diff --git a/CHANGELOG.md b/CHANGELOG.md index 15108e524..e195ec6a0 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,22 @@ Release date: 2024-07-19 ### Dependency Changes +### Added + +### Changed + +### Fixed + +### Deprecated + +### Removed + +## 5.0.0 + +Release date: 2024-07-19 + +### Dependency Changes + Added: - `bayesian-optimization` diff --git a/climada/_version.py b/climada/_version.py index a0f66580c..80952dacb 100644 --- a/climada/_version.py +++ b/climada/_version.py @@ -1 +1 @@ -__version__ = '5.0.0' +__version__ = '5.0.1-dev' diff --git a/climada/hazard/tc_clim_change.py b/climada/hazard/tc_clim_change.py index 21b40a0b0..85e61facd 100644 --- a/climada/hazard/tc_clim_change.py +++ b/climada/hazard/tc_clim_change.py @@ -7,168 +7,383 @@ terms of the GNU General Public License as published by the Free Software Foundation, version 3. -CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY -WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A -PARTICULAR PURPOSE. See the GNU General Public License for more details. +Most of this module are modifications of work originally published under the following license: -You should have received a copy of the GNU General Public License along -with CLIMADA. If not, see . +MIT License + +Copyright (c) 2021 Lynne Jewson, Stephen Jewson + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. --- -Define climate change scenarios for tropical cycones. +Define scaling factors to model the impact of climate change on tropical cyclones. """ -import numpy as np +from math import log +import logging import pandas as pd +import numpy as np + +LOGGER = logging.getLogger(__name__) + +MAP_BASINS_NAMES = {'NA': 0, 'WP': 1, 'EP': 2, 'NI': 3, 'SI': 4, 'SP': 5} + +MAP_VARS_NAMES = {'cat05': 0, 'cat45': 1, 'intensity': 2} + +MAP_PERC_NAMES = {'5/10': 0, '25': 1, '50': 2, '75': 3, '90/95': 4} + +# it defines the first and last projection years as well as the largest smoothing window +YEAR_WINDOWS_PROPS = {'start': 2000, 'end': 2100, 'smoothing': 5} + +def get_knutson_scaling_factor( + variable: str='cat05', + percentile: str='50', + basin: str='NA', + baseline: tuple=(1982, 2022), + yearly_steps: int=5 + ): + """ + This code combines data in Knutson et al. (2020) and global mean surface + temperature (GMST) data (historical and CMIP5 simulated) to produce TC + projections for 4 RCPs and any historical baseline. The code uses GMST data + implements to interpolate and extrapolate the Knutson data + relative to the specified baseline time period for various RCPs + with a log-linear model. The methodology was developed and explained + in Jewson et al., (2021). + + Related publications: + - Knutson et al., (2020): Tropical cyclones and climate + change assessment. Part II: Projected response to anthropogenic warming. + Bull. Amer. Meteor. Soc., 101 (3), E303–E322, + https://doi.org/10.1175/BAMS-D-18-0194.1. + + - Jewson (2021): Conversion of the Knutson et al. (2020) Tropical Cyclone + Climate Change Projections to Risk Model Baselines, + https://doi.org/10.1175/JAMC-D-21-0102.1 + + Parameters + ---------- + variable: int + variable of interest, possible choices are 'cat05' (frequencies of all + tropical cyclones), 'cat45' (frequencies of category 4 and 5 tropical cyclones) + and 'intensity' (mean intensity of all tropical cyclones) + percentile: str + percentiles of Knutson et al. 2020 estimates, representing the model uncertainty + in future changes in TC activity. These estimates come from a review of state-of-the-art + literature and models. For the 'cat05' variable (i.e. frequency of all tropical cyclones) + the 5th, 25th, 50th, 75th and 95th percentiles are provided. For 'cat45' and 'intensity', + the provided percentiles are the 10th, 25th, 50th, 75th and 90th. Please refer to the + mentioned publications for more details. + possible percentiles: + '5/10' either the 5th or 10th percentile depending on variable (see text above) + '25' for the 25th percentile + '50' for the 50th percentile + '75' for the 75th percentile + '90/95' either the 90th or 95th percentile depending on variable (see text above) + Default: '50' + basin : str + region of interest, possible choices are: + 'NA', 'WP', 'EP', 'NI', 'SI', 'SP' + baseline : tuple of int + the starting and ending years that define the historical + baseline. The historical baseline period must fall within + the GSMT data period, i.e., 1880-2100. Default is 1982-2022. + yearly_steps : int + yearly resolution at which projections are provided. Default is 5 years. + Returns + ------- + future_change_variable : pd.DataFrame + data frame with future projections of the selected variables at different + times (indexes) and for RCPs 2.6, 4.5, 6.0 and 8.5 (columns). + """ + + base_start_year, base_end_year = baseline + gmst_info = get_gmst_info() + + knutson_data = get_knutson_data() + + num_of_rcps, gmst_years = gmst_info['gmst_data'].shape -from climada.util.constants import SYSTEM_DIR + if ((base_start_year <= gmst_info['gmst_start_year']) or + (base_start_year >= gmst_info['gmst_end_year']) or + (base_end_year <= gmst_info['gmst_start_year']) or + (base_end_year >= gmst_info['gmst_end_year'])): -TOT_RADIATIVE_FORCE = SYSTEM_DIR.joinpath('rcp_db.xls') -"""© RCP Database (Version 2.0.5) http://www.iiasa.ac.at/web-apps/tnt/RcpDb. -generated: 2018-07-04 10:47:59.""" + raise ValueError("The selected historical baseline falls outside" + f"the GMST data period {gmst_info['gmst_start_year']}" + f"-{gmst_info['gmst_end_year']}") -def get_knutson_criterion(): + var_id = MAP_VARS_NAMES[variable] + perc_id = MAP_PERC_NAMES[percentile] + + # Steps: + # 1. transform annual GMST values using e^βT + # 2. calculate the average of these transformed values over the two time periods + # 3. calculate the fractional change in the averages + # please refer to section 4. Methods of Jewson (2021) for more details. + + mid_years = np.arange(YEAR_WINDOWS_PROPS['start'], + YEAR_WINDOWS_PROPS['end']+1, + yearly_steps) + predicted_change = np.ones((mid_years.shape[0], num_of_rcps)) + + try: + basin_id = MAP_BASINS_NAMES[basin] + knutson_value = knutson_data[var_id, basin_id, perc_id] + + except KeyError: + LOGGER.warning(f"No scaling factors are defined for basin {basin} therefore" + "no change will be projected for tracks in this basin") + return pd.DataFrame(predicted_change, + index=mid_years, + columns=gmst_info['rcps']) + + base_start_pos = base_start_year - gmst_info['gmst_start_year'] + base_end_pos = base_end_year - gmst_info['gmst_start_year'] + + # Step 1. + beta = 0.5 * log(0.01 * knutson_value + 1) # equation 6 in Jewson (2021) + tc_properties = np.exp(beta * gmst_info['gmst_data']) # equation 3 in Jewson (2021) + + # Step 2. + baseline = np.mean(tc_properties[:, base_start_pos:base_end_pos + 1], 1) + + # Step 3. + for i, mid_year in enumerate(mid_years): + mid_year_in_gmst_ind = mid_year - gmst_info['gmst_start_year'] + actual_smoothing = min( + YEAR_WINDOWS_PROPS['smoothing'], + gmst_years - mid_year_in_gmst_ind - 1, + mid_year_in_gmst_ind + ) + fut_start_pos = mid_year_in_gmst_ind - actual_smoothing + fut_end_pos = mid_year_in_gmst_ind + actual_smoothing + 1 + + prediction = np.mean(tc_properties[:, fut_start_pos:fut_end_pos], 1) + + # assess fractional changes + predicted_change[i] = ((prediction - baseline) / + baseline) * 100 + + return pd.DataFrame(predicted_change, + index=mid_years, + columns=gmst_info['rcps']) + +def get_gmst_info(): """ - Fill changes in TCs according to Knutson et al. 2015 Global projections - of intense tropical cyclone activity for the late twenty-first century from - dynamical downscaling of CMIP5/RCP4.5 scenarios. + Get Global Mean Surface Temperature (GMST) data from 1880 to 2100 for + RCPs 2.6, 4.5, 6.0 and 8.5. Data are provided in: + + Jewson (2021): Conversion of the Knutson et al. (2020) Tropical Cyclone + Climate Change Projections to Risk Model Baselines, + https://doi.org/10.1175/JAMC-D-21-0102.1 + + and in supporting documentation and code. Returns ------- - criterion : list(dict) - list of the criterion dictionary for frequency and intensity change - per basin, per category taken from the Table 3 in Knutson et al. 2015. - with items 'basin' (str), 'category' (list(int)), 'year' (int), - 'change' (float), 'variable' ('intensity' or 'frequency') + gmst_info : dict + dictionary with four keys, which are: + - rcps: list of strings referring to RCPs 2.6, 4.5, 6.0 and 8.5 + - gmst_start_year: integer with the GMST data starting year, 1880 + - gmst_end_year: integer with the GMST data ending year, 2100 + - gmst_data: array with GMST data across RCPs (first dim) and years (second dim) """ - # NA - na = [ - {'basin': 'NA', 'category': [0, 1, 2, 3, 4, 5], - 'year': 2100, 'change': 1, 'variable': 'frequency'}, - {'basin': 'NA', 'category': [1, 2, 3, 4, 5], - 'year': 2100, 'change': 1, 'variable': 'frequency'}, - {'basin': 'NA', 'category': [3, 4, 5], - 'year': 2100, 'change': 1, 'variable': 'frequency'}, - {'basin': 'NA', 'category': [4, 5], - 'year': 2100, 'change': 1, 'variable': 'frequency'}, - {'basin': 'NA', 'category': [1, 2, 3, 4, 5], - 'year': 2100, 'change': 1.045, 'variable': 'intensity'} - ] - - # EP - ep = [ - {'basin': 'EP', 'category': [0, 1, 2, 3, 4, 5], - 'year': 2100, 'change': 1.163, 'variable': 'frequency'}, - {'basin': 'EP', 'category': [1, 2, 3, 4, 5], - 'year': 2100, 'change': 1.193, 'variable': 'frequency'}, - {'basin': 'EP', 'category': [3, 4, 5], - 'year': 2100, 'change': 1.837, 'variable': 'frequency'}, - {'basin': 'EP', 'category': [4, 5], - 'year': 2100, 'change': 3.375, 'variable': 'frequency'}, - {'basin': 'EP', 'category': [0], - 'year': 2100, 'change': 1.082, 'variable': 'intensity'}, - {'basin': 'EP', 'category': [1, 2, 3, 4, 5], - 'year': 2100, 'change': 1.078, 'variable': 'intensity'} - ] - - # WP - wp = [ - {'basin': 'WP', 'category': [0, 1, 2, 3, 4, 5], - 'year': 2100, 'change': 1 - 0.345, 'variable': 'frequency'}, - {'basin': 'WP', 'category': [1, 2, 3, 4, 5], - 'year': 2100, 'change': 1 - 0.316, 'variable': 'frequency'}, - {'basin': 'WP', 'category': [3, 4, 5], - 'year': 2100, 'change': 1 - 0.169, 'variable': 'frequency'}, - {'basin': 