diff --git a/pcmdi_metrics/io/__init__.py b/pcmdi_metrics/io/__init__.py deleted file mode 100644 index 397b65316..000000000 --- a/pcmdi_metrics/io/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -# init for pcmdi_metrics.io -from .xcdat_openxml import xcdat_open # noqa # isort:skip -from . import base # noqa -from .base import MV2Json # noqa -from .default_regions_define import load_regions_specs # noqa -from .default_regions_define import region_subset # noqa -from .xcdat_dataset_io import ( # noqa - get_axis_list, - get_data_list, - get_latitude_bounds_key, - get_latitude_key, - get_latitude, - get_latitude_bounds, - get_longitude_bounds_key, - get_longitude_key, - get_longitude, - get_longitude_bounds, - get_time, - get_time_bounds, - get_time_bounds_key, - get_time_key, - select_subset, - da_to_ds, -) diff --git a/pcmdi_metrics/io/base.py b/pcmdi_metrics/io/base.py deleted file mode 100755 index 85bbe1eeb..000000000 --- a/pcmdi_metrics/io/base.py +++ /dev/null @@ -1,878 +0,0 @@ -import copy -import hashlib -import json -import logging -import os -import re -from collections import OrderedDict -from collections.abc import Mapping - -import cdat_info -import cdms2 -import cdp.cdp_io -import cdutil -import genutil -import MV2 -import numpy -import xcdat -import xcdat as xc - -import pcmdi_metrics -from pcmdi_metrics import LOG_LEVEL -from pcmdi_metrics.utils import StringConstructor - -value = 0 -cdms2.setNetcdfShuffleFlag(value) # where value is either 0 or 1 -cdms2.setNetcdfDeflateFlag(value) # where value is either 0 or 1 -# where value is a integer between 0 and 9 included -cdms2.setNetcdfDeflateLevelFlag(value) -logging.getLogger("pcmdi_metrics").setLevel(LOG_LEVEL) - -try: - basestring # noqa -except Exception: - basestring = str - - -# Convert cdms MVs to json -def MV2Json(data, dic={}, struct=None): - if struct is None: - struct = [] - if not isinstance(data, cdms2.tvariable.TransientVariable) and dic != {}: - raise RuntimeError("MV2Json needs a cdms2 transient variable as input") - if not isinstance(data, cdms2.tvariable.TransientVariable): - return data, struct # we reach the end - else: - axis = data.getAxis(0) - if axis.id not in struct: - struct.append(axis.id) - for i, name in enumerate(axis): - dic[name], _ = MV2Json(data[i], {}, struct) - return dic, struct - - -# Group merged axes -def groupAxes(axes, ids=None, separator="_"): - if ids is None: - ids = [ax.id for ax in axes] - if len(ids) != len(axes): - raise RuntimeError("You need to pass as many ids as axes") - final = [] - while len(axes) > 0: - axis = axes.pop(-1) - if final == []: - final = [str(v) for v in axis] - else: - tmp = final - final = [] - for v1 in axis: - for v2 in tmp: - final += ["{}{}{}".format(v1, separator, v2)] - return cdms2.createAxis(final, id=separator.join(ids)) - - -# cdutil region object need a serializer -def update_dict(d, u): - for k, v in u.items(): - if isinstance(v, Mapping): - r = update_dict(d.get(k, {}), v) - d[k] = r - else: - d[k] = u[k] - return d - - -def generateProvenance(): - extra_pairs = { - "matplotlib": "matplotlib ", - "scipy": "scipy", - "xcdat": "xcdat", - "xarray": "xarray", - } - prov = cdat_info.generateProvenance(extra_pairs=extra_pairs) - prov["packages"]["PMP"] = pcmdi_metrics.version.__git_tag_describe__ - prov["packages"][ - "PMPObs" - ] = "See 'References' key below, for detailed obs provenance information." - return prov - - -def sort_human(input_list): - lst = copy.copy(input_list) - - def convert(text): - return int(text) if text.isdigit() else text - - def alphanum(key): - return [convert(c) for c in re.split("([0-9]+)", key)] - - lst.sort(key=alphanum) - return lst - - -def scrap(data, axis=0): - originalOrder = data.getOrder(ids=True) - if axis not in ["x", "y", "z", "t"] and not isinstance(axis, int): - order = "({})...".format(axis) - else: - order = "{}...".format(axis) - new = data(order=order) - axes = new.getAxisList() # Save for later - new = MV2.array(new.asma()) # lose dims - for i in range(new.shape[0] - 1, -1, -1): - tmp = new[i] - if not isinstance(tmp, (float, numpy.float)) and tmp.mask.all(): - a = new[:i] - b = new[i + 1 :] - if b.shape[0] == 0: - new = a - else: - new = MV2.concatenate((a, b)) - newAxis = [] - for v in new.getAxis(0): - newAxis.append(axes[0][int(v)]) - ax = cdms2.createAxis(newAxis, id=axes[0].id) - axes[0] = ax - new.setAxisList(axes) - return new(order=originalOrder) - - -class CDMSDomainsEncoder(json.JSONEncoder): - def default(self, o): - components = o.components()[0].kargs - args = ",".join(["%s=%s" % (key, val) for key, val in components.items()]) - return {o.id: "cdutil.region.domain(%s)" % args} - - -class Base(cdp.cdp_io.CDPIO, StringConstructor): - def __init__(self, root, file_template, file_mask_template=None): - StringConstructor.__init__(self, root + "/" + file_template) - self.target_grid = None - self.mask = None - self.target_mask = None - self.regrid_tool = "esmf" - self.file_mask_template = file_mask_template - self.root = root - self.type = "" - self.setup_cdms2() - - def __call__(self): - path = os.path.abspath(StringConstructor.__call__(self)) - if self.type in path: - return path - else: - return path + "." + self.type - - def read(self): - pass - - def write( - self, - data, - type="json", - mode="w", - include_YAML=False, - include_history=False, - include_script=False, - *args, - **kwargs, - ): - self.type = type.lower() - file_name = self() - dir_path = os.path.split(file_name)[0] - - if not os.path.exists(dir_path): - try: - os.makedirs(dir_path, exist_ok=True) - except Exception: - logging.getLogger("pcmdi_metrics").error( - "Could not create output directory: %s" % dir_path - ) - - if self.type == "json": - json_version = float( - kwargs.get("json_version", data.get("json_version", 3.0)) - ) - json_structure = kwargs.get( - "json_structure", data.get("json_structure", None) - ) - if json_version >= 3.0 and json_structure is None: - raise Exception( - "json_version 3.0 of PMP requires json_structure to be passed" - + "to the write function or part of the dictionary dumped" - ) - for k in ["json_structure", "json_version"]: - if k in kwargs: - del kwargs[k] - data["json_version"] = json_version - data["json_structure"] = json_structure - - if mode == "r+" and os.path.exists(file_name): - f = open(file_name) - out_dict = json.load(f) - else: - out_dict = OrderedDict({"provenance": generateProvenance()}) - f = open(file_name, "w") - update_dict(out_dict, data) - if "yaml" in out_dict["provenance"]["conda"]: - if include_YAML: - out_dict["YAML"] = out_dict["provenance"]["conda"]["yaml"] - del out_dict["provenance"]["conda"]["yaml"] - - if not include_script: - if "script" in out_dict["provenance"].keys(): - del out_dict["provenance"]["script"] - if not include_history: - if "history" in out_dict["provenance"].keys(): - del out_dict["provenance"]["history"] - json.dump(out_dict, f, cls=CDMSDomainsEncoder, *args, **kwargs) - f.close() - - elif self.type in ["asc", "ascii", "txt"]: - f = open(file_name, "w") - for key in list(data.keys()): - f.write("%s %s\n" % (key, data[key])) - f.close() - - elif self.type == "nc": - """ - f = cdms2.open(file_name, "w") - f.write(data, *args, **kwargs) - f.metrics_git_sha1 = pcmdi_metrics.