data\"] = convsf_masked\n", "# add field\n", - "grid.add_field(\"convsf\", convsf_dict[\"feature_detection\"], replace_existing=True)\n" + "grid.add_field(\"convsf\", convsf_dict[\"feature_detection\"], replace_existing=True)" ] }, { @@ -463,10 +477,7 @@ } ], "source": [ - "reclass_dict = pyart.retrieve.conv_strat_raut(\n", - " grid, \n", - " refl_field=\"reflectivity\"\n", - ")\n", + "reclass_dict = pyart.retrieve.conv_strat_raut(grid, refl_field=\"reflectivity\")\n", "\n", "# add field\n", "grid.add_field(\"wt_reclass\", reclass_dict[\"wt_reclass\"], replace_existing=True)\n", @@ -512,28 +523,37 @@ "# First panel - Reflectivity (Top Left)\n", "ax1 = plt.subplot(1, 3, 1, projection=projection)\n", "display.plot_grid(\n", - " \"reflectivity\", vmin=0, vmax=55, cmap=ref_cmap,\n", - " transform=ccrs.PlateCarree(), ax=ax1\n", + " \"reflectivity\", vmin=0, vmax=55, cmap=ref_cmap, transform=ccrs.PlateCarree(), ax=ax1\n", ")\n", "\n", "# Second panel - csy (Bottom Left)\n", "ax2 = plt.subplot(1, 3, 2, projection=projection)\n", "display.plot_grid(\n", - " \"convsf\", vmin=0, vmax=3, cmap=plt.get_cmap(\"pyart_HomeyerRainbow\", 4), ax=ax2,\n", - " transform=ccrs.PlateCarree(), ticks=[1 / 3, 1, 2],\n", - " ticklabs=[\"< 5dBZ\", \"Stratiform\", \"Convective\"]\n", + " \"convsf\",\n", + " vmin=0,\n", + " vmax=3,\n", + " cmap=plt.get_cmap(\"pyart_HomeyerRainbow\", 4),\n", + " ax=ax2,\n", + " transform=ccrs.PlateCarree(),\n", + " ticks=[1 / 3, 1, 2],\n", + " ticklabs=[\"< 5dBZ\", \"Stratiform\", \"Convective\"],\n", ")\n", "\n", "# third panel - reclass (Bottom Right)\n", "ax3 = plt.subplot(1, 3, 3, projection=projection)\n", "display.plot_grid(\n", - " \"wt_reclass\", vmin=0, vmax=4, cmap=plt.get_cmap(\"pyart_HomeyerRainbow\", 4), ax=ax3,\n", - " transform=ccrs.PlateCarree(), ticks=[0.5, 1.5, 2.5, 3.5],\n", - " ticklabs=[\"< 5dBZ\", \"Non-Convective\", \"Convective (Mixed)\", \"Convective (Cores)\"]\n", + " \"wt_reclass\",\n", + " vmin=0,\n", + " vmax=4,\n", + " cmap=plt.get_cmap(\"pyart_HomeyerRainbow\", 4),\n", + " ax=ax3,\n", + " transform=ccrs.PlateCarree(),\n", + " ticks=[0.5, 1.5, 2.5, 3.5],\n", + " ticklabs=[\"< 5dBZ\", \"Non-Convective\", \"Convective (Mixed)\", \"Convective (Cores)\"],\n", ")\n", "\n", "# Show the plot\n", - "plt.show()\n" + "plt.show()" ] }, { diff --git a/pyart/retrieve/__init__.py b/pyart/retrieve/__init__.py index f189584fff..0deedf7f8f 100644 --- a/pyart/retrieve/__init__.py +++ b/pyart/retrieve/__init__.py @@ -10,7 +10,7 @@ from .echo_class import get_freq_band # noqa from .echo_class import hydroclass_semisupervised # noqa from .echo_class import steiner_conv_strat # noqa -from .echo_class import conv_strat_raut #noqa +from .echo_class import conv_strat_raut # noqa from .gate_id import fetch_radar_time_profile, map_profile_to_gates # noqa from .kdp_proc import kdp_maesaka, kdp_schneebeli, kdp_vulpiani # noqa from .qpe import est_rain_rate_a # noqa diff --git a/pyart/testing/sample_objects.py b/pyart/testing/sample_objects.py index 31483841a3..0b0f88c669 100644 --- a/pyart/testing/sample_objects.py +++ b/pyart/testing/sample_objects.py @@ -378,8 +378,9 @@ def make_normal_storm(sigma, mu): return test_grid -def make_gaussian_storm_grid(min_value=5, max_value=45, grid_len=32, - sigma=0.2, mu=0.0, masked_boundary=3): +def make_gaussian_storm_grid( + min_value=5, max_value=45, grid_len=32, sigma=0.2, mu=0.0, masked_boundary=3 +): """ Make a 1 km resolution grid with a Gaussian storm pattern at the center, with two layers having the same data and masked boundaries. @@ -404,20 +405,25 @@ def