'WP', 'category': [4, 5], - 'year': 2100, 'change': 1, 'variable': 'frequency'}, - {'basin': 'WP', 'category': [0], - 'year': 2100, 'change': 1.074, 'variable': 'intensity'}, - {'basin': 'WP', 'category': [1, 2, 3, 4, 5], - 'year': 2100, 'change': 1.055, 'variable': 'intensity'}, - ] - - # SP - sp = [ - {'basin': 'SP', 'category': [0, 1, 2, 3, 4, 5], - 'year': 2100, 'change': 1 - 0.366, 'variable': 'frequency'}, - {'basin': 'SP', 'category': [1, 2, 3, 4, 5], - 'year': 2100, 'change': 1 - 0.406, 'variable': 'frequency'}, - {'basin': 'SP', 'category': [3, 4, 5], - 'year': 2100, 'change': 1 - 0.506, 'variable': 'frequency'}, - {'basin': 'SP', 'category': [4, 5], - 'year': 2100, 'change': 1 - 0.583, 'variable': 'frequency'} - ] - - # NI - ni = [ - {'basin': 'NI', 'category': [0, 1, 2, 3, 4, 5], - 'year': 2100, 'change': 1, 'variable': 'frequency'}, - {'basin': 'NI', 'category': [1, 2, 3, 4, 5], - 'year': 2100, 'change': 1.256, 'variable': 'frequency'}, - {'basin': 'NI', 'category': [3, 4, 5], - 'year': 2100, 'change': 1, 'variable': 'frequency'}, - {'basin': 'NI', 'category': [4, 5], - 'year': 2100, 'change': 1, 'variable': 'frequency'} - ] - - # SI - si = [ - {'basin': 'SI', 'category': [0, 1, 2, 3, 4, 5], - 'year': 2100, 'change': 1 - 0.261, 'variable': 'frequency'}, - {'basin': 'SI', 'category': [1, 2, 3, 4, 5], - 'year': 2100, 'change': 1 - 0.284, 'variable': 'frequency'}, - {'basin': 'SI', 'category': [3, 4, 5], - 'year': 2100, 'change': 1, 'variable': 'frequency'}, - {'basin': 'SI', 'category': [4, 5], - 'year': 2100, 'change': 1, 'variable': 'frequency'}, - {'basin': 'SI', 'category': [1, 2, 3, 4, 5], - 'year': 2100, 'change': 1.033, 'variable': 'intensity'} - ] - - return na + ep + wp + sp + ni + si - - -def calc_scale_knutson(ref_year=2050, rcp_scenario=45): + + gmst_data = np.array([ + [-0.16,-0.08,-0.1,-0.16,-0.28,-0.32,-0.3,-0.35,-0.16,-0.1, + -0.35,-0.22,-0.27,-0.31,-0.3,-0.22,-0.11,-0.11,-0.26,-0.17, + -0.08,-0.15,-0.28,-0.37, -0.47,-0.26,-0.22,-0.39,-0.43,-0.48, + -0.43,-0.44,-0.36,-0.34,-0.15,-0.14,-0.36,-0.46,-0.29,-0.27, + -0.27,-0.19,-0.28,-0.26,-0.27,-0.22,-0.1,-0.22,-0.2,-0.36, + -0.16,-0.1,-0.16,-0.29,-0.13,-0.2,-0.15,-0.03,-0.01,-0.02, + 0.13,0.19,0.07,0.09,0.2,0.09,-0.07,-0.03,-0.11,-0.11,-0.17, + -0.07,0.01,0.08,-0.13,-0.14,-0.19,0.05,0.06,0.03,-0.02,0.06, + 0.04,0.05,-0.2,-0.11,-0.06,-0.02,-0.08,0.05,0.02,-0.08,0.01, + 0.16,-0.07,-0.01,-0.1,0.18,0.07,0.16,0.26,0.32,0.14,0.31,0.15, + 0.11,0.18,0.32,0.38,0.27,0.45,0.4,0.22,0.23,0.32,0.45,0.33,0.47, + 0.61,0.39,0.39,0.54,0.63,0.62,0.54, 0.68,0.64,0.66,0.54,0.66, + 0.72,0.61,0.64,0.68,0.75,0.9,1.02,0.92,0.85,0.98,0.909014286, + 0.938814286,0.999714286,1.034314286,1.009714286,1.020014286, + 1.040914286,1.068614286,1.072114286,1.095114286,1.100414286, + 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1.385592857,1.380892857,1.415092857,1.439892857,1.457092857, + 1.493592857,1.520292857,1.517692857,1.538092857,1.577192857, + 1.575492857,1.620392857,1.657092857,1.673492857,1.669992857, + 1.706292857,1.707892857,1.758592857,1.739492857,1.740192857, + 1.797792857,1.839292857,1.865392857,1.857692857,1.864092857, + 1.881192857,1.907592857,1.918492857,1.933992857,1.929392857, + 1.931192857,1.942492857,1.985592857,1.997392857,2.000992857, + 2.028692857,2.016192857,2.020792857,2.032892857,2.057492857, + 2.092092857,2.106292857,2.117492857,2.123492857,2.121092857, + 2.096892857,2.126892857,2.131292857,2.144892857,2.124092857, + 2.134492857,2.171392857,2.163692857,2.144092857,2.145092857, + 2.128992857,2.129992857,2.169192857,2.186492857,2.181092857, + 2.217592857,2.210492857,2.223692857], + [-0.16,-0.08,-0.1,-0.16,-0.28,-0.32,-0.3,-0.35,-0.16,-0.1, + -0.35,-0.22,-0.27, -0.31,-0.3,-0.22,-0.11,-0.11,-0.26,-0.17, + -0.08,-0.15,-0.28,-0.37,-0.47,-0.26,-0.22,-0.39,-0.43,-0.48, + -0.43,-0.44,-0.36,-0.34,-0.15,-0.14,-0.36,-0.46,-0.29,-0.27, + -0.27,-0.19,-0.28,-0.26,-0.27,-0.22,-0.1,-0.22,-0.2,-0.36, + -0.16,-0.1,-0.16,-0.29,-0.13,-0.2,-0.15,-0.03,-0.01,-0.02,0.13, + 0.19,0.07,0.09,0.2,0.09,-0.07,-0.03,-0.11,-0.11,-0.17,-0.07,0.01, + 0.08,-0.13,-0.14,-0.19,0.05,0.06,0.03,-0.02,0.06,0.04,0.05,-0.2, + -0.11,-0.06,-0.02,-0.08,0.05,0.02,-0.08,0.01,0.16,-0.07,-0.01,-0.1, + 0.18,0.07,0.16,0.26,0.32,0.14,0.31,0.15,0.11,0.18,0.32,0.38,0.27,0.45, + 0.4,0.22,0.23,0.32,0.45,0.33,0.47,0.61,0.39,0.39,0.54,0.63,0.62,0.54, + 0.68,0.64,0.66,0.54,0.66,0.72,0.61,0.64,0.68,0.75,0.9,1.02,0.92,0.85, + 0.98,0.885114286,0.899814286,0.919314286,0.942414286,0.957814286, + 1.000414286,1.023114286,1.053414286,1.090814286,1.073014286,1.058114286, + 1.117514286,1.123714286,1.123814286,1.177514286,1.190814286,1.187514286, + 1.223514286,1.261714286,1.289014286,1.276414286,1.339114286,1.365714286, + 1.375314286,1.402214286,1.399914286,1.437314286,1.464914286,1.479114286, + 1.505514286,1.509614286,1.539814286,1.558214286,1.595014286,1.637114286, + 1.653414286,1.636714286,1.652214286,1.701014286,1.731114286,1.759214286, + 1.782114286,1.811014286,1.801714286,1.823014286,1.842914286,1.913014286, + 1.943114286,1.977514286,1.982014286,2.007114286,2.066314286,2.079214286, + 2.126014286,2.147314286,2.174914286,2.184414286,2.218514286,2.261514286, + 2.309614286,2.328014286,2.347014286,2.369414286,2.396614286,2.452014286, + 2.473314286,2.486514286,2.497914286,2.518014286,2.561814286,2.613014286, + 2.626814286,2.585914286,2.614614286,2.644714286,2.688414286,2.688514286, + 2.685314286,2.724614286,2.746214286,2.773814286], + [-0.16,-0.08,-0.1,-0.16,-0.28,-0.32,-0.3,-0.35,-0.16,-0.1,-0.35,-0.22, + -0.27,-0.31,-0.3,-0.22,-0.11,-0.11,-0.26,-0.17,-0.08,-0.15,-0.28,-0.37, + -0.47,-0.26,-0.22,-0.39,-0.43,-0.48,-0.43,-0.44,-0.36,-0.34,-0.15,-0.14, + -0.36,-0.46,-0.29,-0.27,-0.27,-0.19,-0.28,-0.26,-0.27,-0.22,-0.1,-0.22, + -0.2,-0.36,-0.16,-0.1,-0.16,-0.29,-0.13,-0.2,-0.15,-0.03,-0.01,-0.02,0.13, + 0.19,0.07,0.09,0.2,0.09,-0.07,-0.03,-0.11,-0.11,-0.17,-0.07,0.01,0.08,-0.13, + -0.14,-0.19,0.05,0.06,0.03,-0.02,0.06,0.04,0.05,-0.2,-0.11,-0.06,-0.02,-0.08, + 0.05,0.02,-0.08,0.01,0.16,-0.07,-0.01,-0.1,0.18,0.07,0.16,0.26,0.32,0.14,0.31, + 0.15,0.11,0.18,0.32,0.38,0.27,0.45,0.4,0.22,0.23,0.32,0.45,0.33,0.47,0.61,0.39, + 0.39,0.54,0.63,0.62,0.54,0.68,0.64,0.66,0.54,0.66,0.72,0.61,0.64, 0.68,0.75,0.9, + 1.02,0.92,0.85,0.98,0.945764286,1.011064286,1.048564286,1.049564286,1.070264286, + 1.126564286,1.195464286,1.215064286,1.246964286,1.272564286,1.262464286, + 1.293464286,1.340864286,1.391164286,1.428764286,1.452564286,1.494164286, + 1.520664286,1.557164286,1.633664286,1.654264286,1.693264286,1.730264286, + 1.795264286,1.824264286,1.823864286,1.880664286,1.952864286,1.991764286, + 1.994764286,2.085764286,2.105764286,2.155064286,2.227464286,2.249964286, + 2.313664286,2.341464286,2.394064286,2.457364286,2.484664286,2.549564286, + 2.605964286,2.656864286,2.707364286,2.742964286,2.789764286,2.847664286, + 2.903564286,2.925064286,2.962864286,3.002664286,3.069264286,3.133364286, + 3.174764286,3.217764286,3.256564286,3.306864286,3.375464286,3.420264286, + 3.476464286,3.493864286,3.552964286,3.592364286,3.630664286,3.672464286, + 3.734364286,3.789764286,3.838164286,3.882264286,3.936064286,3.984064286, + 4.055764286,4.098964286,4.122364286,4.172064286,4.225264286,4.275064286, + 4.339064286,4.375864286,4.408064286,4.477764286] +]) + + gmst_info = { + 'rcps' : ['2.6', '4.5', '6.0', '8.5'], + 'gmst_start_year' : 1880, + 'gmst_end_year' : 2100, + 'gmst_data' : gmst_data + } + + return gmst_info + +def get_knutson_data(): """ - Comparison 2081-2100 (i.e., late twenty-first century) and 2001-20 - (i.e., present day). Late twenty-first century effects on intensity and - frequency per Saffir-Simpson-category and ocean basin is scaled to target - year and target RCP proportional to total radiative forcing of the - respective RCP and year. + Retrieve projections data in Knutson et al., (2020): - Parameters - ---------- - ref_year : int, optional - year between 2000 ad 2100. Default: 2050 - rcp_scenario: int, optional - 26 for RCP 2.6, 45 for RCP 4.5. The default is 45 - 60 for RCP 6.0 and 85 for RCP 8.5. + Tropical cyclones and climate change assessment. Part II: Projected + response to anthropogenic warming. Bull. Amer. Meteor. Soc., 101 (3), E303–E322, + https://doi.org/10.1175/BAMS-D-18-0194.1. + + for 4 variables (i.e., cat05 frequency, cat45 frequency, intensity, precipitation rate), + 6 regions, i.e., N. Atl (NA), NW Pac. (WP), NE Pac (EP)., N. Ind (NI), S. Ind. (SI), + SW Pac. (SP), and 5 percentiles, i.e., 5% or 10%, 25%, 50%, 75%, 95% or 90%. + + The data are available at: + S Jewson, T Knutson, S Camargo, J Chan, K Emanuel, C Ho, J Kossin, M Mohapatra, M Satoh, + M Sugi, K Walsh, & L Wu. (2021). Knutson et al 2020 Tropical Cyclone Projections Data (v0.2) + [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4757343 Returns ------- - factor : float - factor to scale Knutson parameters to the give RCP and year + knutson_data : np.array of dimension (4x6x5) + array contaning data used by Knutson et al. (2020) to project changes in cat05 frequency, + cat45 frequency, intensity and precipitation rate (first array's dimension), for the + N. Atl (NA), NW Pac. (WP), NE Pac (EP)., N. Ind (NI), S. Ind. (SI), SW Pac. (SP) regions + (second array's dimension) for the 5%/10%, 25%, 50%, 75%, 95%/90% percentiles + (thirs array's dimension). """ - # Parameters used in Knutson et al 2015 - base_knu = np.arange(2001, 2021) - end_knu = np.arange(2081, 2101) - rcp_knu = 45 - - # radiative forcings for each RCP scenario - rad_force = pd.read_excel(TOT_RADIATIVE_FORCE) - years = np.array([year for year in rad_force.columns if isinstance(year, int)]) - rad_rcp = np.array([int(float(sce[sce.index('.') - 1:sce.index('.') + 2]) * 10) - for sce in rad_force.Scenario if isinstance(sce, str)]) - - # mean values for Knutson values - rf_vals = np.argwhere(rad_rcp == rcp_knu).reshape(-1)[0] - rf_vals = np.array([rad_force.iloc[rf_vals][year] for year in years]) - rf_base = np.nanmean(np.interp(base_knu, years, rf_vals)) - rf_end = np.nanmean(np.interp(end_knu, years, rf_vals)) - - # scale factor for ref_year and rcp_scenario - rf_vals = np.argwhere(rad_rcp == rcp_scenario).reshape(-1)[0] - rf_vals = np.array([rad_force.iloc[rf_vals][year] for year in years]) - rf_sel = np.interp(ref_year, years, rf_vals) - return max((rf_sel - rf_base) / (rf_end - rf_base), 0) + + # The knutson_data array has dimension: + # 4 (tropical cyclones variables) x 6 (tropical cyclone regions) x 5 (percentiles) + knutson_data = np.array([[ + [-34.49,-24.875,-14.444,3.019,28.737], + [-30.444,-20,-10.27,0.377,17.252], + [-32.075,-18.491,-3.774,11.606,36.682], + [-35.094,-15.115,-4.465,5.785,29.405], + [-32.778,-22.522,-17.297,-8.995,7.241], + [-40.417,-26.321,-18.113,-8.21,4.689]], + [ + [-38.038,-22.264,11.321,38.302,81.874], + [-25.811,-14.34,-4.75,16.146,41.979], + [-24.83,-6.792,22.642,57.297,104.315], + [-30.566,-16.415,5.283,38.491,79.119], + [-23.229,-13.611,4.528,26.645,63.514], + [-42.453,-29.434,-14.467,-0.541,19.061]], + [ + [0.543,1.547,2.943,4.734,6.821], + [1.939,3.205,5.328,6.549,9.306], + [-2.217,0.602,5.472,9.191,10.368], + [-0.973,1.944,4.324,6.15,7.808], + [1.605,3.455,5.405,7.69,10.884], + [-6.318,-0.783,0.938,5.314,12.213]], + [ + [5.848,9.122,15.869,20.352,22.803], + [6.273,12.121,16.486,18.323,23.784], + [6.014,8.108,21.081,29.324,31.838], + [12.703,14.347,17.649,19.182,20.77], + [2.2,11.919,19.73,23.115,26.243], + [-1.299,5.137,7.297,11.091,15.419] + ]]) + + return knutson_data diff --git a/climada/hazard/tc_tracks.py b/climada/hazard/tc_tracks.py index 683588ab8..311901d44 100644 --- a/climada/hazard/tc_tracks.py +++ b/climada/hazard/tc_tracks.py @@ -334,11 +334,11 @@ def from_ibtracs_netcdf(cls, provider=None, rescale_windspeeds=True, storm_id=No When using data from IBTrACS, make sure to be familiar with the scope and limitations of IBTrACS, e.g. by reading the official documentation - (https://www.ncdc.noaa.gov/ibtracs/pdf/IBTrACS_version4_Technical_Details.pdf). Reading the - CLIMADA documentation can't replace a thorough understanding of the underlying data. This - function only provides a (hopefully useful) interface for the data input, but cannot - provide any guidance or make recommendations about if and how to use IBTrACS data for your - particular project. + (https://www.ncei.noaa.gov/sites/default/files/2021-07/IBTrACS_version4_Technical_Details.pdf). + Reading the CLIMADA documentation can't replace a thorough understanding of the underlying + data. This function only provides a (hopefully useful) interface for the data input, but + cannot provide any guidance or make recommendations about if and how to use IBTrACS data + for your particular project. Resulting tracks are required to have both pressure and wind speed information at all time steps. Therefore, all track positions where one of wind speed or pressure are missing are @@ -377,8 +377,8 @@ def from_ibtracs_netcdf(cls, provider=None, rescale_windspeeds=True, storm_id=No rescale_windspeeds : bool, optional If True, all wind speeds are linearly rescaled to 1-minute sustained winds. Note however that the IBTrACS documentation (Section 5.2, - https://www.ncdc.noaa.gov/ibtracs/pdf/IBTrACS_version4_Technical_Details.pdf) includes - a warning about this kind of conversion: "While a multiplicative factor can describe + https://www.ncei.noaa.gov/sites/default/files/2021-07/IBTrACS_version4_Technical_Details.pdf) + includes a warning about this kind of conversion: "While a multiplicative factor can the numerical differences, there are procedural and observational differences between agencies that can change through time, which confounds the simple multiplicative factor." Default: True diff --git a/climada/hazard/test/test_tc_cc.py b/climada/hazard/test/test_tc_cc.py index 00264e5f1..aac375e88 100644 --- a/climada/hazard/test/test_tc_cc.py +++ b/climada/hazard/test/test_tc_cc.py @@ -21,45 +21,127 @@ import unittest +import unittest +from math import log +import pandas as pd +import numpy as np import climada.hazard.tc_clim_change as tc_cc class TestKnutson(unittest.TestCase): - """Test loading funcions from the TropCyclone class""" - def test_get_pass(self): + def test_get_knutson_scaling_calculations(self): + + basin = 'NA' + variable = 'cat05' + percentile = '5/10' + base_start, base_end = 1950, 2018 + yearly_steps = 5 + + predicted_changes = tc_cc.get_knutson_scaling_factor( + percentile=percentile, + variable=variable, + basin=basin, + baseline=(base_start, base_end), + yearly_steps=yearly_steps + ) + + ## Test computations of future changes + # Load data + gmst_info = tc_cc.get_gmst_info() + + var_id, basin_id, perc_id = (tc_cc.MAP_VARS_NAMES[variable], + tc_cc.MAP_BASINS_NAMES[basin], + tc_cc.MAP_PERC_NAMES[percentile]) + + knutson_data = tc_cc.get_knutson_data() + knutson_value = knutson_data[var_id, basin_id, perc_id] + + start_ind = base_start - gmst_info['gmst_start_year'] + end_ind = base_end - gmst_info['gmst_start_year'] + + # Apply model + beta = 0.5 * log(0.01 * knutson_value + 1) + tc_properties = np.exp(beta * gmst_info['gmst_data']) + + # Assess baseline value + baseline = np.mean(tc_properties[:, start_ind:end_ind + 1], 1) + + # Assess future value and test predicted change from baseline is + # the same as given by function + smoothing = 5 + + for target_year in [2030, 2050, 2070, 2090]: + target_year_ind = target_year - gmst_info['gmst_start_year'] + ind1 = target_year_ind - smoothing + ind2 = target_year_ind + smoothing + 1 + + prediction = np.mean(tc_properties[:, ind1:ind2], 1) + predicted_change = ((prediction - baseline) / baseline) * 100 + + np.testing.assert_array_almost_equal(predicted_changes.loc[target_year, '2.6'], predicted_change[0]) + np.testing.assert_array_almost_equal(predicted_changes.loc[target_year, '4.5'], predicted_change[1]) + np.testing.assert_array_almost_equal(predicted_changes.loc[target_year, '6.0'], predicted_change[2]) + np.testing.assert_array_almost_equal(predicted_changes.loc[target_year, '8.5'], predicted_change[3]) + + def test_get_knutson_scaling_structure(self): """Test get_knutson_criterion function.""" - criterion = tc_cc.get_knutson_criterion() - self.assertTrue(len(criterion), 20) - for crit_val in criterion: - self.assertTrue('year' in crit_val) - self.assertTrue('change' in crit_val) - self.assertTrue('variable' in crit_val) - self.assertEqual(criterion[0]['variable'], "frequency") - self.assertEqual(criterion[0]['change'], 1) - self.assertEqual(criterion[4]['variable'], "intensity") - self.assertEqual(criterion[4]['change'], 1.045) - self.assertEqual(criterion[-10]['basin'], "SP") - self.assertEqual(criterion[-10]['variable'], "frequency") - self.assertEqual(criterion[-10]['change'], 1 - 0.583) - - def test_scale_pass(self): - """Test calc_scale_knutson function.""" - self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2050, rcp_scenario=45), - 0.759630751756698) - self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2070, rcp_scenario=45), - 0.958978483788876) - self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2060, rcp_scenario=60), - 0.825572149523299) - self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2080, rcp_scenario=60), - 1.309882943406079) - self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2090, rcp_scenario=85), - 2.635069196605717) - self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2100, rcp_scenario=85), - 2.940055236533517) - self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2066, rcp_scenario=26), - 0.341930203294547) - self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2078, rcp_scenario=26), - 0.312383928930456) + + yearly_steps = 8 + predicted_changes = tc_cc.get_knutson_scaling_factor(yearly_steps=yearly_steps) + + np.testing.assert_equal(predicted_changes.columns, np.array(['2.6', '4.5', '6.0', '8.5'])) + + simulated_years = np.arange(tc_cc.YEAR_WINDOWS_PROPS['start'], + tc_cc.YEAR_WINDOWS_PROPS['end']+1, + yearly_steps) + np.testing.assert_equal(predicted_changes.index, simulated_years) + + def test_get_knutson_scaling_valid_inputs(self): + df = tc_cc.get_knutson_scaling_factor() + self.assertIsInstance(df, pd.DataFrame) + np.testing.assert_equal(df.shape, (21, 4)) + + def test_get_knutson_scaling_invalid_baseline_start_year(self): + with self.assertRaises(ValueError): + tc_cc.get_knutson_scaling_factor(baseline=(1870, 2022)) + + def test_get_knutson_scaling_invalid_baseline_end_year(self): + with self.assertRaises(ValueError): + tc_cc.get_knutson_scaling_factor(baseline=(1982, 2110)) + + def test_get_knutson_scaling_no_scaling_factors_for_unknonw_basin(self): + df = tc_cc.get_knutson_scaling_factor(basin='ZZZZZ') + self.assertIsInstance(df, pd.DataFrame) + np.testing.assert_equal(df.values, np.ones_like(df.values)) + + def test_get_gmst(self): + """Test get_gmst_info function.""" + gmst_info = tc_cc.get_gmst_info() + + self.assertAlmostEqual(gmst_info['gmst_start_year'], 1880) + self.assertAlmostEqual(gmst_info['gmst_end_year'], 2100) + self.assertAlmostEqual(len(gmst_info['rcps']), 4) + + self.assertAlmostEqual(gmst_info['gmst_data'].shape, + (len(gmst_info['rcps']), + gmst_info['gmst_end_year']-gmst_info['gmst_start_year']+1)) + self.assertAlmostEqual(gmst_info['gmst_data'][0,0], -0.16) + self.assertAlmostEqual(gmst_info['gmst_data'][0,-1], 1.27641, 4) + self.assertAlmostEqual(gmst_info['gmst_data'][-1,0], -0.16) + self.assertAlmostEqual(gmst_info['gmst_data'][-1,-1], 4.477764, 4) + + def test_get_knutson_data_pass(self): + """Test get_knutson_data function.""" + + data_knutson = tc_cc.get_knutson_data() + + self.assertAlmostEqual(data_knutson.shape, (4,6,5)) + self.assertAlmostEqual(data_knutson[0,0,0], -34.49) + self.assertAlmostEqual(data_knutson[-1,-1,-1], 15.419) + self.assertAlmostEqual(data_knutson[0,-1,-1], 4.689) + self.assertAlmostEqual(data_knutson[-1,0,0], 5.848) + self.assertAlmostEqual(data_knutson[-1,0,-1], 22.803) + self.assertAlmostEqual(data_knutson[2,3,2], 4.324) if __name__ == "__main__": TESTS = unittest.TestLoader().loadTestsFromTestCase(TestKnutson) diff --git a/climada/hazard/test/test_trop_cyclone.py b/climada/hazard/test/test_trop_cyclone.py index 41ab3a81f..ea52017a0 100644 --- a/climada/hazard/test/test_trop_cyclone.py +++ b/climada/hazard/test/test_trop_cyclone.py @@ -31,6 +31,7 @@ from climada.util import ureg from climada.test import get_test_file from