__git_sha1__ - f.uvcdat_version = cdat_info.get_version() - f.close() - """ - data.to_netcdf(file_name) - - else: - logging.getLogger("pcmdi_metrics").error("Unknown type: %s" % type) - raise RuntimeError("Unknown type: %s" % type) - - logging.getLogger("pcmdi_metrics").info( - "Results saved to a %s file: %s" % (type, file_name) - ) - - def write_cmec(self, *args, **kwargs): - """Converts json file to cmec compliant format.""" - - file_name = self() - # load json file - try: - f = open(file_name, "r") - data = json.load(f) - f.close() - except Exception: - logging.getLogger("pcmdi_metrics").error( - "Could not load metrics file: %s" % file_name - ) - - # create dimensions - cmec_data = {"DIMENSIONS": {}, "SCHEMA": {}} - cmec_data["DIMENSIONS"] = {"dimensions": {}, "json_structure": []} - cmec_data["SCHEMA"] = {"name": "CMEC", "package": "PMP", "version": "v1"} - - # copy other fields except results - for key in data: - if key not in ["json_structure", "RESULTS"]: - cmec_data[key] = data[key] - - # move json structure - cmec_data["DIMENSIONS"]["json_structure"] = data["json_structure"] - - # recursively process json - def recursive_replace(json_dict, extra_fields): - new_dict = json_dict.copy() - # replace blank keys with unspecified - if "" in new_dict: - new_dict["Unspecified"] = new_dict.pop("") - - for key in new_dict: - if key != "attributes": - if isinstance(new_dict[key], dict): - # move extra fields into attributes key - atts = {} - for field in extra_fields: - if field in new_dict[key]: - atts[field] = new_dict[key].pop(field, None) - if atts: - new_dict[key]["attributes"] = atts - # process sub-dictionary - tmp_dict = recursive_replace(new_dict[key], extra_fields) - new_dict[key] = tmp_dict - # convert string metrics to float - if isinstance(new_dict[key], str): - new_dict[key] = float(new_dict[key]) - return new_dict - - extra_fields = [ - "source", - "metadata", - "units", - "SimulationDescription", - "InputClimatologyFileName", - "InputClimatologyMD5", - "InputRegionFileName", - "InputRegionMD5", - "best_matching_model_eofs__cor", - "best_matching_model_eofs__rms", - "best_matching_model_eofs__tcor_cbf_vs_eof_pc", - "period", - "target_model_eofs", - "analysis_time_window_end_year", - "analysis_time_window_start_year", - ] - # clean up formatting in RESULTS section - cmec_data["RESULTS"] = recursive_replace(data["RESULTS"], extra_fields) - - # Populate dimensions fields - def get_dimensions(json_dict, json_structure): - keylist = {} - level = 0 - while level < len(json_structure): - if isinstance(json_dict, dict): - first_key = list(json_dict.items())[0][0] - # skip over attributes key when building dimensions - if first_key == "attributes": - first_key = list(json_dict.items())[1][0] - dim = json_structure[level] - keys = {key: {} for key in json_dict if key != "attributes"} - keylist[dim] = keys - json_dict = json_dict[first_key] - level += 1 - return keylist - - dimensions = get_dimensions(cmec_data["RESULTS"].copy(), data["json_structure"]) - cmec_data["DIMENSIONS"]["dimensions"] = dimensions - - cmec_file_name = file_name.replace(".json", "_cmec.json") - f_cmec = open(cmec_file_name, "w") - json.dump(cmec_data, f_cmec, cls=CDMSDomainsEncoder, *args, **kwargs) - f_cmec.close() - logging.getLogger("pcmdi_metrics").info( - "CMEC results saved to a %s file: %s" % ("json", cmec_file_name) - ) - - def get(self, var, var_in_file=None, region={}, *args, **kwargs): - self.variable = var - self.var_from_file = self.extract_var_from_file( - var, var_in_file, *args, **kwargs - ) - - self.region = region - if self.region is None: - self.region = {} - self.value = self.region.get("value", None) - - if self.is_masking(): - self.var_from_file = self.mask_var(self.var_from_file) - - self.var_from_file = self.set_target_grid_and_mask_in_var( - self.var_from_file, var - ) - - self.var_from_file = self.set_domain_in_var(self.var_from_file, self.region) - - return self.var_from_file - - def extract_var_from_file(self, var, var_in_file, *args, **kwargs): - if var_in_file is None: - var_in_file = var - - try: - ds = xc.open_mfdataset(self(), data_var=var_in_file, decode_times=True) - except Exception: - ds = xc.open_mfdataset( - self(), data_var=var_in_file, decode_times=False - ) # Temporary part to read in cdms written obs4MIP AC files - - if "level" in list(kwargs.keys()): - level = kwargs["level"] - ds = ds.sel(plev=level) - - extracted_var = ds - - return extracted_var - - def is_masking(self): - if self.value is not None: - return True - else: - return False - - def mask_var(self, var): - """ - self: - var: - """ - var_shape = tuple(var.dims[d] for d in ["lat", "lon"]) - - if self.mask is None: - self.set_file_mask_template() - self.mask = self.get_mask_from_var(var) - # if self.mask.shape != var.shape: - if self.mask.shape != var_shape: - dummy, mask = genutil.grower(var, self.mask) - else: - mask = self.target_mask - mask = MV2.not_equal(mask, self.value) - return MV2.masked_where(mask, var) - - def set_target_grid_and_mask_in_var(self, var, var_in_file): - """ - self: object - self(): string, path to input file - """ - if self.target_grid is not None: - var = var.regridder.horizontal( - var_in_file, self.target_grid, tool=self.regrid_tool - ) - if self.target_mask is not None: - # if self.target_mask.shape != var.shape: - if self.target_mask.shape != var[var_in_file].shape: - dummy, mask = genutil.grower(var, self.target_mask) - else: - mask = self.target_mask - var = MV2.masked_where(mask, var) - return var - - def set_domain_in_var(self, var, region): - """ - self: - var: - region: , e.g., {'domain': Selector(), 'id': 'NHEX'} - """ - region_id = region["id"] - from pcmdi_metrics.io import load_regions_specs, region_subset - - regions_specs = load_regions_specs() - if region_id not in ["global", "land", "ocean"]: - var = region_subset(var, regions_specs, region=region_id) - - return var - - def set_file_mask_template(self): - if isinstance(self.file_mask_template, basestring): - self.file_mask_template = Base( - self.root, - self.file_mask_template, - {"domain": self.region.get("domain", None)}, - ) - - def get_mask_from_var(self, var): - try: - # o_mask = self.file_mask_template.get("sftlf") - o_mask = self.file_mask_template.get("sftlf", var_in_file="sftlf") - except Exception: - o_mask = ( - cdutil.generateLandSeaMask(var, regridTool=self.regrid_tool).filled(1.0) - * 100.0 - ) - o_mask = MV2.array(o_mask) - o_mask.setAxis(-1, var.getLongitude()) - o_mask.setAxis(-2, var.getLatitude()) - return o_mask - - def set_target_grid(self, target, regrid_tool="esmf", regrid_method="linear"): - self.regrid_tool = regrid_tool - self.regrid_method = regrid_method - if target == "2.5x2.5": - # self.target_grid = cdms2.createUniformGrid(-88.875, 72, 2.5, 0, 144, 2.5) - self.target_grid = xcdat.create_uniform_grid( - -88.875, 88.625, 2.5, 0, 357.5, 2.5 - ) - self.target_grid_name = target - elif cdms2.isGrid(target): - self.target_grid = target - self.target_grid_name = target - else: - logging.getLogger("pcmdi_metrics").error("Unknown grid: %s" % target) - raise RuntimeError("Unknown grid: %s" % target) - - def setup_cdms2(self): - cdms2.setNetcdfShuffleFlag(0) # Argument is either 0 or 1 - cdms2.setNetcdfDeflateFlag(0) # Argument is either 0 or 1 - cdms2.setNetcdfDeflateLevelFlag(0) # Argument is int between 0 and 9 - - def hash(self, block_size=65536): - self_file = open(self(), "rb") - buffer = self_file.read(block_size) - hasher = hashlib.md5() - while len(buffer) > 0: - hasher.update(buffer) - buffer = self_file.read(block_size) - self_file.close() - return hasher.hexdigest() - - -class JSONs(object): - def addDict2Self(self, json_dict, json_struct, json_version): - if float(json_version) == 1.0: - V = json_dict[list(json_dict.keys())[0]] - for model in list(V.keys()): # loop through models - m = V[model] - for ref in list(m.keys()): - aref = m[ref] - if not ( - isinstance(aref, dict) and "source" in aref - ): # not an obs key - continue - reals = list(aref.keys()) - src = reals.pop(reals.index("source")) - for real in reals: - areal = aref[real] - areal2 = {"source": src} - for region in list(areal.keys()): - reg = areal[region] - if region == "global": - region2 = "" - else: - region2 = region + "_" - areal2[region2 + "global"] = {} - areal2[region2 + "NHEX"] = {} - areal2[region2 + "SHEX"] = {} - areal2[region2 + "TROPICS"] = {} - key_stats = list(reg.keys()) - for k in key_stats: - if k[:7] == "custom_": - continue - else: - sp = k.split("_") - new_key = "_".join(sp[:-1]) - domain = sp[-1] - if domain == "GLB": - domain = "global" - sp = new_key.split("_") - stat = "_".join(sp[:-1]) - stat_dict = areal2[region2 + domain].get(stat, {}) - season = sp[-1] - season_dict = stat_dict - stat_dict[season] = reg[k] - if stat in areal2[region2 + domain]: - areal2[region2 + domain][stat].update(stat_dict) - else: - areal2[region2 + domain][stat] = stat_dict - # Now we can replace the realization with the correctly - # formatted one - aref[real] = areal2 - # restore ref into model - m[ref] = aref - elif float(json_version) == 2.0: - V = json_dict[list(json_dict.keys())[0]] - for model in list(V.keys()): # loop through models - m = V[model] - for ref in list(m.keys()): - aref = m[ref] - if not ( - isinstance(aref, dict) and "source" in aref - ): # not an obs key - continue - reals = list(aref.keys()) - src = reals.pop(reals.index("source")) - for real in reals: - areal = aref[real] - for region in list(areal.keys()): - reg = areal[region] - key_stats = list(reg.keys()) - for k in key_stats: - if k[:7] == "custom_": - continue - sp = k.split("_") - season = sp[-1] - stat = "_".join(sp[:-1]) - stat_dict = reg.get(stat, {}) - season_dict = stat_dict.get(season, {}) - season_dict[season] = reg[k] - # if stat_dict.has_key(stat): - # stat_dict[stat].update(season_dict) - # else: - # stat_dict[stat]=season_dict - del reg[k] - if stat in reg: - reg[stat].update(season_dict) - else: - reg[stat] = season_dict - aref[real] = areal - # restore ref into model - m[ref] = aref - V[model] = m - json_dict[list(json_dict.keys())[0]] = V - update_dict(self.data, json_dict) - - def get_axes_values_recursive(self, depth, max_depth, data, values): - for k in list(data.keys()): - if k not in self.ignored_keys and ( - isinstance(data[k], dict) or depth == max_depth - ): - values[depth].add(k) - if depth != max_depth: - self.get_axes_values_recursive( - depth + 1, max_depth, data[k], values - ) - - def get_array_values_from_dict_recursive(self, out, ids, nms, axval, axes): - if len(axes) > 0: - for i, val in enumerate(axes[0][:]): - self.get_array_values_from_dict_recursive( - out, - list(ids) - + [ - i, - ], - list(nms) + [axes[0].id], - list(axval) - + [ - val, - ], - axes[1:], - ) - else: - vals = self.data - for k in axval: - try: - vals = vals[k] - except Exception: - vals = 9.99e20 - try: - out[tuple(ids)] = float(vals) - except Exception: - out[tuple(ids)] = 9.99e20 - - def __init__( - self, - files=[], - structure=[], - ignored_keys=[], - oneVariablePerFile=True, - sortHuman=True, - ): - self.json_version = 3.0 - self.json_struct = structure - self.data = {} - self.axes = None - self.ignored_keys = ignored_keys - self.oneVariablePerFile = oneVariablePerFile - self.sortHuman = sortHuman - if len(files) == 0: - raise Exception("You need to pass at least one file") - - for fnm in files: - self.addJson(fnm) - - def addJson(self, filename): - f = open(filename) - tmp_dict = json.load(f) - json_struct = tmp_dict.get("json_structure", list(self.json_struct)) - json_version = tmp_dict.get("json_version", self.json_version) - if self.oneVariablePerFile and json_struct[0] == "variable": - json_struct = json_struct[1:] - if self.oneVariablePerFile and json_struct[0] != "variable": - json_struct.insert(0, "variable") - var = tmp_dict.get("Variable", None) - if var is None: # Not stored in json, need to get from file name - fnm = os.path.basename(filename) - varnm = fnm.split("_")[0] - else: - varnm = var["id"] - if "level" in var: - varnm += "-%i" % int(var["level"] / 100.0) - tmp_dict = {varnm: tmp_dict["RESULTS"]} - else: - tmp_dict = tmp_dict["RESULTS"] - if json_struct != self.json_struct and self.json_struct == []: - self.json_struct = json_struct - self.addDict2Self(tmp_dict, json_struct, json_version) - - def getAxis(self, axis): - axes = self.getAxisList() - for a in axes: - if a.id == axis: - return a - return None - - def getAxisIds(self): - axes = self.getAxisList() - return [ax.id for ax in axes] - - def getAxisList(self): - values = [] - axes = [] - for a in self.json_struct: - values.append(set()) - self.get_axes_values_recursive(0, len(self.json_struct) - 1, self.data, values) - autoBounds = cdms2.getAutoBounds() - cdms2.setAutoBounds("off") - if self.sortHuman: - sortFunc = sort_human - else: - sortFunc = sorted - for i, nm in enumerate(self.json_struct): - axes.append(cdms2.createAxis(sortFunc(list(values[i])), id=nm)) - self.axes = axes - cdms2.setAutoBounds(autoBounds) - return self.axes - - def __call__(self, merge=[], **kargs): - """Returns the array of values""" - # First clean up kargs - if "merge" in kargs: - merge = kargs["merge"] - del kargs["merge"] - order = None - axes_ids = self.getAxisIds() - if "order" in kargs: - # If it's an actual axis assume that it's what user wants - # Otherwise it's an out order keyword - if "order" not in axes_ids: - order = kargs["order"] - del kargs["order"] - ab = cdms2.getAutoBounds() - cdms2.setAutoBounds("off") - axes = self.getAxisList() - if merge != []: - if isinstance(merge[0], str): - merge = [ - merge, - ] - if merge != []: - for merger in merge: - for merge_axis_id in merger: - if merge_axis_id not in axes_ids: - raise RuntimeError( - "You requested to merge axis is '{}' which is not valid. Axes: {}".format( - merge_axis_id, axes_ids - ) - ) - sh = [] - ids = [] - used_ids = [] - for a in axes: - # Regular axis not a merged one - sh.append(len(a)) # store length to construct array shape - ids.append(a.id) # store ids - - used_ids.append(a.id) - - # first let's see which vars are actually asked for - # for now assume all keys means restriction on dims - if not isinstance(merge, (list, tuple)): - raise RuntimeError( - "merge keyword must be a list of dimensions to merge together" - ) - - if len(merge) > 0 and not isinstance(merge[0], (list, tuple)): - merge = [ - merge, - ] - - for axis_id in kargs: - if axis_id not in ids: - raise ValueError("Invalid axis '%s'" % axis_id) - index = ids.index(axis_id) - value = kargs[axis_id] - if isinstance(value, basestring): - value = [value] - if not isinstance(value, (list, tuple, slice)): - raise TypeError( - "Invalid subsetting type for axis '%s', axes can only be subsetted by string,list or slice" - % axis_id - ) - if isinstance(value, slice): - axes[index] = axes[index].subAxis(value.start, value.stop, value.step) - sh[index] = len(axes[index]) - else: # ok it's a list - for v in value: - if v not in axes[index][:]: - raise ValueError( - "Unkwown value '%s' for axis '%s'" % (v, axis_id) - ) - axis = cdms2.createAxis(value, id=axes[index].id) - axes[index] = axis - sh[index] = len(axis) - - array = numpy.ma.ones(sh, dtype=numpy.float) - # Now let's fill this array - self.get_array_values_from_dict_recursive(array, [], [], [], axes) - - # Ok at this point we need to take care of merged axes - # First let's create the merged axes - axes_to_group = [] - for merger in merge: - merged_axes = [] - for axid in merger: - for ax in axes: - if ax.id == axid: - merged_axes.append(ax) - axes_to_group.append(merged_axes) - new_axes = [groupAxes(grp_axes) for grp_axes in axes_to_group] - sh2 = list(sh) - for merger in merge: - for merger in merge: # loop through all possible merging - merged_indices = [] - for id in merger: - merged_indices.append(axes_ids.index(id)) - for indx in merged_indices: - sh2[indx] = 1 - smallest = min(merged_indices) - for indx in merged_indices: - sh2[smallest] *= sh[indx] - - myorder = [] - for index in range(len(sh)): - if index in myorder: - continue - for merger in merge: - merger = [axes_ids.index(x) for x in merger] - if index in merger and index not in myorder: - for indx in merger: - myorder.append(indx) - if index not in myorder: # ok did not find this one anywhere - myorder.append(index) - - outData = numpy.transpose(array, myorder) - outData = numpy.reshape(outData, sh2) - - yank = [] - for merger in merge: - merger = [axes_ids.index(x) for x in merger] - mn = min(merger) - merger.remove(mn) - yank += merger - yank = sorted(yank, reverse=True) - for yk in yank: - extract = (slice(0, None),) * yk - extract += (0,) - outData = outData[extract] - # Ok now let's apply the newaxes - sub = 0 - outData = MV2.array(outData) - merged_axis_done = [] - for index in range(len(array.shape)): - foundInMerge = False - for imerge, merger in enumerate(merge): - merger = [axes_ids.index(x) for x in merger] - if index in merger: - foundInMerge = True - if imerge not in merged_axis_done: - merged_axis_done.append(imerge) - setMergedAxis = imerge - else: - setMergedAxis = -1 - if not foundInMerge: - outData.setAxis(index - sub, axes[index]) - else: - if setMergedAxis == -1: - sub += 1 - else: - outData.setAxis(index - sub, new_axes[setMergedAxis]) - outData = MV2.masked_greater(outData, 9.98e20) - outData.id = "pmp" - if order is not None: - myorder = "".join(["({})".format(nm) for nm in order]) - outData = outData(order=myorder) - # Merge needs cleaning for extra dims crated - if merge != []: - for i in range(outData.ndim): - outData = scrap(outData, axis=i) - outData = MV2.masked_greater(outData, 9.9e19) - cdms2.setAutoBounds(ab) - return outData diff --git