From be40e48fc81cf42109a4c368cd5edd3da34eeee9 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Wed, 13 Sep 2023 11:01:04 -0600 Subject: [PATCH 01/10] requirements updated --- pvdeg/__init__.py | 2 +- requirements.txt | 6 +++++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/pvdeg/__init__.py b/pvdeg/__init__.py index db52c643..710a4aa7 100644 --- a/pvdeg/__init__.py +++ b/pvdeg/__init__.py @@ -2,7 +2,7 @@ from .config import * -from . import cli +#from . import cli from . import collection from . import degradation from . import design diff --git a/requirements.txt b/requirements.txt index 876d58a2..15e3cabc 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,4 +12,8 @@ matplotlib jupyterlab notebook NREL-rex - +pytables +xarray +netCDF4 +h5py +h5netcdf From 8e284c6c6799a0738740f9e9bbcc1468b36e9f6b Mon Sep 17 00:00:00 2001 From: martin-springer Date: Sun, 24 Sep 2023 20:47:39 -0600 Subject: [PATCH 02/10] added geospatial analysis via dask --- pvdeg/__init__.py | 1 + pvdeg/geospatial.py | 266 ++++++ pvdeg/humidity.py | 277 +++--- pvdeg/standards.py | 271 +++--- pvdeg/utilities.py | 22 +- pvdeg/weather.py | 290 ++++-- .../tutorials/6 - Geospatial Analysis.ipynb | 878 ++++++++++++++++++ 7 files changed, 1667 insertions(+), 338 deletions(-) create mode 100644 pvdeg/geospatial.py create mode 100644 pvdeg_tutorials/tutorials/6 - Geospatial Analysis.ipynb diff --git a/pvdeg/__init__.py b/pvdeg/__init__.py index 710a4aa7..66791c0e 100644 --- a/pvdeg/__init__.py +++ b/pvdeg/__init__.py @@ -7,6 +7,7 @@ from . import degradation from . import design from . import fatigue +from . import geospatial from . import humidity from . import letid from .scenario import Scenario diff --git a/pvdeg/geospatial.py b/pvdeg/geospatial.py new file mode 100644 index 00000000..d9d68395 --- /dev/null +++ b/pvdeg/geospatial.py @@ -0,0 +1,266 @@ +""" +Collection of classes and functions for geospatial analysis. +""" + +from . import standards +from . import humidity + +import xarray as xr +import dask.array as da +import pandas as pd +from dask.distributed import Client, LocalCluster + + +def start_dask(hpc=None): + """ + Starts a dask cluster for parallel processing. + + Parameters + ---------- + hpc : dict + Dictionary containing dask hpc settings (see examples below). + + Examples + -------- + Local cluster: + + .. code-block:: python + + hpc = {'manager': 'local', + 'n_workers': 1, + 'threads_per_worker': 8, + 'memory_limit': '10GB'} + + SLURM cluster: + + .. code-block:: python + + hpc = {'manager': 'slurm', + 'n_jobs': 1, # Max number of nodes used for parallel processing + 'cores': 36, + 'memory': '96GB', + 'queue': 'debug', + 'account': 'pvsoiling', + 'walltime': '01:00:00', + 'interface': 'ib0', + 'processes': 18, + 'local_directory': '/tmp/scratch', + 'shared_temp_directory': '/scratch/mspringe', + 'job_extra_directives': ['-o ./logs/slurm-%j.out'], + 'silence_logs': 'error', + 'death_timeout': '60',} + + Returns + ------- + client : dask.distributed.Client + Dask client object. + """ + if hpc is None: + cluster = LocalCluster() + else: + manager = hpc.pop('manager') + + if manager == 'local': + cluster = LocalCluster(**hpc) + elif manager == 'slurm': + from dask_jobqueue import SLURMCluster + n_jobs = hpc.pop('n_jobs') + cluster = SLURMCluster(**hpc) + cluster.scale(jobs=n_jobs) + + client = Client(cluster) + print('Dashboard:', client.dashboard_link) + client.wait_for_workers(n_workers=1) + + return client + + +def calc_gid(ds_gid, meta_gid, func, **kwargs): + """ + Calculates a single gid for a given function. + + Parameters + ---------- + ds_gid : xarray.Dataset + Dataset containing weather data for a single gid. + meta_gid : dict + Dictionary containing meta data for a single gid. + func : function + Function to apply to weather data. + kwargs : dict + Keyword arguments to pass to func. + + Returns + ------- + ds_res : xarray.Dataset + Dataset with results for a single gid. + """ + + df_weather = ds_gid.to_dataframe() + + df_res = func(weather_df=df_weather, meta=meta_gid, **kwargs) + ds_res = xr.Dataset.from_dataframe(df_res) + + if not df_res.index.name: + ds_res = ds_res.isel(index=0, drop=True) + + return ds_res + + +def