climada.hazard.tc_tracks import TCTracks +from climada.hazard.tc_clim_change import get_knutson_scaling_factor from climada.hazard.trop_cyclone.trop_cyclone import ( TropCyclone, ) from climada.hazard.centroids.centr import Centroids @@ -292,140 +293,75 @@ def test_two_files_pass(self): class TestClimateSce(unittest.TestCase): - def test_apply_criterion_track(self): - """Test _apply_criterion function.""" + def create_tc(self): + """Create mock TropCyclone object.""" + # Setup data directly intensity = np.zeros((4, 10)) intensity[0, :] = np.arange(10) intensity[1, 5] = 10 intensity[2, :] = np.arange(10, 20) intensity[3, 3] = 3 - tc = TropCyclone( + + self.tc = TropCyclone( intensity=sparse.csr_matrix(intensity), - basin=['NA', 'NA', 'NA', 'NO'], + basin=['NA', 'NA', 'NA', 'WP'], category=np.array([2, 0, 4, 1]), - event_id=np.arange(4), - frequency=np.ones(4) * 0.5, + event_id=np.arange(intensity.shape[0]), + frequency=np.repeat(1./intensity.shape[0], intensity.shape[0]), + date=np.array([723795, 728395, 738395, 724395]) ) - tc_cc = tc.apply_climate_scenario_knu(ref_year=2050, rcp_scenario=45) - self.assertTrue(np.allclose(tc.intensity[1, :].toarray(), tc_cc.intensity[1, :].toarray())) - self.assertTrue(np.allclose(tc.intensity[3, :].toarray(), tc_cc.intensity[3, :].toarray())) - self.assertFalse( - np.allclose(tc.intensity[0, :].toarray(), tc_cc.intensity[0, :].toarray())) - self.assertFalse( - np.allclose(tc.intensity[2, :].toarray(), tc_cc.intensity[2, :].toarray())) - self.assertTrue(np.allclose(tc.frequency, tc_cc.frequency)) - - def test_apply_criterion_track2(self): + def test_apply_climate_scenario_knu_calculations(self): """Test _apply_criterion function.""" - criterion = [{'basin': 'NA', 'category': [1, 2, 3, 4, 5], - 'year': 2100, 'change': 1.045, 'variable': 'intensity'} - ] - scale = 0.75 - # artificially increase the size of the hazard by repeating (tiling) the data: - ntiles = 8 + ## Build tc object + self.create_tc() - intensity = np.zeros((4, 10)) - intensity[0, :] = np.arange(10) - intensity[1, 5] = 10 - intensity[2, :] = np.arange(10, 20) - intensity[3, 3] = 3 - intensity = np.tile(intensity, (ntiles, 1)) - tc = TropCyclone( - intensity=sparse.csr_matrix(intensity), - basin=ntiles * ['NA', 'NA', 'NA', 'WP'], - category=np.array(ntiles * [2, 0, 4, 1]), - event_id=np.arange(intensity.shape[0]), - ) + cat05_sel = np.repeat(True, self.tc.category.shape[0]) + cat03_sel = np.array([cat in [0,1,2,3] for cat in self.tc.category]) + cat45_sel = np.array([cat in [4,5] for cat in self.tc.category]) - tc_cc = tc._apply_knutson_criterion(criterion, scale) - for i_tile in range(ntiles): - offset = i_tile * 4 - # no factor applied because of category 0 - np.testing.assert_array_equal( - tc.intensity[offset + 1, :].toarray(), tc_cc.intensity[offset + 1, :].toarray()) - # no factor applied because of basin "WP" - np.testing.assert_array_equal( - tc.intensity[offset + 3, :].toarray(), tc_cc.intensity[offset + 3, :].toarray()) - # factor is applied to the remaining events - np.testing.assert_array_almost_equal( - tc.intensity[offset + 0, :].toarray() * 1.03375, - tc_cc.intensity[offset + 0, :].toarray()) - np.testing.assert_array_almost_equal( - tc.intensity[offset + 2, :].toarray() * 1.03375, - tc_cc.intensity[offset + 2, :].toarray()) - - def test_two_criterion_track(self): - """Test _apply_criterion function with two criteria""" - criterion = [ - {'basin': 'NA', 'category': [1, 2, 3, 4, 5], - 'year': 2100, 'change': 1.045, 'variable': 'intensity'}, - {'basin': 'WP', 'category': [1, 2, 3, 4, 5], - 'year': 2100, 'change': 1.025, 'variable': 'intensity'}, - {'basin': 'WP', 'category': [1, 2, 3, 4, 5], - 'year': 2100, 'change': 1.025, 'variable': 'frequency'}, - {'basin': 'NA', 'category': [0, 1, 2, 3, 4, 5], - 'year': 2100, 'change': 0.7, 'variable': 'frequency'}, - {'basin': 'NA', 'category': [1, 2, 3, 4, 5], - 'year': 2100, 'change': 1, 'variable': 'frequency'}, - {'basin': 'NA', 'category': [3, 4, 5], - 'year': 2100, 'change': 1, 'variable': 'frequency'}, - {'basin': 'NA', 'category': [4, 5], - 'year': 2100, 'change': 2, 'variable': 'frequency'} - ] - scale = 0.75 + ## Retrieve scaling factors for cat 4 to 5 and 0 to 5 + percentile = '50' + target_year = 2035 + rcp = '8.5' - intensity = np.zeros((4, 10)) - intensity[0, :] = np.arange(10) - intensity[1, 5] = 10 - intensity[2, :] = np.arange(10, 20) - intensity[3, 3] = 3 - tc = TropCyclone( - intensity=sparse.csr_matrix(intensity), - frequency=np.ones(4) * 0.5, - basin=['NA', 'NA', 'NA', 'WP'], - category=np.array([2, 0, 4, 1]), - event_id=np.arange(4), - ) + future_tc = self.tc.apply_climate_scenario_knu(percentile=percentile, + scenario=rcp, + target_year=target_year) - tc_cc = tc._apply_knutson_criterion(criterion, scale) - self.assertTrue(np.allclose(tc.intensity[1, :].toarray(), tc_cc.intensity[1, :].toarray())) - self.assertFalse( - np.allclose(tc.intensity[3, :].toarray(), tc_cc.intensity[3, :].toarray())) - self.assertFalse( - np.allclose(tc.intensity[0, :].toarray(), tc_cc.intensity[0, :].toarray())) - self.assertFalse( - np.allclose(tc.intensity[2, :].toarray(), tc_cc.intensity[2, :].toarray())) - self.assertTrue( - np.allclose(tc.intensity[0, :].toarray() * 1.03375, tc_cc.intensity[0, :].toarray())) - self.assertTrue( - np.allclose(tc.intensity[2, :].toarray() * 1.03375, tc_cc.intensity[2, :].toarray())) - self.assertTrue( - np.allclose(tc.intensity[3, :].toarray() * 1.01875, tc_cc.intensity[3, :].toarray())) - - res_frequency = np.ones(4) * 0.5 - res_frequency[1] = 0.5 * (1 + (0.7 - 1) * scale) - res_frequency[2] = 0.5 * (1 + (2 - 1) * scale) - res_frequency[3] = 0.5 * (1 + (1.025 - 1) * scale) - self.assertTrue(np.allclose(tc_cc.frequency, res_frequency)) - - def test_negative_freq_error(self): - """Test _apply_knutson_criterion with infeasible input.""" - criterion = [{'basin': 'SP', 'category': [0, 1], - 'year': 2100, 'change': 0.5, - 'variable': 'frequency'} - ] - - tc = TropCyclone( - frequency=np.ones(2), - basin=['SP', 'SP'], - category=np.array([0, 1]), - ) + for basin in np.unique(self.tc.basin): + basin_sel = np.array(self.tc.basin)==basin - with self.assertRaises(ValueError): - tc._apply_knutson_criterion(criterion, 3) + scaling_05, scaling_45 = [ + get_knutson_scaling_factor(percentile=percentile, + variable=variable, + basin=basin).loc[target_year, rcp] + for variable in ['cat05', 'cat45'] + ] + + ## Calulate scaling factors for cat 0 to 3 + freq_weighted_scaling_05 = scaling_05 * np.sum(self.tc.frequency[cat05_sel & basin_sel]) + freq_weighted_scaling_45 = scaling_45 * np.sum(self.tc.frequency[cat45_sel & basin_sel]) + freq_sum_03 = np.sum(self.tc.frequency[cat03_sel & basin_sel]) + + scaling_03 = (freq_weighted_scaling_05 - freq_weighted_scaling_45) / freq_sum_03 + + ## Check that frequencies obtained by function are the same as those obtained by scaling + ## historic frequencies with retrieved scaling factors + np.testing.assert_array_equal( + self.tc.frequency[cat03_sel & basin_sel] * (1 + scaling_03/100), + future_tc.frequency[cat03_sel & basin_sel] + ) + np.testing.assert_array_equal( + self.tc.frequency[cat45_sel & basin_sel] * (1 + scaling_45/100), + future_tc.frequency[cat45_sel & basin_sel] + ) + def test_apply_climate_scenario_knu_target_year_out_of_range(self): + self.create_tc() + with self.assertRaises(KeyError): + self.tc.apply_climate_scenario_knu(target_year=2200) class TestDumpReloadCycle(unittest.TestCase): def setUp(self): @@ -447,7 +383,7 @@ def tearDown(self): if __name__ == "__main__": - TESTS = unittest.TestLoader().loadTestsFromTestCase(TestReader) + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestClimateSce) TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestClimateSce)) TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestDumpReloadCycle)) unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/hazard/trop_cyclone/trop_cyclone.py b/climada/hazard/trop_cyclone/trop_cyclone.py index 6fc8347e9..1fcd8f280 100644 --- a/climada/hazard/trop_cyclone/trop_cyclone.py +++ b/climada/hazard/trop_cyclone/trop_cyclone.py @@ -37,7 +37,7 @@ from climada.hazard.base import Hazard from climada.hazard.tc_tracks import TCTracks -from climada.hazard.tc_clim_change import get_knutson_criterion, calc_scale_knutson +from climada.hazard.tc_clim_change import get_knutson_scaling_factor from climada.hazard.centroids.centr import Centroids import climada.util.constants as u_const import climada.util.coordinates as u_coord @@ -366,46 +366,99 @@ def from_tracks( def apply_climate_scenario_knu( self, - ref_year: int = 2050, - rcp_scenario: int = 45 + percentile: str='50', + scenario: str='4.5', + target_year: int=2050, + **kwargs ): """ - From current TC hazard instance, return new hazard set with - future events for a given RCP scenario and year based on the - parametrized values derived from Table 3 in Knutson et al 2015. - https://doi.org/10.1175/JCLI-D-15-0129.1 . The scaling for different - years and RCP scenarios is obtained by linear interpolation. - - Note: The parametrized values are derived from the overall changes - in statistical ensemble of tracks. Hence, this method should only be - applied to sufficiently large tropical cyclone event sets that - approximate the reference years 1981 - 2008 used in Knutson et. al. - - The frequency and intensity changes are applied independently from - one another. The mean intensity factors can thus slightly deviate - from the Knutson value (deviation was found to be less than 1% - for default IBTrACS event sets 1980-2020 for each basin). + From current TC hazard instance, return new hazard set with future events + for a given RCP scenario and year based on the parametrized values derived + by Jewson 2021 (https://doi.org/10.1175/JAMC-D-21-0102.1) based on those + published by Knutson 2020 (https://doi.org/10.1175/BAMS-D-18-0194.1). The + scaling for different years and RCP scenarios is obtained by linear + interpolation. + + Note: Only frequency changes are applied as suggested by Jewson 2022 + (https://doi.org/10.1007/s00477-021-02142-6). Applying only frequency anyway + changes mean intensities and most importantly avoids possible