a/pcmdi_metrics/io/default_regions_define.py b/pcmdi_metrics/io/default_regions_define.py deleted file mode 100755 index b360bf090..000000000 --- a/pcmdi_metrics/io/default_regions_define.py +++ /dev/null @@ -1,213 +0,0 @@ -from typing import Union - -import xarray as xr -import xcdat as xc - -# from pcmdi_metrics.io import da_to_ds, get_longitude, select_subset -from .xcdat_dataset_io import da_to_ds, get_longitude, select_subset - - -def load_regions_specs(): - regions_specs = { - # Mean Climate - "global": {}, - "NHEX": {"domain": {"latitude": (30.0, 90)}}, - "SHEX": {"domain": {"latitude": (-90.0, -30)}}, - "TROPICS": {"domain": {"latitude": (-30.0, 30)}}, - "90S50S": {"domain": {"latitude": (-90.0, -50)}}, - "50S20S": {"domain": {"latitude": (-50.0, -20)}}, - "20S20N": {"domain": {"latitude": (-20.0, 20)}}, - "20N50N": {"domain": {"latitude": (20.0, 50)}}, - "50N90N": {"domain": {"latitude": (50.0, 90)}}, - "CONUS": {"domain": {"latitude": (24.7, 49.4), "longitude": (-124.78, -66.92)}}, - "land": {"value": 100}, - "land_NHEX": {"value": 100, "domain": {"latitude": (30.0, 90)}}, - "land_SHEX": {"value": 100, "domain": {"latitude": (-90.0, -30)}}, - "land_TROPICS": {"value": 100, "domain": {"latitude": (-30.0, 30)}}, - "land_CONUS": { - "value": 100, - "domain": {"latitude": (24.7, 49.4), "longitude": (-124.78, -66.92)}, - }, - "ocean": {"value": 0}, - "ocean_NHEX": {"value": 0, "domain": {"latitude": (30.0, 90)}}, - "ocean_SHEX": {"value": 0, "domain": {"latitude": (-90.0, -30)}}, - "ocean_TROPICS": {"value": 0, "domain": {"latitude": (30.0, 30)}}, - "ocean_50S50N": {"value": 0.0, "domain": {"latitude": (-50.0, 50)}}, - "ocean_50S20S": {"value": 0.0, "domain": {"latitude": (-50.0, -20)}}, - "ocean_20S20N": {"value": 0.0, "domain": {"latitude": (-20.0, 20)}}, - "ocean_20N50N": {"value": 0.0, "domain": {"latitude": (20.0, 50)}}, - # Modes of variability - "NAM": {"domain": {"latitude": (20.0, 90), "longitude": (-180, 180)}}, - "NAO": {"domain": {"latitude": (20.0, 80), "longitude": (-90, 40)}}, - "SAM": {"domain": {"latitude": (-20.0, -90), "longitude": (0, 360)}}, - "PNA": {"domain": {"latitude": (20.0, 85), "longitude": (120, 240)}}, - "PDO": {"domain": {"latitude": (20.0, 70), "longitude": (110, 260)}}, - # Monsoon domains for Wang metrics - # All monsoon domains - "AllMW": {"domain": {"latitude": (-40.0, 45.0), "longitude": (0.0, 360.0)}}, - "AllM": {"domain": {"latitude": (-45.0, 45.0), "longitude": (0.0, 360.0)}}, - # North American Monsoon - "NAMM": {"domain": {"latitude": (0.0, 45.0), "longitude": (210.0, 310.0)}}, - # South American Monsoon - "SAMM": {"domain": {"latitude": (-45.0, 0.0), "longitude": (240.0, 330.0)}}, - # North African Monsoon - # "NAFM": {"domain": {"latitude": (0.0, 45.0), "longitude": (310.0, 60.0)}}, - "NAFM": {"domain": {"latitude": (0.0, 45.0), "longitude": (-50, 60.0)}}, - # South African Monsoon - "SAFM": {"domain": {"latitude": (-45.0, 0.0), "longitude": (0.0, 90.0)}}, - # Asian Summer Monsoon - "ASM": {"domain": {"latitude": (0.0, 45.0), "longitude": (60.0, 180.0)}}, - # Australian Monsoon - "AUSM": {"domain": {"latitude": (-45.0, 0.0), "longitude": (90.0, 160.0)}}, - # Monsoon domains for Sperber metrics - # All India rainfall - "AIR": {"domain": {"latitude": (7.0, 25.0), "longitude": (65.0, 85.0)}}, - # North Australian - "AUS": {"domain": {"latitude": (-20.0, -10.0), "longitude": (120.0, 150.0)}}, - # Sahel - "Sahel": {"domain": {"latitude": (13.0, 18.0), "longitude": (-10.0, 10.0)}}, - # Gulf of Guinea - "GoG": {"domain": {"latitude": (0.0, 5.0), "longitude": (-10.0, 10.0)}}, - # North American monsoon - "NAmo": {"domain": {"latitude": (20.0, 37.0), "longitude": (-112.0, -103.0)}}, - # South American monsoon - "SAmo": {"domain": {"latitude": (-20.0, 2.5), "longitude": (-65.0, -40.0)}}, - } - - return regions_specs - - -# def region_subset(ds, regions_specs, region=None): -# """ -# d: xarray.Dataset -# regions_specs: dict -# region: string -# """ -# -# #print("list(regions_specs.keys())", list(regions_specs.keys())) -# -# if (region is None) or ( -# (region is not None) and (region not in list(regions_specs.keys())) -# ): -# print("Error: region not defined") -# else: -# if "domain" in list(regions_specs[region].keys()): -# if "latitude" in list(regions_specs[region]["domain"].keys()): -# lat0 = regions_specs[region]["domain"]["latitude"][0] -# lat1 = regions_specs[region]["domain"]["latitude"][1] -# # proceed subset -# if "latitude" in (ds.coords.dims): -# ds = ds.sel(latitude=slice(lat0, lat1)) -# elif "lat" in (ds.coords.dims): -# ds = ds.sel(lat=slice(lat0, lat1)) -# -# if "longitude" in list(regions_specs[region]["domain"].keys()): -# lon0 = regions_specs[region]["domain"]["longitude"][0] -# lon1 = regions_specs[region]["domain"]["longitude"][1] -# -# # check original dataset longitude range -# if "longitude" in (ds.coords.dims): -# lon_min = ds.longitude.min() -# lon_max = ds.longitude.max() -# elif "lon" in (ds.coords.dims): -# lon_min = ds.lon.min() -# lon_max = ds.lon.max() -# -# # longitude range swap if needed -# if ( -# min(lon0, lon1) < 0 -# ): # when subset region lon is defined in (-180, 180) range -# if ( -# min(lon_min, lon_max) < 0 -# ): # if original data lon range is (-180, 180) no treatment needed -# pass -# else: # if original data lon range is (0, 360), convert swap lon -# ds = xc.swap_lon_axis(ds, to=(-180, 180)) -# -# # proceed subset -# if "longitude" in (ds.coords.dims): -# ds = ds.sel(longitude=slice(lon0, lon1)) -# elif "lon" in (ds.coords.dims): -# ds = ds.sel(lon=slice(lon0, lon1)) -# -# return ds - - -def region_subset( - ds: Union[xr.Dataset, xr.DataArray], - region: str, - data_var: str = "variable", - regions_specs: dict = None, - debug: bool = False, -) -> Union[xr.Dataset, xr.DataArray]: - """_summary_ - - Parameters - ---------- - ds : Union[xr.Dataset, xr.DataArray] - _description_ - region : str - _description_ - data_var : str, optional - _description_, by default None - regions_specs : dict, optional - _description_, by default None - debug: bool, optional - Turn on debug print, by default False - - Returns - ------- - Union[xr.Dataset, xr.DataArray] - _description_ - """ - if isinstance(ds, xr.DataArray): - is_dataArray = True - ds = da_to_ds(ds, data_var) - else: - is_dataArray = False - - if regions_specs is None: - regions_specs = load_regions_specs() - - if "domain" in regions_specs[region]: - if "latitude" in regions_specs[region]["domain"]: - lat0 = regions_specs[region]["domain"]["latitude"][0] - lat1 = regions_specs[region]["domain"]["latitude"][1] - # proceed subset - ds = select_subset(ds, lat=(min(lat0, lat1), max(lat0, lat1))) - if debug: - print("region_subset, latitude subsetted, ds:", ds) - - if "longitude" in regions_specs[region]["domain"]: - lon0 = regions_specs[region]["domain"]["longitude"][0] - lon1 = regions_specs[region]["domain"]["longitude"][1] - - # check original dataset longitude range - lon_min = get_longitude(ds).min().values.item() - lon_max = get_longitude(ds).max().values.item() - - # Check if longitude range swap is needed - if min(lon0, lon1) < 0: - # when subset region lon is defined in (-180, 180) range - if min(lon_min, lon_max) < 0: - # if original data lon range is (-180, 180), no treatment needed - pass - else: - # if original data lon range is (0, 360), convert and swap lon - try: - ds = ds.drop_vars(["time"]) - except: - pass - ds = xc.swap_lon_axis(ds, to=(-180, 180)) - - # proceed subset - # ds = select_subset(ds, lon=(min(lon0, lon1), max(lon0, lon1))) - ds = select_subset(ds, lon=(lon0, lon1)) - if debug: - print("region_subset, longitude subsetted, ds:", ds) - - # return the same type - if is_dataArray: - return ds[data_var] - else: - return ds diff --git a/pcmdi_metrics/io/region_from_file.py b/pcmdi_metrics/io/region_from_file.py deleted file mode 100644 index bcb5144fe..000000000 --- a/pcmdi_metrics/io/region_from_file.py +++ /dev/null @@ -1,33 +0,0 @@ -import geopandas as gpd -import regionmask - - -def region_from_file(data, rgn_path, attr, feature): - # Return data masked from a feature in input file. - # Arguments: - # data: xcdat dataset - # feature: str, name of region - # rgn_path: str, path to file - # attr: str, attribute name - - lon = data["lon"].data - lat = data["lat"].data - - print("Reading region from file.") - try: - regions_df = gpd.read_file(rgn_path) - regions = regionmask.from_geopandas(regions_df, names=attr) - mask = regions.mask(lon, lat) - # Can't match mask by name, rather index of name - val = list(regions_df[attr]).index(feature) - except Exception as e: - print("Error in creating region subset from file:") - raise e - - try: - masked_data = data.where(mask == val) - except Exception as e: - print("Error: Region selection failed.") - raise e - - return masked_data diff --git a/pcmdi_metrics/io/xcdat_dataset_io.py b/pcmdi_metrics/io/xcdat_dataset_io.py deleted file mode 100644 index 2cdc4a194..000000000 --- a/pcmdi_metrics/io/xcdat_dataset_io.py +++ /dev/null @@ -1,199 +0,0 @@ -from typing import Union - -import xarray as xr -import xcdat as xc - -# Internal function - - -def da_to_ds(d: Union[xr.Dataset, xr.DataArray], var: str = "variable") -> xr.Dataset: - """Convert xarray DataArray to Dataset - - Parameters - ---------- - d : Union[xr.Dataset, xr.DataArray] - Input dataArray. If dataset is given, no process will be done - var : str, optional - Name of dataArray, by default "variable" - - Returns - ------- - xr.Dataset - xarray Dataset - - Raises - ------ - TypeError - Raised when given input is not xarray based variables - """ - if isinstance(d, xr.Dataset): - return d.copy() - elif isinstance(d, xr.DataArray): - return d.to_dataset(name=var).bounds.add_missing_bounds().copy() - else: - raise TypeError( - "Input must be an instance of either xarrary.DataArray or xarrary.Dataset" - ) - - -def _find_key( - ds: Union[xr.Dataset, xr.DataArray], axis: str, potential_names: list -) -> str: - try: - key = xc.get_dim_keys(ds, axis) - except Exception as e: - axes = get_axis_list(ds) - key_candidates = [k for k in axes if k.lower() in potential_names] - if len(key_candidates) > 0: - key = key_candidates[0] - else: - data_keys = get_data_list(ds) - key_candidates = [k for k in data_keys if k.lower() in potential_names] - if len(key_candidates) > 0: - key = key_candidates[0] - else: - all_keys = ", ".join(axes + data_keys) - print( - f"Error: Cannot find a proper key name for {axis} from keys:{all_keys} {e}" - ) - return key - - -# Retrieve coordinate key names - - -def get_axis_list(ds: Union[xr.Dataset, xr.DataArray]) -> list[str]: - axes = list(ds.coords.keys()) - return axes - - -def get_data_list(ds: Union[xr.Dataset, xr.DataArray]) -> list[str]: - return list(ds.data_vars.keys()) - - -def get_time_key(ds: Union[xr.Dataset, xr.DataArray]) -> str: - axis = "T" - potential_names = ["time", "t"] - return _find_key(ds, axis, potential_names) - - -def get_latitude_key(ds: Union[xr.Dataset, xr.DataArray]) -> str: - axis = "Y" - potential_names = ["lat", "latitude"] - return _find_key(ds, axis, potential_names) - - -def get_longitude_key(ds: Union[xr.Dataset, xr.DataArray]) -> str: - axis = "X" - potential_names = ["lon", "longitude"] - return _find_key(ds, axis, potential_names) - - -# Retrieve bounds key names - - -def get_time_bounds_key(ds: Union[xr.Dataset, xr.DataArray]) -> str: - lat_key = get_time_key(ds) - return ds[lat_key].attrs["bounds"] - - -def get_latitude_bounds_key(ds: Union[xr.Dataset, xr.DataArray]) -> str: - lat_key = get_latitude_key(ds) - return ds[lat_key].attrs["bounds"] - - -def get_longitude_bounds_key(ds: Union[xr.Dataset, xr.DataArray]) -> str: - lon_key = get_longitude_key(ds) - return ds[lon_key].attrs["bounds"] - - -# Extract coordinate data - - -def get_time(ds: Union[xr.Dataset, xr.DataArray]) -> xr.DataArray: - time_key = get_time_key(ds) - time = ds[time_key] - return time - - -def get_longitude(ds: Union[xr.Dataset, xr.DataArray]) -> xr.DataArray: - lon_key = get_longitude_key(ds) - lon = ds[lon_key] - return lon - - -def get_latitude(ds: Union[xr.Dataset, xr.DataArray]) -> xr.DataArray: - lat_key = get_latitude_key(ds) - lat = ds[lat_key] - return lat - - -# Extract coordinate bounds data - - -def get_time_bounds(ds: Union[xr.Dataset, xr.DataArray]) -> xr.DataArray: - time_bounds_key = get_time_bounds_key(ds) - time_bounds = ds[time_bounds_key] - return time_bounds - - -def get_longitude_bounds(ds: Union[xr.Dataset, xr.DataArray]) -> xr.DataArray: - lon_bounds_key = get_longitude_bounds_key(ds) - lon_bounds = ds[lon_bounds_key] - return lon_bounds - - -def get_latitude_bounds(ds: Union[xr.Dataset, xr.DataArray]) -> xr.DataArray: - lat_bounds_key = get_latitude_bounds_key(ds) - lat_bounds = ds[lat_bounds_key] - return lat_bounds - - -# Select subset - - -def select_subset( - ds: xr.Dataset, lat: tuple = None, lon: tuple = None, time: tuple = None -) -> xr.Dataset: - """ - Selects a subset of the given xarray dataset based on specified latitude, longitude, and time ranges. - - Parameters: - - ds (xr.Dataset): The input xarray dataset. - - lat (tuple, optional): Latitude range in the form of (min, max). - - lon (tuple, optional): Longitude range in the form of (min, max). - - time (tuple, optional): Time range. If time is specified, it should be in the form of (start_time, end_time), - where start_time and end_time can be integers, floats, or cftime.DatetimeProlepticGregorian objects. - - Returns: - - xr.Dataset: Subset of the input dataset based on the specified latitude, longitude, and time ranges. - - Example Usage: - ``` - import cftime - - # Define latitude, longitude, and time ranges - lat_tuple = (30, 50) # Latitude range - lon_tuple = (110, 130) # Longitude range - time_tuple = (cftime.datetime(1850, 1, 1, 0, 0, 0, 0), - cftime.datetime(1851, 12, 31, 23, 59, 59, 0)) # Time range - - # Load your xarray dataset (ds) here - - # Select subset based on specified ranges - ds_subset = select_subset(ds, lat=lat_tuple, lon=lon_tuple, time=time_tuple) - ``` - """ - sel_keys = {} - if lat is not None: - lat_key = get_latitude_key(ds) - sel_keys[lat_key] = slice(*lat) - if lon is not None: - lon_key = get_longitude_key(ds) - sel_keys[lon_key] = slice(*lon) - if time is not None: - time_key = get_time_key(ds) - sel_keys[time_key] = slice(*time) - - ds = ds.sel(**sel_keys) - return ds diff --git a/pcmdi_metrics/io/xcdat_openxml.py b/pcmdi_metrics/io/xcdat_openxml.py deleted file mode 100644 index 8b42b964b..000000000 --- a/pcmdi_metrics/io/xcdat_openxml.py +++ /dev/null @@ -1,89 +0,0 @@ -import glob -import os -import sys -from typing import Union - -import xarray as xr -import xcdat as xc -import xmltodict - - -def xcdat_open( - infile: Union[str, list], data_var: str = None, decode_times: bool = True -) -> xr.Dataset: - """Open input file (netCDF, or xml generated by cdscan) - - Parameters - ---------- - infile : Union[str, list] - list of string, or string, for path of file(s) to open using xcdat - data_var : str, optional - key of the non-bounds data variable to keep in the Dataset, alongside any existing bounds data variables, by default None, which loads all data variables - decode_times : bool, optional - If True, attempt to decode times encoded in the standard NetCDF datetime format into cftime.datetime objects. Otherwise, leave them encoded as numbers. This keyword may not be supported by all the backends, by default True - - Returns - ------- - xr.Dataset - xarray dataset opened via xcdat - - Usage - ----- - >>> from pcmdi_metrics.io import xcdat_open - # Open a single netCDF file - >>> ds = xcdat_open('mydata.nc') - # Open multiple files - >>> ds2 = xcdat_open(['mydata1.nc', 'mydata2.nc'] # Open multipe netCDF files - # Open with specifing the variable 'ts' - >>> ds3 = xcdat_open(['mydata1.nc', 'mydata2.nc'], data_var='ts') - # Open an xml file - >>> ds = xcdat_open('mydata.xml') - """ - if isinstance(infile, list): - ds = xc.open_mfdataset(infile, data_var=data_var, decode_times=decode_times) - else: - if infile.split(".")