calc_block(weather_ds_block, future_meta_df, func, func_kwargs): + """ + Calculates a block of gids for a given function. + + Parameters + ---------- + weather_ds_block : xarray.Dataset + Dataset containing weather data for a block of gids. + future_meta_df : pandas.DataFrame + DataFrame containing meta data for a block of gids. + func : function + Function to apply to weather data. + func_kwargs : dict + Keyword arguments to pass to func. + + Returns + ------- + ds_res : xarray.Dataset + Dataset with results for a block of gids. + """ + + res = weather_ds_block.groupby('gid').map(lambda ds_gid: calc_gid( + ds_gid=ds_gid, + meta_gid=future_meta_df.loc[ds_gid['gid'].values].to_dict(), + func=func, + **func_kwargs)) + return res + + +def analysis(weather_ds, meta_df, func, template=None, **func_kwargs): + """ + Applies a function to each gid of a weather dataset. + + Parameters + ---------- + weather_ds : xarray.Dataset + Dataset containing weather data for a block of gids. + meta_df : pandas.DataFrame + DataFrame containing meta data for a block of gids. + func : function + Function to apply to weather data. + template : xarray.Dataset + Template for output data. + func_kwargs : dict + Keyword arguments to pass to func. + + Returns + ------- + ds_res : xarray.Dataset + Dataset with results for a block of gids. + """ + + if template is None: + param = template_parameters(func) + template = output_template(weather_ds, **param) + + #future_meta_df = client.scatter(meta_df) + kwargs = {'func': func, + 'future_meta_df': meta_df, + 'func_kwargs': func_kwargs} + + stacked = weather_ds.map_blocks(calc_block, kwargs=kwargs, template=template).compute() + + lats = stacked.latitude.values.flatten() + lons = stacked.longitude.values.flatten() + #stacked = stacked.drop(['gid']) + stacked = stacked.drop_vars(['latitude', 'longitude']) + stacked.coords['gid'] = pd.MultiIndex.from_arrays([lats, lons], names=['latitude', 'longitude']) + + res = stacked.unstack('gid', sparse=True) + + return res + + +def output_template(ds_gids, shapes, attrs=dict(), add_dims=dict()): + """ + Generates a xarray template for output data. Output variables and + associated dimensions need to be specified via the shapes dictionary. + The dimension length are derived from the input data. Additonal output + dimensions can be defined with the add_dims argument. + + Parameters + ---------- + ds_gids : xarray.Dataset + Dataset containing the gids and their associated dimensions. + shapes : dict + Dictionary of variable names and their associated dimensions. + attr : dict + Dictionary of attributes for each variable (e.g. units). + add_dims : dict + Dictionary of dimensions to add to the output template. + + Returns + ------- + output_template : xarray.Dataset + Template for output data. + """ + dims = set([d for dim in shapes.values() for d in dim]) + dims_size = dict(ds_gids.dims) | add_dims + + output_template = xr.Dataset( + data_vars = {var: (dim, da.empty([dims_size[d] for d in dim]) + ) for var, dim in shapes.items()}, + coords = {'gid': ds_gids['gid']}, + attrs = attrs + ).chunk({dim: ds_gids.chunks[dim] for dim in dims}) + + return output_template + + +def template_parameters(func): + """ + Output parameters for xarray template. + + Returns + ------- + shapes : dict + Dictionary of variable names and their associated dimensions. + attrs : dict + Dictionary of attributes for each variable (e.g. units). + """ + + if func == standards.standoff: + + shapes = {'x': ('gid',), + 'T98_inf': ('gid',), + 'T98_0': ('gid',), + 'latitude': ('gid',), + 'longitude':('gid',), + } + + attrs = {'x' : {'units': 'cm'}, + 'T98_0' : {'units': 'Celsius'}, + 'T98_inf' : {'units': 'Celsius'}} + + add_dims = {} + + elif func == humidity.module: + + shapes = {'RH_surface_outside': ('gid', 'time'), + 'RH_front_encap': ('gid', 'time'), + 'RH_back_encap': ('gid', 'time'), + 'RH_backsheet': ('gid', 'time'), + } + + attrs = {} + + add_dims = {} + + else: + raise ValueError(f"No preset output template for function {func}.") + + parameters = {'shapes': shapes, + 'attrs': attrs, + 'add_dims': add_dims} + + return parameters \ No newline at end of file diff --git a/pvdeg/humidity.py b/pvdeg/humidity.py index 1e902bf3..9f973ef5 