inconsistencies + (including possible counting) that may arise from the application of both + frequency and intensity changes, as the relatioship between these two is non + trivial to resolve. Parameters ---------- - ref_year : int - year between 2000 ad 2100. Default: 2050 - rcp_scenario : int - 26 for RCP 2.6, 45 for RCP 4.5, 60 for RCP 6.0 and 85 for RCP 8.5. - The default is 45. - + percentile: str + percentiles of Knutson et al. 2020 estimates, representing the mode + uncertainty in future changes in TC activity. These estimates come from + a review of state-of-the-art literature and models. For the 'cat05' variable + (i.e. frequency of all tropical cyclones) the 5th, 25th, 50th, 75th and 95th + percentiles are provided. For 'cat45' and 'intensity', the provided percentiles + are the 10th, 25th, 50th, 75th and 90th. Please refer to the mentioned publications + for more details. + possible percentiles: + '5/10' either the 5th or 10th percentile depending on variable (see text above) + '25' for the 25th percentile + '50' for the 50th percentile + '75' for the 75th percentile + '90/95' either the 90th or 95th percentile depending on variable (see text above) + Default: '50' + scenario : str + possible scenarios: + '2.6' for RCP 2.6 + '4.5' for RCP 4.5 + '6.0' for RCP 6.0 + '8.5' for RCP 8.5 + target_year : int + future year to be simulated, between 2000 and 2100. Default: 2050. Returns ------- haz_cc : climada.hazard.TropCyclone Tropical cyclone with frequencies and intensity scaled according - to the Knutson criterion for the given year and RCP. Returns - a new instance of climada.hazard.TropCyclone, self is not + to the Knutson criterion for the given year, RCP and percentile. + Returns a new instance of climada.hazard.TropCyclone, self is not modified. """ - chg_int_freq = get_knutson_criterion() - scale_rcp_year = calc_scale_knutson(ref_year, rcp_scenario) - haz_cc = self._apply_knutson_criterion(chg_int_freq, scale_rcp_year) - return haz_cc + + tc_cc = copy.deepcopy(self) + + sel_cat05 = np.isin(tc_cc.category, [0, 1, 2, 3, 4, 5]) + sel_cat03 = np.isin(tc_cc.category, [0, 1, 2, 3]) + sel_cat45 = np.isin(tc_cc.category, [4, 5]) + + years = np.array([dt.datetime.fromordinal(date).year for date in self.date]) + + for basin in np.unique(tc_cc.basin): + scale_year_rcp_05, scale_year_rcp_45 = [ + get_knutson_scaling_factor( + percentile=percentile, + variable=variable, + basin=basin, + baseline=(np.min(years), np.max(years)), + **kwargs + ).loc[target_year, scenario] + for variable in ['cat05', 'cat45'] + ] + + bas_sel = np.array(tc_cc.basin) == basin + + cat_05_freqs_change = scale_year_rcp_05 * np.sum(tc_cc.frequency[sel_cat05 & bas_sel]) + cat_45_freqs_change = scale_year_rcp_45 * np.sum(tc_cc.frequency[sel_cat45 & bas_sel]) + cat_03_freqs = np.sum(tc_cc.frequency[sel_cat03 & bas_sel]) + + scale_year_rcp_03 = (cat_05_freqs_change-cat_45_freqs_change) / cat_03_freqs + + tc_cc.frequency[sel_cat03 & bas_sel] *= 1 + scale_year_rcp_03/100 + tc_cc.frequency[sel_cat45 & bas_sel] *= 1 + scale_year_rcp_45/100 + + if any(tc_cc.frequency) < 0: + raise ValueError( + " The application of the climate scenario leads to " + " negative frequencies. One solution - if appropriate -" + " could be to use a less extreme percentile." + ) + + return tc_cc def set_climate_scenario_knu(self, *args, **kwargs): """This function is deprecated, use TropCyclone.apply_climate_scenario_knu instead.""" diff --git a/doc/misc/citation.rst b/doc/misc/citation.rst index e41d85cac..91570dbe4 100644 --- a/doc/misc/citation.rst +++ b/doc/misc/citation.rst @@ -26,11 +26,13 @@ If you use specific tools and modules of CLIMADA, please cite the appropriate pu * - :doc:`Uncertainty and sensitivity analysis ` - Kropf, C. M. et al. (2022): Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0. Geosci. Model Dev. 15, 7177–7201, https://doi.org/10.5194/gmd-15-7177-2022 * - :doc:`Lines and polygons exposures ` *or* `Open Street Map exposures `_ - - Mühlhofer, E., et al. (2023): OpenStreetMap for Multi-Faceted Climate Risk Assessments https://eartharxiv.org/repository/view/5615/ + - Mühlhofer, E., et al. (2024): OpenStreetMap for Multi-Faceted Climate Risk Assessments : Environ. Res. Commun. 6 015005, https://doi.org/10.1088/2515-7620/ad15ab * - :doc:`LitPop exposures ` - Eberenz, S., et al. (2020): Asset exposure data for global physical risk assessment. Earth System Science Data 12, 817–833, https://doi.org/10.3929/ethz-b-000409595 * - :doc:`Impact function calibration ` - - Riedel, L., et al. (2024): A Module for Calibrating Impact Functions in the Climate Risk Modeling Platform CLIMADA, Journal of Open Source Software [`under review `_] + - Riedel, L., et al. (2024): A Module for Calibrating Impact Functions in the Climate Risk Modeling Platform CLIMADA. Journal of Open Source Software, 9(99), 6755, https://doi.org/10.21105/joss.06755 + * - `GloFAS River Flood Module `_ + - Riedel, L. et al. (2024): Fluvial flood inundation and socio-economic impact model based on open data, Geosci. Model Dev., 17, 5291–5308, https://doi.org/10.5194/gmd-17-5291-2024 Please find the code to reprocduce selected CLIMADA-related scientific publications in our `repository of scientific publications `_. diff --git a/doc/misc/climada_publications.bib b/doc/misc/climada_publications.bib index f00b9cc16..416d85da1 100644 --- a/doc/misc/climada_publications.bib +++ b/doc/misc/climada_publications.bib @@ -55,10 +55,12 @@ @article{Kropf2022c @article{Muhlhofer2023b, title = {{{OpenStreetMap}} for {{Multi-Faceted Climate Risk Assessments}}}, - author = {M{\"u}hlhofer, Evelyn and Kropf, Chahan M. and Bresch, David N. and Koks, Elco E.}, - year = {2023}, - publisher = {{EarthArXiv}}, - url = {https://eartharxiv.org/repository/view/5615/} + author = {M{\"u}hlhofer, Evelyn and Kropf, Chahan M. and Riedel, Lukas and Bresch, David N. and Koks, Elco E.}, + year = {2024}, + journal = {Environ. Res. Commun.}, + volume = {6}, + publisher = {{IOP}}, + doi = {https://doi.org/10.1088/2515-7620/ad15ab} } @article{Muhlhofer2023c, @@ -71,3 +73,32 @@ @article{Muhlhofer2023c issn = {0951-8320}, doi = {10.1016/j.ress.2023.109194} } + +@article{Riedel2024a, + title = {Fluvial flood inundation and socio-economic impact model based on open data}, + author = {Riedel, Lukas and R{\"o}{\"o}sli, Thomas and Vogt, Thomas and Bresch, David N.}, + journal = {Geoscientific Model Development}, + volume = {17}, + issn = {1991-959X}, + doi = {10.5194/gmd-17-5291-2024}, + number = {13}, + month = jul, + year = {2024}, + pages = {5291--5308}, +} + +@article{Riedel2024b, + title = {A {Module} for {Calibrating} {Impact} {Functions} in the {Climate} {Risk} {Modeling} {Platform} {CLIMADA}}, + author = {Riedel, Lukas and Kropf, Chahan M. and Schmid, Timo}, + journal = {Journal of Open Source Software}, + volume = {9}, + issn = {2475-9066}, + url = {https://joss.theoj.org/papers/10.21105/joss.06755}, + doi = {10.21105/joss.06755}, + language = {en}, + number = {99}, + urldate = {2024-07-29}, + month = jul, + year = {2024}, + pages = {6755}, +} diff --git a/doc/tutorial/climada_hazard_TropCyclone.ipynb b/doc/tutorial/climada_hazard_TropCyclone.ipynb index 9c0b1d47f..51a94a9f4 100644 --- a/doc/tutorial/climada_hazard_TropCyclone.ipynb +++ b/doc/tutorial/climada_hazard_TropCyclone.ipynb @@ -47,6 +47,41 @@ "
" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How is this tutorial structured?\n", + "\n", + "[**Part 1: Load TC tracks**](#Part1)\n", + "\n", + "
    \n", + "\n", + "[**a) Load TC tracks from historical records**](#Part1.a)\n", + "\n", + "[**b) Generate probabilistic events**](#Part1.b) \n", + " \n", + "[**c) ECMWF Forecast Tracks**](#Part1.c) \n", + "\n", + "[**d) Load TC tracks from other sources**](#Part1.d) \n", + "\n", + "
\n", + "\n", + "[**Part 2: `TropCyclone()` class**](#Part2) \n", + "\n", + "
    \n", + "\n", + "[**a) Default hazard generation for tropical cyclones**](#Part2.a)\n", + "\n", + "[**b) Implementing climate change**](#Part2.b)\n", + "\n", + "[**c) Multiprocessing - improving performance for big computations**](#Part2.c)\n", + "\n", + "[**d) Making videos**](#Part2.d)\n", + " \n", + "
" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -62,7 +97,7 @@ "metadata": {}, "source": [ " \n", - "### a) Load TC tracks from historical records\n", + "## a) Load TC tracks from historical records\n", "\n", "The best-track historical data from the International Best Track Archive for Climate Stewardship ([IBTrACS](https://www.ncdc.noaa.gov/ibtracs/)) can easily be loaded into CLIMADA to study the historical records of TC events. The constructor `from_ibtracs_netcdf()` generates the `Datasets` for tracks selected by [IBTrACS](https://www.ncdc.noaa.gov/ibtracs/) id, or by basin and year range. To achieve this, it downloads the first time the [IBTrACS data v4 in netcdf format](https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r00/access/netcdf/) and stores it in `~/climada/data/`. The tracks can be accessed later either using the attribute `data` or using `get_track()`, which allows to select tracks by its name or id. Use the method `append()` to extend the `data` list.\n", "\n", @@ -81,60 +116,4772 @@ } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 12.0.1. Please consider upgrading.\n", + " warnings.warn(\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "2021-06-04 17:07:33,515 - climada.hazard.tc_tracks - INFO - Progress: 100%\n", - "2021-06-04 17:07:35,833 - climada.hazard.tc_tracks - WARNING - 19 storm events are discarded because no valid wind/pressure values have been found: 1993178N14265, 1993221N12216, 1993223N07185, 1993246N16129, 1993263N11168, ...