[-1].lower() == "xml": - ds = xcdat_openxml(infile, data_var=data_var, decode_times=decode_times) - else: - ds = xc.open_dataset(infile, data_var=data_var, decode_times=decode_times) - - return ds - - -def xcdat_openxml( - xmlfile: str, data_var: str = None, decode_times: bool = True -) -> xr.Dataset: - """Open input file (xml generated by cdscan) - - Parameters - ---------- - infile: str - path of xml file to open using xcdat - data_var: str, optional - key of the non-bounds data variable to keep in the Dataset, alongside any existing bounds data variables, by default None, which loads all data variables - decode_times : bool, optional - If True, attempt to decode times encoded in the standard NetCDF datetime format into cftime.datetime objects. Otherwise, leave them encoded as numbers. This keyword may not be supported by all the backends, by default True - - Returns - ------- - xr.Dataset - xarray dataset opened via xcdat - """ - if not os.path.exists(xmlfile): - sys.exit(f"ERROR: File not exist: {xmlfile}") - - with open(xmlfile, encoding="utf-8") as fd: - doc = xmltodict.parse(fd.read()) - - ncfile_list = glob.glob(os.path.join(doc["dataset"]["@directory"], "*.nc")) - - if len(ncfile_list) > 1: - ds = xc.open_mfdataset( - ncfile_list, data_var=data_var, decode_times=decode_times - ) - else: - ds = xc.open_dataset( - ncfile_list[0], data_var=data_var, decode_times=decode_times - ) - - return ds diff --git a/pcmdi_metrics/monsoon_sperber/lf_AIR_cdms.nc b/pcmdi_metrics/monsoon_sperber/lf_AIR_cdms.nc deleted file mode 100644 index 7d8ec5014..000000000 Binary files a/pcmdi_metrics/monsoon_sperber/lf_AIR_cdms.nc and /dev/null differ diff --git a/pcmdi_metrics/monsoon_sperber/lf_AIR_xcdat.nc b/pcmdi_metrics/monsoon_sperber/lf_AIR_xcdat.nc deleted file mode 100644 index bab356dd4..000000000 Binary files a/pcmdi_metrics/monsoon_sperber/lf_AIR_xcdat.nc and /dev/null differ diff --git a/pcmdi_metrics/monsoon_sperber/lf_AUS_cdms.nc b/pcmdi_metrics/monsoon_sperber/lf_AUS_cdms.nc deleted file mode 100644 index a1b747c57..000000000 Binary files a/pcmdi_metrics/monsoon_sperber/lf_AUS_cdms.nc and /dev/null differ diff --git a/pcmdi_metrics/monsoon_sperber/lf_AUS_xcdat.nc b/pcmdi_metrics/monsoon_sperber/lf_AUS_xcdat.nc deleted file mode 100644 index 7c2412eca..000000000 Binary files a/pcmdi_metrics/monsoon_sperber/lf_AUS_xcdat.nc and /dev/null differ diff --git a/pcmdi_metrics/monsoon_sperber/lf_NHEX_xcdat.nc b/pcmdi_metrics/monsoon_sperber/lf_NHEX_xcdat.nc deleted file mode 100644 index 1a98b5218..000000000 Binary files a/pcmdi_metrics/monsoon_sperber/lf_NHEX_xcdat.nc and /dev/null differ diff --git a/pcmdi_metrics/monsoon_sperber/lf_SAmo_cdms.nc b/pcmdi_metrics/monsoon_sperber/lf_SAmo_cdms.nc deleted file mode 100644 index 75b0643e8..000000000 Binary files a/pcmdi_metrics/monsoon_sperber/lf_SAmo_cdms.nc and /dev/null differ diff --git a/pcmdi_metrics/monsoon_sperber/lf_SAmo_xcdat.nc b/pcmdi_metrics/monsoon_sperber/lf_SAmo_xcdat.nc deleted file mode 100644 index 922fb453e..000000000 Binary files a/pcmdi_metrics/monsoon_sperber/lf_SAmo_xcdat.nc and /dev/null differ diff --git a/pcmdi_metrics/monsoon_sperber/lf_Sahel_cdms.nc b/pcmdi_metrics/monsoon_sperber/lf_Sahel_cdms.nc deleted file mode 100644 index a202a8ebc..000000000 Binary files a/pcmdi_metrics/monsoon_sperber/lf_Sahel_cdms.nc and /dev/null differ diff --git a/pcmdi_metrics/monsoon_sperber/lf_Sahel_xcdat.nc b/pcmdi_metrics/monsoon_sperber/lf_Sahel_xcdat.nc deleted file mode 100644 index a3194bbda..000000000 Binary files a/pcmdi_metrics/monsoon_sperber/lf_Sahel_xcdat.nc and /dev/null differ diff --git a/pcmdi_metrics/monsoon_sperber/lib/__init__.py b/pcmdi_metrics/monsoon_sperber/lib/__init__.py deleted file mode 100644 index 688f34958..000000000 --- a/pcmdi_metrics/monsoon_sperber/lib/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .argparse_functions import AddParserArgument, YearCheck # noqa -from .calc_metrics import sperber_metrics # noqa -from .divide_chunks import divide_chunks, divide_chunks_advanced, interp1d # noqa -from .model_land_only import model_land_only # noqa diff --git a/pcmdi_metrics/monsoon_sperber/lib/argparse_functions.py b/pcmdi_metrics/monsoon_sperber/lib/argparse_functions.py deleted file mode 100644 index a51cc81a3..000000000 --- a/pcmdi_metrics/monsoon_sperber/lib/argparse_functions.py +++ /dev/null @@ -1,112 +0,0 @@ -def AddParserArgument(P): - # Load pre-defined parsers - P.use("--mip") - P.use("--exp") - P.use("--results_dir") - P.use("--reference_data_path") - P.use("--modpath") - # Add parsers for options - P.add_argument("--frequency", default="da") - P.add_argument("--realm", default="atm") - P.add_argument("--reference_data_name", type=str, help="Name of reference data set") - P.add_argument( - "--reference_data_lf_path", - type=str, - help="Path of landsea mask for reference data set", - ) - P.add_argument( - "--modpath_lf", type=str, help="Path of landsea mask for model data set" - ) - P.add_argument("--varOBS", type=str, help="Name of variable in reference data") - P.add_argument("--varModel", type=str, help="Name of variable in model(s)") - P.add_argument( - "--ObsUnitsAdjust", - type=tuple, - default=(False, 0, 0), - help="For unit adjust for OBS dataset. For example:\n" - "- (True, 'divide', 100.0) # Pa to hPa\n" - "- (True, 'subtract', 273.15) # degK to degC\n" - "- (False, 0, 0) # No adjustment (default)", - ) - P.add_argument( - "--ModUnitsAdjust", - type=tuple, - default=(False, 0, 0), - help="For unit adjust for model dataset. For example:\n" - "- (True, 'divide', 100.0) # Pa to hPa\n" - "- (True, 'subtract', 273.15) # degK to degC\n" - "- (False, 0, 0) # No adjustment (default)", - ) - - P.add_argument( - "--units", dest="units", type=str, help="Final units for the variable" - ) - P.add_argument( - "--osyear", dest="osyear", type=int, help="Start year for reference data set" - ) - P.add_argument( - "--msyear", dest="msyear", type=int, help="Start year for model data set" - ) - P.add_argument( - "--oeyear", dest="oeyear", type=int, help="End year for reference data set" - ) - P.add_argument( - "--meyear", dest="meyear", type=int, help="End year for model data set" - ) - P.add_argument("--modnames", type=str, default=None, help="List of models") - P.add_argument( - "--list_monsoon_regions", type=str, default=None, help="List of regions" - ) - P.add_argument( - "-r", - "--realization", - type=str, - default="r1i1p1", - help="Consider all accessible realizations as idividual\n" - "- r1i1p1: default, consider only 'r1i1p1' member\n" - " Or, specify realization, e.g, r3i1p1'\n" - "- *: consider all available realizations", - ) - # Add parsers as switches - P.add_argument( - "-d", - "--debug", - type=bool, - default=False, - help="Option for debug: False (defualt) or True", - ) - P.add_argument( - "--nc_out", - dest="nc_out", - help="record netcdf output", - action="store_true", - default=False, - ) - P.add_argument( - "--plot", dest="plot", help="produce plots", action="store_true", default=False - ) - P.add_argument( - "--includeOBS", - dest="includeOBS", - help="include observation", - action="store_true", - default=False, - ) - P.add_argument( - "--update_json", - type=bool, - default=True, - help="Option for update existing JSON file: True (i.e., update) (default) / False (i.e., overwrite)", - ) - return P - - -def YearCheck(syear, eyear, P): - if syear >= eyear: - P.error( - "Given starting year {} is later than or same as the given ending year {}".format( - syear, eyear - ) - ) - else: - pass diff --git a/pcmdi_metrics/monsoon_sperber/lib/calc_metrics.py b/pcmdi_metrics/monsoon_sperber/lib/calc_metrics.py deleted file mode 100644 index cdf1745a4..000000000 --- a/pcmdi_metrics/monsoon_sperber/lib/calc_metrics.py +++ /dev/null @@ -1,54 +0,0 @@ -""" Calculate metrics based on Sperber and Annamalai 2014 Clim. Dyn., -which are: -- onset pentad index: when fractional accumulation hit 20% -- decay pentad index: when fractional accumulation hit 80% -- slope: slope between onset and decay pentad time step indices -- duration: duration of monsoon period as index -calculated from cumulative pentad time series - -Drafted: Jiwoo Lee, 2018-07 -Revised: Jiwoo Lee, 2019-05 -Revised: Bo Dong, 2023-12 - -Note: Code for picking onset/decay index inspired by -https://stackoverflow.com/questions/2236906/first-python-list-index-greater-than-x -""" - - -def sperber_metrics(d, region, debug=False): - """d: input, 1d array of cumulative pentad time series""" - # Convert accumulation to fractional accumulation; normalize by sum - d_sum = d[-1] - # Normalize - - frac_accum = d / d_sum - - # Stat 1: Onset - onset_index = (i for i, v in enumerate(frac_accum) if v >= 0.2) - onset_index = next(onset_index) - i = onset_index - v = frac_accum[i] - print("i = , ", i, " v = ", v) - # Stat 2: Decay - if region == "GoG": - decay_threshold = 0.6 - else: - decay_threshold = 0.8 - - decay_index = (i