make_gaussian_storm_grid(min_value=5, max_value=45, grid_len=32, # Create an empty Py-ART grid grid_shape = (2, grid_len, grid_len) - grid_limits = ((1000, 1000), (-grid_len*1000/2, grid_len*1000/2), (-grid_len*1000/2, grid_len*1000/2)) + grid_limits = ( + (1000, 1000), + (-grid_len * 1000 / 2, grid_len * 1000 / 2), + (-grid_len * 1000 / 2, grid_len * 1000 / 2), + ) grid = make_empty_grid(grid_shape, grid_limits) # Creating a grid with Gaussian distribution values x, y = np.meshgrid(np.linspace(-1, 1, grid_len), np.linspace(-1, 1, grid_len)) - d = np.sqrt(x*x + y*y) - gaussian = np.exp(-((d - mu)**2 / (2.0 * sigma**2))) + d = np.sqrt(x * x + y * y) + gaussian = np.exp(-((d - mu) ** 2 / (2.0 * sigma**2))) # Normalize and scale the Gaussian distribution - gaussian_normalized = (gaussian - np.min(gaussian)) / (np.max(gaussian) - np.min(gaussian)) + gaussian_normalized = (gaussian - np.min(gaussian)) / ( + np.max(gaussian) - np.min(gaussian) + ) storm_intensity = gaussian_normalized * (max_value - min_value) + min_value storm_intensity = np.stack([storm_intensity, storm_intensity]) - # Apply thresholds for storm intensity and masking mask = np.zeros_like(storm_intensity, dtype=bool) mask[:, :masked_boundary, :] = True @@ -425,7 +431,6 @@ def make_gaussian_storm_grid(min_value=5, max_value=45, grid_len=32, mask[:, :, :masked_boundary] = True mask[:, :, -masked_boundary:] = True - storm_intensity = np.ma.array(storm_intensity, mask=mask) # Prepare dictionary for Py-ART grid fields rdic = {"data": storm_intensity, "long_name": "reflectivity", "units": "dBz"} diff --git a/tests/retrieve/test_echo_class.py b/tests/retrieve/test_echo_class.py index 8249f84913..ce9e276250 100644 --- a/tests/retrieve/test_echo_class.py +++ b/tests/retrieve/test_echo_class.py @@ -302,7 +302,6 @@ def test_standardize(): pytest.raises(ValueError, pyart.retrieve.echo_class._standardize, rhohv, "foo") - def test_conv_strat_raut_outDict_valid(): """ Test that function returns a valid dictionary with all expected keys'. @@ -396,4 +395,3 @@ def test_conv_strat_raut_results_correct(): masked_reclass = np.expand_dims(masked_reclass, axis=0) assert_allclose(masked_reclass, wtclass["wt_reclass"]["data"], atol=0.1) - diff --git a/tests/testing/test_sample_objects.py b/tests/testing/test_sample_objects.py index 10f85b1a94..1cdaad28c1 100644 --- a/tests/testing/test_sample_objects.py +++ b/tests/testing/test_sample_objects.py @@ -21,30 +21,43 @@ def test_gaussian_storm_grid_results_correct(): gaussian_storm_2d = make_gaussian_storm_grid() # Test Shape - assert gaussian_storm_2d.fields['reflectivity']['data'].shape == (2, grid_len, grid_len), "Grid shape mismatch" + assert gaussian_storm_2d.fields["reflectivity"]["data"].shape == ( + 2, + grid_len, + grid_len, + ), "Grid shape mismatch" # Test Data - assert gaussian_storm_2d.fields['reflectivity']['data'] is not None, "No data in reflectivity field" + assert ( + gaussian_storm_2d.fields["reflectivity"]["data"] is not None + ), "No data in reflectivity field" # Test Masking - mask = gaussian_storm_2d.fields['reflectivity']['data'].mask + mask = gaussian_storm_2d.fields["reflectivity"]["data"].mask assert np.all(mask[:, :mask_margin, :]), "Masking at the boundary is incorrect" assert np.all(mask[:, -mask_margin:, :]), "Masking at the boundary is incorrect" assert np.all(mask[:, :, :mask_margin]), "Masking at the boundary is incorrect" assert np.all(mask[:, :, -mask_margin:]), "Masking at the boundary is incorrect" - - storm_data = gaussian_storm_2d.fields['reflectivity']['data'] + storm_data = gaussian_storm_2d.fields["reflectivity"]["data"] # Test for Max and Min - assert np.isclose(np.max(storm_data), max_value), "Maximum value does not match expected" - assert np.isclose(np.min(storm_data[~storm_data.mask]), min_value), "Minimum value does not match expected" + assert np.isclose( + np.max(storm_data), max_value + ), "Maximum value does not match expected" + assert np.isclose( + np.min(storm_data[~storm_data.mask]), min_value + ), "Minimum value does not match expected" # Test Mean and SD expected_mean = 8.666844653650797 expected_std = 7.863066829145 - assert np.isclose(np.mean(storm_data), expected_mean, atol=5), "Mean value out of expected range" - assert np.isclose(np.std(storm_data), expected_std, atol=5), "Standard deviation out of expected range" + assert np.isclose( + np.mean(storm_data), expected_mean, atol=5 + ), "Mean value out of expected range" + assert np.isclose( + np.std(storm_data), expected_std, atol=5 + ), "Standard deviation out of expected range" # Test Central Value assert storm_data[0, 15, 15] == max_value, "Maximum value is not at the center" From 2c93f6b1c1f13d6e078108b0ddb424169ce0af22 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Wed, 10 Jan 2024 16:06:39 -0600 Subject: [PATCH 53/54] DOCS:minor docstrig chages from Zach --- pyart/retrieve/_echo_class_wt.py | 74 ++++++++++++++++---------------- pyart/testing/sample_objects.py | 6 ++- 2 files changed, 41 insertions(+), 39 deletions(-) diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py index 7039304189..e9d38365e3 100644 --- a/pyart/retrieve/_echo_class_wt.py +++ b/pyart/retrieve/_echo_class_wt.py @@ -35,18 +35,18 @@ def wavelet_reclass( First, convert dBZ to rain rates using standard Z-R relationship or user given coefficients. This is to transform the normally distributed dBZ to gamma-like distribution, enhancing the structure of the field. - Parameters: - =========== - dbz_data: ndarray + Parameters + ---------- + dbz_data : ndarray 2D array containing radar data. Last dimension should be levels. - res_km: float + res_km : float Resolution of the radar data in km - scale_break: int + scale_break : int Calculated scale break between convective and stratiform scales. Dyadically spaced in grid pixels. - Returns: - ======== - wt_class: ndarray + Returns + ------- + wt_class : ndarray Precipitation type classification: 0. N/A 1. stratiform/non-convective, 2. convective cores and 3. moderate+transitional (mix) convective regions. @@ -83,20 +83,20 @@ def conv_wavelet_sum(dbz_data, zr_a, zr_b, scale_break): """ Computes the sum of wavelet transform components for convective scales from dBZ data. - Parameters: - =========== - dbz_data: ndarray + Parameters + ------------ + dbz_data : ndarray 2D array containing radar dBZ data. - zr_a, zr_b: float + zr_a, zr_b : float Coefficients for the Z-R relationship. - res_km: float + res_km : float Resolution of the radar data in km. - scale_break: int + scale_break : int Calculated scale break (in pixels) between convective and stratiform scales - Returns: - ======== - wt_sum: ndarray + Returns + --------- + wt_sum : ndarray Sum of convective scale wavelet transform components. """ try: @@ -136,16 +136,16 @@ def label_classes( min_reflectivity = 10 # pixels below this value are not classified. conv_min_refl = 30 # pixel below this value are not convective. This works for most cases. - Parameters: - =========== - wt_sum: ndarray + Parameters + ----------- + wt_sum : ndarray Integrated wavelet transform - vol_data: ndarray + vol_data : ndarray Array, vector or matrix of data - Returns: - ======== - wt_class: ndarray + Returns + --------- + wt_class : ndarray Precipitation type classification. """ @@ -176,16 +176,16 @@ def calc_scale_break(res_meters, conv_scale_km): Compute scale break for convection and stratiform regions. WT will be computed upto this scale and features will be designated as convection. - Parameters: - =========== - res_meters: float + Parameters + ----------- + res_meters : float resolution of the image. - conv_scale_km: float + conv_scale_km : float expected size of spatial variations due to convection. - Returns: - ======== - dyadic: int + Returns + -------- + dyadic scale break : int integer scale break in dyadic scale. """ res_km = res_meters / 1000 @@ -206,16 +206,16 @@ def atwt2d(data2d, max_scale=-1): @authors: Bhupendra A. Raut and Dileep M. Puranik @references: Press et al. (1992) Numerical Recipes in C. - Parameters: - =========== - data2d: ndarray + Parameters + ----------- + data2d : ndarray 2D image as array or matrix. - max_scale: + max_scale : Computes wavelets up to max_scale. Leave blank for maximum possible scales. - Returns: - =========== + Returns + --------- tuple of ndarray ATWT of input image and the final smoothed image or background image. """ diff --git a/pyart/testing/sample_objects.py b/pyart/testing/sample_objects.py index 0b0f88c669..e9fc2833a4 100644 --- a/pyart/testing/sample_objects.py +++ b/pyart/testing/sample_objects.py @@ -385,7 +385,8 @@ def make_gaussian_storm_grid( Make a 1 km resolution grid with a Gaussian storm pattern at the center, with two layers having the same data and masked boundaries. - Parameters: + Parameters + ----------- min_value : float Minimum value of the storm intensity. max_value : float @@ -399,7 +400,8 @@ def make_gaussian_storm_grid( masked_boundary : int Number of pixels around the edge to be masked. - Returns: + Returns + -------- A Py-ART grid with the Gaussian storm field added. """ From bac411b0e97917f8b601fb809bfb6697fb038ad3 Mon Sep 17 00:00:00 2001 From: Bhupendra Raut Date: Wed, 10 Jan 2024 16:22:20 -0600 Subject: [PATCH 54/54] DOCS: testing precommit --- pyart/retrieve/echo_class.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index dd085b3b95..039034bfa5 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -998,14 +998,15 @@ def conv_strat_raut( override_checks=False, ): """ - A fast method to classify radar echoes into convective cores, mixed convection, and stratiform regions. + A computationally efficient method to classify radar echoes into convective cores, mixed convection, + and stratiform regions for gridded radar reflectivity field. - This function uses à trous wavelet transform (ATWT) for multiresolution (scale) analysis of radar field, - focusing on precipitation structure over reflectivity thresholds for classification (Raut et al 2008, 2020). + This function uses à trous wavelet transform (ATWT) for multiresolution (i.e. scale) analysis of radar field, + focusing on precipitation structure over reflectivity thresholds for robust echo classification (Raut et al 2008, 2020). Parameters ---------- - grid : Grid + grid : PyART Grid Grid object containing radar data. refl_field : str Field name for reflectivity data in the Py-ART grid object. @@ -1048,7 +1049,7 @@ def conv_strat_raut( Returns ------- - dict: + dict : A dictionary structured as a Py-ART grid field, suitable for adding to a Py-ART Grid object. The dictionary contains the classification data and associated metadata. The classification categories are as follows: - 3: Convective Cores: associated with strong updrafts and active collision-coalescence.