\n", - "2021-06-04 17:07:35,940 - climada.hazard.tc_tracks - INFO - Progress: 11%\n", - "2021-06-04 17:07:36,028 - climada.hazard.tc_tracks - INFO - Progress: 23%\n", - "2021-06-04 17:07:36,119 - climada.hazard.tc_tracks - INFO - Progress: 35%\n", - "2021-06-04 17:07:36,218 - climada.hazard.tc_tracks - INFO - Progress: 47%\n", - "2021-06-04 17:07:36,312 - climada.hazard.tc_tracks - INFO - Progress: 58%\n", - "2021-06-04 17:07:36,399 - climada.hazard.tc_tracks - INFO - Progress: 70%\n", - "2021-06-04 17:07:36,493 - climada.hazard.tc_tracks - INFO - Progress: 82%\n", - "2021-06-04 17:07:36,585 - climada.hazard.tc_tracks - INFO - Progress: 94%\n", - "2021-06-04 17:07:36,612 - climada.hazard.tc_tracks - INFO - Progress: 100%\n", - "Number of tracks: 33\n", - "2021-06-04 17:07:38,825 - climada.hazard.tc_tracks - INFO - Progress: 100%\n", - "2021-06-04 17:07:39,974 - climada.hazard.tc_tracks - INFO - Progress: 100%\n" + "2024-08-06 16:27:44,455 - climada.hazard.tc_tracks - WARNING - The cached IBTrACS data set dates from 2023-06-07 23:07:38 (older than 180 days). Very likely, a more recent version is available. Consider manually removing the file /Users/aciullo/climada/data/IBTrACS.ALL.v04r00.nc and re-running this function, which will download the most recent version of the IBTrACS data set from the official URL.\n", + "2024-08-06 16:27:45,084 - climada.hazard.tc_tracks - INFO - Progress: 100%\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/aciullo/Documents/GitHub/climada_python/climada/hazard/tc_tracks.py:614: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n", + " if ibtracs_ds.dims['storm'] == 0:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-06 16:27:46,216 - climada.hazard.tc_tracks - WARNING - The cached IBTrACS data set dates from 2023-06-07 23:07:38 (older than 180 days). Very likely, a more recent version is available. Consider manually removing the file /Users/aciullo/climada/data/IBTrACS.ALL.v04r00.nc and re-running this function, which will download the most recent version of the IBTrACS data set from the official URL.\n", + "2024-08-06 16:27:46,923 - climada.hazard.tc_tracks - WARNING - 19 storm events are discarded because no valid wind/pressure values have been found: 1993178N14265, 1993221N12216, 1993223N07185, 1993246N16129, 1993263N11168, ...\n", + "2024-08-06 16:27:46,943 - climada.hazard.tc_tracks - INFO - Progress: 11%\n", + "2024-08-06 16:27:46,961 - climada.hazard.tc_tracks - INFO - Progress: 23%\n", + "2024-08-06 16:27:46,979 - climada.hazard.tc_tracks - INFO - Progress: 35%\n", + "2024-08-06 16:27:46,998 - climada.hazard.tc_tracks - INFO - Progress: 47%\n", + "2024-08-06 16:27:47,016 - climada.hazard.tc_tracks - INFO - Progress: 58%\n", + "2024-08-06 16:27:47,035 - climada.hazard.tc_tracks - INFO - Progress: 70%\n", + "2024-08-06 16:27:47,052 - climada.hazard.tc_tracks - INFO - Progress: 82%\n", + "2024-08-06 16:27:47,070 - climada.hazard.tc_tracks - INFO - Progress: 94%\n", + "2024-08-06 16:27:47,076 - climada.hazard.tc_tracks - INFO - Progress: 100%\n", + "Number of tracks: 33\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/aciullo/Documents/GitHub/climada_python/climada/hazard/tc_tracks.py:614: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n", + " if ibtracs_ds.dims['storm'] == 0:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-06 16:27:48,261 - climada.hazard.tc_tracks - WARNING - The cached IBTrACS data set dates from 2023-06-07 23:07:38 (older than 180 days). Very likely, a more recent version is available. Consider manually removing the file /Users/aciullo/climada/data/IBTrACS.ALL.v04r00.nc and re-running this function, which will download the most recent version of the IBTrACS data set from the official URL.\n", + "2024-08-06 16:27:48,849 - climada.hazard.tc_tracks - INFO - Progress: 100%\n", + "2024-08-06 16:27:48,981 - climada.hazard.tc_tracks - WARNING - The cached IBTrACS data set dates from 2023-06-07 23:07:38 (older than 180 days). Very likely, a more recent version is available. Consider manually removing the file /Users/aciullo/climada/data/IBTrACS.ALL.v04r00.nc and re-running this function, which will download the most recent version of the IBTrACS data set from the official URL.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/aciullo/Documents/GitHub/climada_python/climada/hazard/tc_tracks.py:614: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n", + " if ibtracs_ds.dims['storm'] == 0:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-06 16:27:49,578 - climada.hazard.tc_tracks - INFO - Progress: 100%\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/aciullo/Documents/GitHub/climada_python/climada/hazard/tc_tracks.py:614: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n", + " if ibtracs_ds.dims['storm'] == 0:\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n" ] }, { "data": { - "image/png": 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", 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", 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"/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n" + ] + }, { "data": { - "image/png": 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n" + ] + }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -146,8 +4893,6 @@ "ax = tr_irma.plot();\n", "ax.set_title('IRMA') # set title\n", "\n", - "# other ibtracs selection options\n", - "from climada.hazard import TCTracks\n", "# years 1993 and 1994 in basin EP.\n", "# correct_pres ignores tracks with not enough data. For statistics (frequency of events), these should be considered as well\n", "sel_ibtracs = TCTracks.from_ibtracs_netcdf(provider='usa', year_range=(1993, 1994), basin='EP', correct_pres=False)\n", @@ -208,6 +4953,7 @@ "}\n", "\n", "html[theme=dark],\n", + "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", @@ -220,7 +4966,7 @@ "}\n", "\n", ".xr-wrap {\n", - " display: block;\n", + " display: block !important;\n", " min-width: 300px;\n", " max-width: 700px;\n", "}\n", @@ -437,6 +5183,11 @@ " grid-column: 4;\n", "}\n", "\n", + ".xr-index-preview {\n", + " grid-column: 2 / 5;\n", + " color: var(--xr-font-color2);\n", + "}\n", + "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", @@ -458,14 +5209,16 @@ "}\n", "\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", - ".xr-var-data-in:checked ~ .xr-var-data {\n", + ".xr-var-data-in:checked ~ .xr-var-data,\n", + ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", @@ -475,13 +5228,16 @@ "\n", ".xr-var-name span,\n", ".xr-var-data,\n", + ".xr-index-name div,\n", + ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", @@ -519,7 +5275,8 @@ "}\n", "\n", ".xr-icon-database,\n", - ".xr-icon-file-text2 {\n", + ".xr-icon-file-text2,\n", + ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", @@ -528,29 +5285,29 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
<xarray.Dataset>\n",
+       "
<xarray.Dataset> Size: 7kB\n",
        "Dimensions:                 (time: 123)\n",
        "Coordinates:\n",
-       "  * time                    (time) datetime64[ns] 2017-08-30 ... 2017-09-13T1...\n",
-       "    lat                     (time) float32 16.1 16.15 16.2 ... 36.2 36.5 36.8\n",
-       "    lon                     (time) float32 -26.9 -27.59 -28.3 ... -89.79 -90.1\n",
+       "  * time                    (time) datetime64[ns] 984B 2017-08-30 ... 2017-09...\n",
+       "    lat                     (time) float32 492B 16.1 16.15 16.2 ... 36.5 36.8\n",
+       "    lon                     (time) float32 492B -26.9 -27.59 ... -89.79 -90.1\n",
        "Data variables:\n",
-       "    time_step               (time) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0 3.0\n",
-       "    radius_max_wind         (time) float32 60.0 60.0 60.0 ... 60.0 60.0 60.0\n",
-       "    radius_oci              (time) float32 180.0 180.0 180.0 ... 350.0 350.0\n",
-       "    max_sustained_wind      (time) float32 30.0 32.0 35.0 ... 15.0 15.0 15.0\n",
-       "    central_pressure        (time) float32 1.008e+03 1.007e+03 ... 1.005e+03\n",
-       "    environmental_pressure  (time) float64 1.012e+03 1.012e+03 ... 1.008e+03\n",
-       "    basin                   (time) <U2 'NA' 'NA' 'NA' 'NA' ... 'NA' 'NA' 'NA'\n",
+       "    radius_max_wind         (time) float32 492B 60.0 60.0 60.0 ... 60.0 60.0\n",
+       "    radius_oci              (time) float32 492B 180.0 180.0 ... 350.0 350.0\n",
+       "    max_sustained_wind      (time) float32 492B 30.0 32.0 35.0 ... 15.0 15.0\n",
+       "    central_pressure        (time) float32 492B 1.008e+03 ... 1.005e+03\n",
+       "    environmental_pressure  (time) float64 984B 1.012e+03 ... 1.008e+03\n",
+       "    time_step               (time) float64 984B 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0\n",
+       "    basin                   (time) <U2 984B 'NA' 'NA' 'NA' ... 'NA' 'NA' 'NA'\n",
        "Attributes:\n",
        "    max_sustained_wind_unit:  kn\n",
        "    central_pressure_unit:    mb\n",
-       "    name:                     IRMA\n",
-       "    sid:                      2017242N16333\n",
        "    orig_event_flag:          True\n",
        "    data_provider:            ibtracs_usa\n",
-       "    id_no:                    2017242016333.0\n",
-       "    category:                 5
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2017-08-30 00:00:00', '2017-08-30 03:00:00',\n",
+       "               '2017-08-30 06:00:00', '2017-08-30 09:00:00',\n",
+       "               '2017-08-30 12:00:00', '2017-08-30 15:00:00',\n",
+       "               '2017-08-30 18:00:00', '2017-08-30 21:00:00',\n",
+       "               '2017-08-31 00:00:00', '2017-08-31 03:00:00',\n",
+       "               ...\n",
+       "               '2017-09-12 09:00:00', '2017-09-12 12:00:00',\n",
+       "               '2017-09-12 15:00:00', '2017-09-12 18:00:00',\n",
+       "               '2017-09-12 21:00:00', '2017-09-13 00:00:00',\n",
+       "               '2017-09-13 03:00:00', '2017-09-13 06:00:00',\n",
+       "               '2017-09-13 09:00:00', '2017-09-13 12:00:00'],\n",
+       "              dtype='datetime64[ns]', name='time', length=123, freq=None))
  • max_sustained_wind_unit :
    kn
    central_pressure_unit :
    mb
    orig_event_flag :
    True
    data_provider :
    ibtracs_usa
    category :
    5
    name :
    IRMA
    sid :
    2017242N16333
    id_no :
    2017242016333.0