for i, v in enumerate(frac_accum) if v >= decay_threshold) - decay_index = next(decay_index) - - # Stat 3: Slope - slope = (frac_accum[decay_index] - frac_accum[onset_index]) / float( - decay_index - onset_index - ) - # Stat 4: Duration - duration = decay_index - onset_index + 1 - # Calc done, return result as dic - return { - "frac_accum": frac_accum, - "onset_index": onset_index, - "decay_index": decay_index, - "slope": slope, - "duration": duration, - } diff --git a/pcmdi_metrics/monsoon_sperber/lib/divide_chunks.py b/pcmdi_metrics/monsoon_sperber/lib/divide_chunks.py deleted file mode 100644 index 8a215f4f8..000000000 --- a/pcmdi_metrics/monsoon_sperber/lib/divide_chunks.py +++ /dev/null @@ -1,97 +0,0 @@ -import sys - -import numpy as np -import scipy.interpolate as interp - -""" For pentad, -Code taken from https://www.geeksforgeeks.org/break-list-chunks-size-n-python/ -""" -# Yield successive n-sized -# chunks from data. - - -def divide_chunks(data, n): - # looping till length data - for i in range(0, data.time.shape[0], n): - yield data[i : i + n] - - -""" Above code advanced considering leap year -""" - - -def divide_chunks_advanced(data, n, debug=False): - # Double check first date should be Jan 1 (except for SH monsoon) - - tim = data.time.dt - month = tim.month[0] - day = tim.day[0] - month = month.values - day = day.values - calendar = "gregorian" - if debug: - # print("month = ", month, "day = ", day) - print("debug: first day of year is " + str(month) + "/" + str(day)) - if month not in [1, 7] or day != 1: - sys.exit( - "error: first day of year time series is " + str(month) + "/" + str(day) - ) - - # Check number of days in given year - nday = data.time.shape[0] - - if nday in [365, 360]: - # looping till length data - for i in range(0, nday, n): - yield data[i : i + n] - - elif nday == 366: - # until leap year day detected - for i in range(0, nday, n): - # Check if leap year date included - leap_detect = False - for ii in range(i, i + n): - date = data.time.dt - month = date.month[ii] - day = date.day[ii] - if month == 2 and day > 28: - if debug: - print("debug: leap year detected:", month, "/", day) - leap_detect = True - - if leap_detect: - yield data[i : i + n + 1] - tmp = i + n + 1 - break - else: - yield data[i : i + n] - - # after leap year day passed - if leap_detect: - for i in range(tmp, nday, n): - yield data[i : i + n] - elif nday == 361 and calendar == "360_day": - # Speacial case handling for HadGEM2 family where time bounds was not - # properly saved, so include next year's first day in time series - if debug: - print("debug: 361 to 360 revise: l.shape, before:", data.shape) - data = data[0:360] - if debug: - print("debug: 361 to 360 revise: l.shape, after:", data.shape) - # looping till length data - for i in range(0, nday, n): - yield data[i : i + n] - - else: - sys.exit("error: number of days in year is " + str(nday)) - - -def interp1d(data, ref_length, debug=False): - data = np.array(data) - data_interp = interp.interp1d(np.arange(data.size), data) - data2 = data_interp(np.linspace(0, data.size - 1, ref_length)) - if debug: - print("debug: 1d interpolation") - print("debug: length before interp: ", len(data)) - print("debug: length after interp: ", len(data2)) - return data2 diff --git a/pcmdi_metrics/monsoon_sperber/lib/model_land_only.py b/pcmdi_metrics/monsoon_sperber/lib/model_land_only.py deleted file mode 100644 index b1319fb65..000000000 --- a/pcmdi_metrics/monsoon_sperber/lib/model_land_only.py +++ /dev/null @@ -1,49 +0,0 @@ -import cartopy.crs as ccrs -import matplotlib.pyplot as plt -import numpy as np - - -def model_land_only(model, model_timeseries, lf, debug=False): - # ------------------------------------------------- - # Mask out over ocean grid - # - - - - - - - - - - - - - - - - - - - - - - - - - - - if debug: - # plot_map(model_timeseries[0], "_".join(["test", model, "beforeMask.png"])) - print("debug: plot for beforeMask done") - - # Check land fraction variable to see if it meet criteria - # (0 for ocean, 100 for land, no missing value) - - if np.max(lf) == 1.0: - lf = lf * 100.0 - - opt1 = False - - if opt1: # Masking out partial ocean grids as well - # Mask out ocean even fractional (leave only pure ocean grid) - model_timeseries_masked = model_timeseries.where(lf > 0 & lf < 100) - - else: # Mask out only full ocean grid & use weighting for partial ocean grid - model_timeseries_masked = model_timeseries.where(lf > 0) - - if model == "EC-EARTH": - # Mask out over 90% land grids for models those consider river as - # part of land-sea fraction. So far only 'EC-EARTH' does.. - model_timeseries_masked = model_timeseries.where(lf > 90) - - if debug: - # plot_map(model_timeseries_masked[0], "_".join(["test", model, "afterMask.png"])) - print("debug: plot for afterMask done") - - return model_timeseries_masked - - -def plot_map(data, filename): - lons = data["lon"] - lats = data["lat"] - ax = plt.axes(projection=ccrs.PlateCarree(central_longitude=180)) - ax.contourf(lons, lats, data, transform=ccrs.PlateCarree(), cmap="viridis") - ax.coastlines() - ax.set_global() - plt.savefig(filename) diff --git a/pcmdi_metrics/monsoon_sperber/param/Bo_param.py b/pcmdi_metrics/monsoon_sperber/param/Bo_param.py deleted file mode 100644 index ca0d9bd6e..000000000 --- a/pcmdi_metrics/monsoon_sperber/param/Bo_param.py +++ /dev/null @@ -1,76 +0,0 @@ -import datetime -import os - -# ================================================= -# Background Information -# ------------------------------------------------- -mip = "cmip5" -exp = "historical" -frequency = "da" -realm = "atm" - -# ================================================= -# Miscellaneous -# ------------------------------------------------- -update_json = False -debug = False -# debug = True - -# list_monsoon_regions = ["AIR", "AUS", "Sahel", "GoG", "NAmo", "SAmo"] -list_monsoon_regions = ["AUS"] -# ================================================= -# Observation -# ------------------------------------------------- -reference_data_name = "GPCP-1-3" -reference_data_path = "/p/user_pub/PCMDIobs/obs4MIPs/NASA-GSFC/GPCP-1DD-CDR-v1-3/day/pr/1x1/latest/pr_day_GPCP-1DD-CDR-v1-3_PCMDIFROGS_1x1_19961001-20201231.nc" -reference_data_lf_path = ( - "/work/lee1043/DATA/LandSeaMask_1x1_NCL/NCL_LandSeaMask_rewritten.nc" # noqa -) - -varOBS = "pr" -ObsUnitsAdjust = (True, "multiply", 86400.0) # kg m-2 s-1 to mm day-1 - -osyear = 1998 -oeyear = 1999 - -includeOBS = True - -# ================================================= -# Models -# ------------------------------------------------- -modpath = "/work/lee1043/ESGF/xmls/cmip5/historical/day/pr/cmip5.%(model).%(exp).%(realization).day.pr.xml" -modpath_lf = "/work/lee1043/ESGF/xmls/cmip5/historical/fx/sftlf/cmip5.%(model).historical.r0i0p0.fx.sftlf.xml" - -# /p/css03/scratch/published-older/cmip5/output1/CSIRO-BOM/ACCESS1-0/historical/day/atmos/day/r1i1p1/v4/pr/pr_day_ACCESS1-0_historical_r1i1p1_19750101-19991231.nc - -# modnames = ['ACCESS1-0', 'ACCESS1-3', 'BCC-CSM1-1', 'BCC-CSM1-1-M', 'BNU-ESM', 'CanCM4', 'CanESM2', 'CCSM4', 'CESM1-BGC', 'CESM1-CAM5', 'CESM1-FASTCHEM', 'CMCC-CESM', 'CMCC-CM', 'CMCC-CMS', 'CNRM-CM5', 'CSIRO-Mk3-6-0', 'EC-EARTH', 'FGOALS-g2', 'GFDL-CM3', 'GFDL-ESM2G', 'GFDL-ESM2M', 'GISS-E2-H', 'GISS-E2-R', 'HadGEM2-AO', 'HadGEM2-CC', 'HadGEM2-ES', 'INMCM4', 'IPSL-CM5A-LR', 'IPSL-CM5A-MR', 'IPSL-CM5B-LR', 'MIROC-ESM', 'MIROC-ESM-CHEM', 'MIROC4h', 'MIROC5', 'MPI-ESM-MR', 'MPI-ESM-P', 'MRI-CGCM3', 'MRI-ESM1', 'NorESM1-M'] # noqa - -modnames = ["ACCESS1-0"] - -realization = "r1i1p1" -# realization = '*' - -varModel = "pr" -ModUnitsAdjust = (True, "multiply", 86400.0) # kg m-2 s-1 to mm day-1 -units = "mm/d" - -msyear = 1998 -meyear = 1999 - -# ================================================= -# Output -# ------------------------------------------------- -# pmprdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2" -pmprdir = "/p/user_pub/climate_work/dong12/PMP_result/" -case_id = "{:v%Y%m%d}".format(datetime.datetime.now()) - -if debug: - pmprdir = "/p/user_pub/climate_work/dong12/PMP_result/" - case_id = "{:v%Y%m%d-%H%M}".format(datetime.datetime.now()) - -results_dir = os.path.join( - pmprdir, "%(output_type)", "monsoon", "monsoon_sperber", mip, exp, case_id -) - -nc_out = True # Write output in NetCDF -plot = True # Create map graphics diff --git a/pcmdi_metrics/monsoon_sperber/param/myParam.py b/pcmdi_metrics/monsoon_sperber/param/myParam.py deleted file mode 100644 index 23e8a578f..000000000 --- a/pcmdi_metrics/monsoon_sperber/param/myParam.py +++ /dev/null @@ -1,70 +0,0 @@ -import datetime -import os - -# ================================================= -# Background Information -# ------------------------------------------------- -mip = "cmip5" -exp = "historical" -frequency = "da" -realm = "atm" - -# ================================================= -# Miscellaneous -# ------------------------------------------------- -update_json = False -debug = False - -# ================================================= -# Observation -# ------------------------------------------------- -reference_data_name = "GPCP-1-3" -reference_data_path = "/p/user_pub/PCMDIobs/PCMDIobs2/atmos/day/pr/GPCP-1-3/gn/v20200924/pr_day_GPCP-1-3_BE_gn_v20200924_19961002-20170101.nc" # noqa -reference_data_lf_path = ( - "/work/lee1043/DATA/LandSeaMask_1x1_NCL/NCL_LandSeaMask_rewritten.nc" # noqa -) - -varOBS = "pr" -ObsUnitsAdjust = (True, "multiply", 86400.0) # kg m-2 s-1 to mm day-1 - -osyear = 1998 -oeyear = 1999 - -includeOBS = True - -# ================================================= -# Models -# ------------------------------------------------- -modpath = "/work/lee1043/ESGF/xmls/cmip5/historical/day/pr/cmip5.%(model).%(exp).%(realization).day.pr.xml" -modpath_lf = "/work/lee1043/ESGF/xmls/cmip5/historical/fx/sftlf/cmip5.%(model).historical.r0i0p0.fx.sftlf.xml" - -# modnames = ['ACCESS1-0', 'ACCESS1-3', 'BCC-CSM1-1', 'BCC-CSM1-1-M', 'BNU-ESM', 'CanCM4', 'CanESM2', 'CCSM4', 'CESM1-BGC', 'CESM1-CAM5', 'CESM1-FASTCHEM', 'CMCC-CESM', 'CMCC-CM', 'CMCC-CMS', 'CNRM-CM5', 'CSIRO-Mk3-6-0', 'EC-EARTH', 'FGOALS-g2', 'GFDL-CM3', 'GFDL-ESM2G', 'GFDL-ESM2M', 'GISS-E2-H', 'GISS-E2-R', 'HadGEM2-AO', 'HadGEM2-CC', 'HadGEM2-ES', 'INMCM4', 'IPSL-CM5A-LR', 'IPSL-CM5A-MR', 'IPSL-CM5B-LR', 'MIROC-ESM', 'MIROC-ESM-CHEM', 'MIROC4h', 'MIROC5', 'MPI-ESM-MR', 'MPI-ESM-P', 'MRI-CGCM3', 'MRI-ESM1', 'NorESM1-M'] # noqa - -modnames = ["ACCESS1-0"] - -realization = "r1i1p1" -# realization = '*' - -varModel = "pr" -ModUnitsAdjust = (True, "multiply", 86400.0) # kg m-2 s-1 to mm day-1 -units = "mm/d" - -msyear = 1998 -meyear = 1999 - -# ================================================= -# Output -# ------------------------------------------------- -pmprdir = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2" -case_id = "{:v%Y%m%d}".format(datetime.datetime.now()) - -if debug: - pmprdir = "/work/lee1043/imsi/result_test" - case_id = "{:v%Y%m%d-%H%M}".format(datetime.datetime.now()) - -results_dir = os.path.join( - pmprdir, "%(output_type)", "monsoon", "monsoon_sperber", mip, exp, case_id -) - -nc_out = True # Write output in NetCDF -plot = True # Create map graphics diff --git a/pcmdi_metrics/monsoon_sperber/test_ACCESS1-0_afterMask.png b/pcmdi_metrics/monsoon_sperber/test_ACCESS1-0_afterMask.png deleted file mode 100644 index 707011119..000000000 Binary files a/pcmdi_metrics/monsoon_sperber/test_ACCESS1-0_afterMask.png and /dev/null differ diff --git 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b/share/default_regions.py deleted file mode 100755 index 72eb7804a..000000000 --- a/share/default_regions.py +++ /dev/null @@ -1,99 +0,0 @@ -import cdutil - -regions_specs = { - "NHEX": {"domain": cdutil.region.domain(latitude=(30.0, 90))}, - "SHEX": {"domain": cdutil.region.domain(latitude=(-90.0, -30))}, - "TROPICS": {"domain": cdutil.region.domain(latitude=(-30.0, 30))}, - "global": {}, - "90S50S": {"domain": cdutil.region.domain(latitude=(-90.0, -50))}, - "50S20S": {"domain": cdutil.region.domain(latitude=(-50.0, -20))}, - "20S20N": {"domain": cdutil.region.domain(latitude=(-20.0, 20))}, - "20N50N": {"domain": cdutil.region.domain(latitude=(20.0, 50))}, - "50N90N": {"domain": cdutil.region.domain(latitude=(50.0, 90))}, - "land_NHEX": {"value": 100, "domain": cdutil.region.domain(latitude=(30.0, 90))}, - "land_SHEX": {"value": 100, "domain": cdutil.region.domain(latitude=(-90.0, -30))}, - "land_TROPICS": { - "value": 100, - "domain": cdutil.region.domain(latitude=(-30.0, 30)), - }, - "land": { - "value": 100, - }, - "ocean_NHEX": {"value": 0, "domain": cdutil.region.domain(latitude=(30.0, 90))}, - "ocean_SHEX": {"value": 0, "domain": cdutil.region.domain(latitude=(-90.0, -30))}, - "ocean_TROPICS": {"value": 0, "domain": cdutil.region.domain(latitude=(30.0, 30))}, - "ocean": { - "value": 0, - }, - # Modes of variability - "NAM": {"domain": cdutil.region.domain(latitude=(20.0, 90), longitude=(-180, 180))}, - "NAO": {"domain": cdutil.region.domain(latitude=(20.0, 80), longitude=(-90, 40))}, - "SAM": {"domain": cdutil.region.domain(latitude=(-20.0, -90), longitude=(0, 360))}, - "PNA": {"domain": cdutil.region.domain(latitude=(20.0, 85), longitude=(120, 240))}, - "PDO": {"domain": cdutil.region.domain(latitude=(20.0, 70), longitude=(110, 260))}, - "AMO": {"domain": cdutil.region.domain(latitude=(0.0, 70), longitude=(-80, 0))}, - # Monsoon domains for Wang metrics - # All monsoon domains - "AllMW": { - "domain": cdutil.region.domain(latitude=(-40.0, 45.0), longitude=(0.0, 360.0)) - }, - "AllM": { - "domain": cdutil.region.domain(latitude=(-45.0, 45.0), longitude=(0.0, 360.0)) - }, - # North American Monsoon - "NAMM": { - "domain": cdutil.region.domain(latitude=(0.0, 45.0), longitude=(210.0, 310.0)) - }, - # South American Monsoon - "SAMM": { - "domain": cdutil.region.domain(latitude=(-45.0, 0.0), longitude=(240.0, 330.0)) - }, - # North African Monsoon - "NAFM": { - # "domain": cdutil.region.domain(latitude=(0.0, 45.0), longitude=(310.0, 60.0)) - "domain": cdutil.region.domain(latitude=(0.0, 45.0), longitude=(-50, 60.0)) - }, - # South African Monsoon - "SAFM": { - "domain": cdutil.region.domain(latitude=(-45.0, 0.0), longitude=(0.0, 90.0)) - }, - # Asian Summer Monsoon - "ASM": { - "domain": cdutil.region.domain(latitude=(0.0, 45.0), longitude=(60.0, 180.0)) - }, - # Australian Monsoon - "AUSM": { - "domain": cdutil.region.domain(latitude=(-45.0, 0.0), longitude=(90.0, 160.0)) - }, - # Monsoon domains for Sperber metrics - # All India rainfall - "AIR": { - "domain": cdutil.region.domain(latitude=(7.0, 25.0), longitude=(65.0, 85.0)) - }, - # North Australian - "AUS": { - "domain": cdutil.region.domain( - latitude=(-20.0, -10.0), longitude=(120.0, 150.0) - ) - }, - # Sahel - "Sahel": { - "domain": cdutil.region.domain(latitude=(13.0, 18.0), longitude=(-10.0, 10.0)) - }, - # Gulf of Guinea - "GoG": { - "domain": cdutil.region.domain(latitude=(0.0, 5.0), longitude=(-10.0, 10.0)) - }, - # North American monsoon - "NAmo": { - "domain": cdutil.region.domain( - latitude=(20.0, 37.0), longitude=(-112.0, -103.0) - ) - }, - # South American monsoon - "SAmo": { - "domain": cdutil.region.domain(latitude=(-20.0, 2.5), longitude=(-65.0, -40.0)) - }, -} - -default_regions = ["global", "NHEX", "SHEX", "TROPICS"] diff --git a/test_landmask_generation.py b/test_landmask_generation.py deleted file mode 100644 index b4b84e37e..000000000 --- a/test_landmask_generation.py +++ /dev/null @@ -1,63 +0,0 @@ -import os - -import xarray as xr -import xcdat as xc - -from pcmdi_metrics.utils import create_land_sea_mask - -demo_dir = "/Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_data/" -demo_file = "CMIP5_demo_data/ts_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc" - -dummy_input = os.path.join(demo_dir, demo_file) -ds = xc.open_dataset(dummy_input) -mask = create_land_sea_mask(ds) -mask2 = create_land_sea_mask(ds, method="pcmdi") - -mask.plot() -mask2.plot() -(mask2 - mask).plot() - -import os - -from pcmdi_metrics import resources - -egg_pth = resources.resource_path() -source_path = os.path.join(egg_pth, "navy_land.nc") - -ds_navy = xc.open_dataset(source_path) - -ds_navy["sftlf"].plot() - -import cdms2 -import cdutil - -f = cdms2.open(dummy_input) - -dc = f("ts") - -mask_cdms = cdutil.generateLandSeaMask(dc) - -lat = dc.getLatitude() -lon = dc.getLongitude() - - -mask_cdms_da = xr.DataArray( - mask_cdms, coords=[list(lat), list(lon)], dims=["lat", "lon"], name=dc.id -) - - -mask_cdms_da.plot() - - -(mask2 - mask_cdms_da).plot() - - -(mask2 - mask_cdms_da).sum() - -# -# %%time -# a = mask.to_numpy() -# -# -# %%time -# b = mask.data