    • " ], "text/plain": [ - "\n", + " Size: 7kB\n", "Dimensions: (time: 123)\n", "Coordinates:\n", - " * time (time) datetime64[ns] 2017-08-30 ... 2017-09-13T1...\n", - " lat (time) float32 16.1 16.15 16.2 ... 36.2 36.5 36.8\n", - " lon (time) float32 -26.9 -27.59 -28.3 ... -89.79 -90.1\n", + " * time (time) datetime64[ns] 984B 2017-08-30 ... 2017-09...\n", + " lat (time) float32 492B 16.1 16.15 16.2 ... 36.5 36.8\n", + " lon (time) float32 492B -26.9 -27.59 ... -89.79 -90.1\n", "Data variables:\n", - " time_step (time) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0 3.0\n", - " radius_max_wind (time) float32 60.0 60.0 60.0 ... 60.0 60.0 60.0\n", - " radius_oci (time) float32 180.0 180.0 180.0 ... 350.0 350.0\n", - " max_sustained_wind (time) float32 30.0 32.0 35.0 ... 15.0 15.0 15.0\n", - " central_pressure (time) float32 1.008e+03 1.007e+03 ... 1.005e+03\n", - " environmental_pressure (time) float64 1.012e+03 1.012e+03 ... 1.008e+03\n", - " basin (time) \n", - "### b) Generate probabilistic events\n", + "## b) Generate probabilistic events\n", "\n", "Once tracks are present in `TCTracks`, one can generate synthetic tracks for each present track based on directed random walk. Note that the tracks should be interpolated to use the same timestep **before** generation of probabilistic events.\n", "\n", @@ -808,25 +5576,42 @@ }, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-06 16:36:46,739 - climada.hazard.tc_tracks - INFO - Interpolating 1 tracks to 1h time steps.\n", + "2024-08-06 16:36:47,644 - climada.hazard.tc_tracks_synth - INFO - Computing 5 synthetic tracks.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/aciullo/Documents/GitHub/climada_python/climada/hazard/tc_tracks.py:1511: NumbaWarning: \u001b[1m\n", + "Compilation is falling back to object mode WITHOUT looplifting enabled because Function \"_one_interp_data\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n", + "\u001b[0m\u001b[1mDuring: typing of argument at /Users/aciullo/Documents/GitHub/climada_python/climada/hazard/tc_tracks.py (1545)\u001b[0m\n", + "\u001b[1m\n", + "File \"climada/hazard/tc_tracks.py\", line 1545:\u001b[0m\n", + "\u001b[1m def _one_interp_data(track, time_step_h, land_geom=None):\n", + " \n", + "\n", + "\u001b[1m time_step = pd.tseries.frequencies.to_offset(pd.Timedelta(hours=time_step_h)).freqstr\n", + "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n", + "\u001b[0m\n", + " @staticmethod\n", + ":6: FutureWarning: 'H' is deprecated and will be removed in a future version. Please use 'h' instead of 'H'.\n", + "/Users/aciullo/opt/anaconda3/envs/climada_env/lib/python3.11/site-packages/shapely/set_operations.py:133: RuntimeWarning: invalid value encountered in intersection\n", + " return lib.intersection(a, b, **kwargs)\n" + ] }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
    " + "
    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -883,6 +5668,7 @@ "}\n", "\n", "html[theme=dark],\n", + "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", @@ -895,7 +5681,7 @@ "}\n", "\n", ".xr-wrap {\n", - " display: block;\n", + " display: block !important;\n", " min-width: 300px;\n", " max-width: 700px;\n", "}\n", @@ -1112,6 +5898,11 @@ " grid-column: 4;\n", "}\n", "\n", + ".xr-index-preview {\n", + " grid-column: 2 / 5;\n", + " color: var(--xr-font-color2);\n", + "}\n", + "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", @@ -1133,14 +5924,16 @@ "}\n", "\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", - ".xr-var-data-in:checked ~ .xr-var-data {\n", + ".xr-var-data-in:checked ~ .xr-var-data,\n", + ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", @@ -1150,13 +5943,16 @@ "\n", ".xr-var-name span,\n", ".xr-var-data,\n", + ".xr-index-name div,\n", + ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", @@ -1194,7 +5990,8 @@ "}\n", "\n", ".xr-icon-database,\n", - ".xr-icon-file-text2 {\n", + ".xr-icon-file-text2,\n", + ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", @@ -1203,34 +6000,34 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
    <xarray.Dataset>\n",
+       "
    <xarray.Dataset> Size: 31kB\n",
        "Dimensions:                 (time: 349)\n",
        "Coordinates:\n",
-       "  * time                    (time) datetime64[ns] 2017-08-30 ... 2017-09-13T1...\n",
-       "    lon                     (time) float64 -27.64 -27.8 -27.96 ... -97.81 -97.93\n",
-       "    lat                     (time) float64 15.39 15.41 15.42 ... 27.41 27.49\n",
+       "  * time                    (time) datetime64[ns] 3kB 2017-08-30 ... 2017-09-...\n",
+       "    lon                     (time) float64 3kB -27.64 -27.8 ... -97.81 -97.93\n",
+       "    lat                     (time) float64 3kB 15.39 15.41 15.42 ... 27.41 27.49\n",
        "Data variables:\n",
-       "    time_step               (time) float64 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0\n",
-       "    radius_max_wind         (time) float64 60.0 60.0 60.0 ... 60.0 60.0 60.0\n",
-       "    radius_oci              (time) float64 180.0 180.0 180.0 ... 350.0 350.0\n",
-       "    max_sustained_wind      (time) float64 30.0 30.67 31.33 ... 15.0 14.99 14.96\n",
-       "    central_pressure        (time) float64 1.008e+03 1.008e+03 ... 1.005e+03\n",
-       "    environmental_pressure  (time) float64 1.012e+03 1.012e+03 ... 1.008e+03\n",
-       "    basin                   (time) <U2 'NA' 'NA' 'NA' 'NA' ... 'NA' 'NA' 'NA'\n",
-       "    on_land                 (time) bool False False False ... False True True\n",
-       "    dist_since_lf           (time) float64 nan nan nan nan ... nan 7.605 22.71\n",
+       "    radius_max_wind         (time) float64 3kB 60.0 60.0 60.0 ... 60.0 60.0 60.0\n",
+       "    radius_oci              (time) float64 3kB 180.0 180.0 180.0 ... 350.0 350.0\n",
+       "    max_sustained_wind      (time) float64 3kB 30.0 30.67 31.33 ... 14.99 14.96\n",
+       "    central_pressure        (time) float64 3kB 1.008e+03 1.008e+03 ... 1.005e+03\n",
+       "    environmental_pressure  (time) float64 3kB 1.012e+03 1.012e+03 ... 1.008e+03\n",
+       "    time_step               (time) float64 3kB 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0\n",
+       "    basin                   (time) <U2 3kB 'NA' 'NA' 'NA' ... 'NA' 'NA' 'NA'\n",
+       "    on_land                 (time) bool 349B False False False ... True True\n",
+       "    dist_since_lf           (time) float64 3kB nan nan nan ... nan 7.605 22.71\n",
        "Attributes:\n",
        "    max_sustained_wind_unit:  kn\n",
        "    central_pressure_unit:    mb\n",
-       "    name:                     IRMA_gen5\n",
-       "    sid:                      2017242N16333_gen5\n",
        "    orig_event_flag:          False\n",
        "    data_provider:            ibtracs_usa\n",
-       "    id_no:                    2017242016333.05\n",
-       "    category:                 5
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2017-08-30 00:00:00', '2017-08-30 01:00:00',\n",
+       "               '2017-08-30 02:00:00', '2017-08-30 03:00:00',\n",
+       "               '2017-08-30 04:00:00', '2017-08-30 05:00:00',\n",
+       "               '2017-08-30 06:00:00', '2017-08-30 07:00:00',\n",
+       "               '2017-08-30 08:00:00', '2017-08-30 09:00:00',\n",
+       "               ...\n",
+       "               '2017-09-13 03:00:00', '2017-09-13 04:00:00',\n",
+       "               '2017-09-13 05:00:00', '2017-09-13 06:00:00',\n",
+       "               '2017-09-13 07:00:00', '2017-09-13 08:00:00',\n",
+       "               '2017-09-13 09:00:00', '2017-09-13 10:00:00',\n",
+       "               '2017-09-13 11:00:00', '2017-09-13 12:00:00'],\n",
+       "              dtype='datetime64[ns]', name='time', length=349, freq=None))
  • max_sustained_wind_unit :
    kn
    central_pressure_unit :
    mb
    orig_event_flag :
    False
    data_provider :
    ibtracs_usa
    category :
    5
    name :
    IRMA_gen5
    sid :
    2017242N16333_gen5
    id_no :
    2017242016333.05
    • " ], "text/plain": [ - "\n", + " Size: 31kB\n", "Dimensions: (time: 349)\n", "Coordinates:\n", - " * time (time) datetime64[ns] 2017-08-30 ... 2017-09-13T1...\n", - " lon (time) float64 -27.64 -27.8 -27.96 ... -97.81 -97.93\n", - " lat (time) float64 15.39 15.41 15.42 ... 27.41 27.49\n", + " * time (time) datetime64[ns] 3kB 2017-08-30 ... 2017-09-...\n", + " lon (time) float64 3kB -27.64 -27.8 ... -97.81 -97.93\n", + " lat (time) float64 3kB 15.39 15.41 15.42 ... 27.41 27.49\n", "Data variables:\n", - " time_step (time) float64 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0\n", - " radius_max_wind (time) float64 60.0 60.0 60.0 ... 60.0 60.0 60.0\n", - " radius_oci (time) float64 180.0 180.0 180.0 ... 350.0 350.0\n", - " max_sustained_wind (time) float64 30.0 30.67 31.33 ... 15.0 14.99 14.96\n", - " central_pressure (time) float64 1.008e+03 1.008e+03 ... 1.005e+03\n", - " environmental_pressure (time) float64 1.012e+03 1.012e+03 ... 1.008e+03\n", - " basin (time) \n", + "Daily max sustained wind: Size: 120B\n", "array([100. , 100. , 100. , 123.33333333,\n", " 155. , 155. , 150. , 138. ,\n", " 51.85384486, 58.03963987, 29.03963987, 3.57342356,\n", " 3.35512013, 54. , 99. ])\n", "Coordinates:\n", - " * day (day) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 30 31\n" + " * day (day) int64 120B 1 2 3 4 5 6 7 8 9 10 11 12 13 30 31\n" ] } ], @@ -1758,7 +6566,7 @@ "metadata": {}, "source": [ " \n", - "### c) ECMWF Forecast Tracks\n", + "## c) ECMWF Forecast Tracks\n", "\n", "ECMWF publishes tropical cyclone forecast tracks free of charge as part of the [WMO essentials](https://www.ecmwf.int/en/forecasts/datasets/wmo-essential#Essential_Tropical). These tracks are detected automatically in the ENS and HRES models. The non-supervised nature of the model may lead to artefacts.\n", "\n", @@ -1791,7 +6599,7 @@ "metadata": {}, "source": [ " \n", - "### d) Load TC tracks from other sources\n", + "## d) Load TC tracks from other sources\n", "\n", "In addition to the [historical records of TCs (IBTrACS)](#Part1.a), the [probabilistic extension](#Part1.b) of these tracks, and the [ECMWF Forecast tracks](#Part1.c), CLIMADA also features functions to read in synthetic TC tracks from other sources. These include synthetic storm tracks from Kerry Emanuel's coupled statistical-dynamical model (Emanuel et al., 2006 as used in Geiger et al., 2016), synthetic storm tracks from a second coupled statistical-dynamical model (CHAZ) (as described in Lee et al., 2018), and synthetic storm tracks from a fully statistical model (STORM) Bloemendaal et al., 2020). However, these functions are partly under development and/or targeted at advanced users of CLIMADA in the context of very specific use cases. They are thus not covered in this tutorial." ] @@ -1820,12 +6628,12 @@ "metadata": {}, "source": [ " \n", - "### a) Default hazard generation for tropical cyclones" + "## a) Default hazard generation for tropical cyclones" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2021-01-14T08:56:56.801886Z", @@ -1834,49 +6642,47 @@ }, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-06 16:37:45,734 - climada.util.coordinates - INFO - Sampling from /Users/aciullo/climada/data/GMT_intermediate_coast_distance_01d.tif\n", + "2024-08-06 16:37:45,787 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Mapping 6 tracks to 3822 coastal centroids.\n", + "2024-08-06 16:37:45,941 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 16%\n", + "2024-08-06 16:37:46,083 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 33%\n", + "2024-08-06 16:37:46,172 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 50%\n", + "2024-08-06 16:37:46,309 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 66%\n", + "2024-08-06 16:37:46,442 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 83%\n", + "2024-08-06 16:37:46,457 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 100%\n" + ] }, { "data": { - "image/png": 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", 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", 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", 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", 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", 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", "text/plain": [ - "
    " + "
    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1886,7 +6692,6 @@ "# construct centroids\n", "min_lat, max_lat, min_lon, max_lon = 16.99375, 21.95625, -72.48125, -61.66875\n", "cent = Centroids.from_pnt_bounds((min_lon, min_lat, max_lon, max_lat), res=0.12)\n", - "cent.check()\n", "cent.plot();\n", "\n", "# construct tropical cyclones\n", @@ -1903,14 +6708,14 @@ "metadata": {}, "source": [ " \n", - "### b) Implementing climate change\n", + "## b) Implementing climate change\n", "\n", - "`apply_climate_scenario_knu` implements the changes on intensity and frequency due to climate change described in *Global projections of intense tropical cyclone activity for the late twenty-first century from dynamical downscaling of CMIP5/RCP4.5 scenarios* of Knutson et al 2015. Other RCP scenarios are approximated from the RCP 4.5 values by interpolating them according to their relative radiative forcing." + "`apply_climate_scenario_knu` implements the changes in frequency due to climate change described in Knutson et al (2020) and Jewson et al. (2021). This requires to pass the rcp scenario of interest, the projection's future reference year, the projection's percentile of interest and the historical baseline period. For simplicity we keep these latter two as default values only spefify the rcp (45) and the future reference year (2055)." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2021-01-14T08:56:59.107089Z", @@ -1919,40 +6724,39 @@ }, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-06 16:40:25,994 - climada.util.coordinates - INFO - Sampling from /Users/aciullo/climada/data/GMT_intermediate_coast_distance_01d.tif\n", + "2024-08-06 16:40:26,030 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Mapping 6 tracks to 3822 coastal centroids.\n", + "2024-08-06 16:40:26,181 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 16%\n", + "2024-08-06 16:40:26,323 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 33%\n", + "2024-08-06 16:40:26,412 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 50%\n", + "2024-08-06 16:40:26,544 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 66%\n", + "2024-08-06 16:40:26,681 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 83%\n", + "2024-08-06 16:40:26,697 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 100%\n", + "\n", + "A TC like Irma would undergo a frequency increase of about 5.0 % in 2055 under RCP 45\n" + ] } ], "source": [ "# an Irma event-like in 2055 under RCP 4.5:\n", "tc_irma = TropCyclone.from_tracks(tr_irma, centroids=cent)\n", - "tc_irma_cc = tc_irma.apply_climate_scenario_knu(ref_year=2055, rcp_scenario=45)\n", - "tc_irma_cc.plot_intensity('2017242N16333');" + "tc_irma_cc = tc_irma.apply_climate_scenario_knu(target_year=2055, scenario='4.5')\n", + "\n", + "rel_freq_incr = np.round(\n", + " (np.mean(tc_irma_cc.frequency) - np.mean(tc_irma.frequency)\n", + " ) / np.mean(tc_irma.frequency)*100, 0)\n", + "\n", + "print(f\"\\nA TC like Irma would undergo a frequency increase of about {rel_freq_incr} % in 2055 under RCP 45\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "**Note:** this method to implement climate change is simplified and does only take into account changes in TC frequency and intensity. However, how hurricane damage changes with climate remains challenging to assess. Records of hurricane damage exhibit widely fluctuating values because they depend on rare, landfalling events which are substantially more volatile than the underlying basin-wide TC characteristics. For more accurate future projections of how a warming climate might shape TC characteristics, there is a two-step process needed. First, the understanding of how climate change affects critical environmental factors (like SST, humidity, etc.) that shape TCs is required. Second, the means of simulating how these changes impact TC characteristics (such as intensity, frequency, etc.) are necessary. Statistical-dynamical models (Emanuel et al., 2006 and Lee et al., 2018) are physics-based and allow for such climate change studies. However, this goes beyond the scope of this tutorial." + "**Note:** this method to implement climate change is simplified and does only take into account changes in TC frequency. However, how hurricane damage changes with climate remains challenging to assess. Records of hurricane damage exhibit widely fluctuating values because they depend on rare, landfalling events which are substantially more volatile than the underlying basin-wide TC characteristics. For more accurate future projections of how a warming climate might shape TC characteristics, there is a two-step process needed. First, the understanding of how climate change affects critical environmental factors (like SST, humidity, etc.) that shape TCs is required. Second, the means of simulating how these changes impact TC characteristics (such as intensity, frequency, etc.) are necessary. Statistical-dynamical models (Emanuel et al., 2006 and Lee et al., 2018) are physics-based and allow for such climate change studies. However, this goes beyond the scope of this tutorial." ] }, { @@ -1960,7 +6764,7 @@ "metadata": {}, "source": [ " \n", - "### c) Multiprocessing - improving performance for big computations\n", + "## c) Multiprocessing - improving performance for big computations\n", "\n", "Multiprocessing is part of the tropical cyclone module. Simply provide a process pool as method argument.
    \n", "Below is an example of how large amounts of data could be processed.\n", @@ -1999,7 +6803,7 @@ "metadata": {}, "source": [ " \n", - "### d) Making videos\n", + "## d) Making videos\n", "\n", "Videos of a tropical cyclone hitting specific centroids can be created with the method `video_intensity()`.\n", "\n", @@ -2168,10 +6972,17 @@ "\n", "- Geiger, T., Frieler, K., & Levermann, A. (2016). High-income does not protect against hurricane losses. Environmental Research Letters, 11(8). https://doi.org/10.1088/1748-9326/11/8/084012\n", "\n", - "- Knutson, T. R., Sirutis, J. J., Zhao, M., Tuleya, R. E., Bender, M., Vecchi, G. A., … Chavas, D. (2015). Global projections of intense tropical cyclone activity for the late twenty-first century from dynamical downscaling of CMIP5/RCP4.5 scenarios. Journal of Climate, 28(18), 7203–7224. https://doi.org/10.1175/JCLI-D-15-0129.1\n", + "- Knutson, T., Camargo, S. J., Chan, J. C. L., Emanuel, K., Ho, C.-H., Kossin, J., Mohapatra, M., Satoh, M., Sugi, M., Walsh, K., & Wu, L. (2020). Tropical Cyclones and Climate Change Assessment: Part II: Projected Response to Anthropogenic Warming. Bulletin of the American Meteorological Society, 101(3), Article 3. https://doi.org/10.1175/BAMS-D-18-0194.1\n", + "\n", + "- Jewson, S. (2021). Conversion of the Knutson et al. Tropical Cyclone Climate Change Projections to Risk Model Baselines. Journal of Applied Meteorology and Climatology, 60(11), 1517–1530. https://doi.org/10.1175/JAMC-D-21-0102.1\n", "\n", "- Lee, C. Y., Tippett, M. K., Sobel, A. H., & Camargo, S. J. (2018). An environmentally forced tropical cyclone hazard model. Journal of Advances in Modeling Earth Systems, 10(1), 223–241. https://doi.org/10.1002/2017MS001186" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { @@ -2190,7 +7001,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.11.6" }, "toc": { "base_numbering": 1, diff --git a/requirements/env_climada.yml b/requirements/env_climada.yml index 52baaa8fd..9ebc16c2b 100644 --- a/requirements/env_climada.yml +++ b/requirements/env_climada.yml @@ -7,7 +7,7 @@ dependencies: - cartopy>=0.23 - cfgrib>=0.9.9,<0.9.10 # 0.9.10 cannot read the icon_grib files from https://opendata.dwd.de - contextily>=1.6 - - dask>=2024.2,<2024.3 # 2024.3 apparently instroduced a sophisticated locking mechanism which leads to read-only exceptions in several places + - dask>=2024.5 - eccodes>=2.27,<2.28 # 2.28 changed some labels, in particular: gust -> i20fg - gdal>=3.6 - geopandas>=0.14 diff --git a/setup.py b/setup.py index 99652b0fc..867f001f9 100644 --- a/setup.py +++ b/setup.py @@ -32,9 +32,7 @@ setup( name='climada', - - version='5.0.0', - + version='5.0.1-dev', description='CLIMADA in Python', long_description=long_description,