100644 --- a/pvdeg/humidity.py +++ b/pvdeg/humidity.py @@ -31,7 +31,7 @@ def _ambient(weather_df): weather_df : pd.DataFrame Datetime-indexed weather dataframe which contains (at minimum) Ambient temperature ('temp_air') and dew point ('Dew Point') in units [C] - + Returns: -------- weather_df : pd.DataFrame @@ -40,13 +40,13 @@ def _ambient(weather_df): ''' temp_air = weather_df['temp_air'] dew_point = weather_df['Dew Point'] - + num = np.exp( 17.625*dew_point / (243.04 + dew_point) ) den = np.exp( 17.625*temp_air / (243.04 + temp_air) ) rh_ambient = 100 * num / den - + weather_df['relative_humidity'] = rh_ambient - + return weather_df #TODO: When is dew_yield used? @@ -155,7 +155,7 @@ def _diffusivity_numerator(rh_ambient, temp_ambient, temp_module, So=1.81390702, """ Calculation is used in determining a weighted average Relative Humidity of the outside surface of a module. This funciton is used exclusively in the function _diffusivity_weighted_water and could be combined. - + The function returns values needed for the numerator of the Diffusivity weighted water content equation. This function will return a pandas series prior to summation of the numerator @@ -201,7 +201,7 @@ def _diffusivity_denominator(temp_module, Ead=38.14): """ Calculation is used in determining a weighted average Relative Humidity of the outside surface of a module. This funciton is used exclusively in the function _diffusivity_weighted_water and could be combined. - + The function returns values needed for the denominator of the Diffusivity weighted water content equation(diffuse_water). This function will return a pandas series prior to summation of the denominator @@ -225,14 +225,14 @@ def _diffusivity_denominator(temp_module, Ead=38.14): (0.00831446261815324 * (temp_module + 273.15)))) return diff_denominator - + def _diffusivity_weighted_water(rh_ambient, temp_ambient, temp_module, So=1.81390702, Eas=16.729, Ead=38.14): """ Calculation is used in determining a weighted average water content at the surface of a module. It is used as a constant water content that is equivalent to the time varying one with respect to moisture ingress. - The function calculates the Diffusivity weighted water content. + The function calculates the Diffusivity weighted water content. Parameters ---------- @@ -282,7 +282,7 @@ def front_encap(rh_ambient, temp_ambient, temp_module, So=1.81390702, Eas=16.729 rh_ambient : series (float) ambient Relative Humidity [%] temp_ambient : series (float) - ambient outdoor temperature [°C] + ambient outdoor temperature [°C] temp_module : pandas series (float) The surface temperature in Celsius of the solar panel module "module temperature [°C]" @@ -439,7 +439,7 @@ def back_encap(rh_ambient, temp_ambient, temp_module, rh_back_encap() Function to calculate the Relative Humidity of Backside Solar Module Encapsulant - and return a pandas series for each time step + and return a pandas series for each time step Parameters ----------- @@ -469,7 +469,7 @@ def back_encap(rh_ambient, temp_ambient, temp_module, Eas = 16.729[kJ/mol] is the suggested value for EVA. Returns - -------- + -------- RHback_series : pandas series (float) Relative Humidity of Backside Solar Module Encapsulant [%] @@ -497,7 +497,7 @@ def back_encap(rh_ambient, temp_ambient, temp_module, l=l, Eas=Eas) - #RHback_series = 100 * (Ce_nparray / (So * np.exp(-( (Eas) / + #RHback_series = 100 * (Ce_nparray / (So * np.exp(-( (Eas) / # (0.00831446261815324 * (temp_module + 273.15)) )) )) RHback_series = 100 * (Ce_nparray / Csat) @@ -518,7 +518,7 @@ def backsheet_from_encap(rh_back_encap, rh_surface_outside): Returns -------- RHbacksheet_series : pandas series (float) - Relative Humidity of Backside Backsheet of a Solar Module [%] + Relative Humidity of Backside Backsheet of a Solar Module [%] """ RHbacksheet_series = (rh_back_encap + rh_surface_outside)/2 @@ -562,14 +562,14 @@ def backsheet(rh_ambient, temp_ambient, temp_module, relative humidity of the PV backsheet as a time-series [%] """ - back_encap = back_encap(rh_ambient=rh_ambient, + RHback_series = back_encap(rh_ambient=rh_ambient, temp_ambient=temp_ambient, temp_module=temp_module, WVTRo=WVTRo, EaWVTR=EaWVTR, So=So, l=l, Eas=Eas) surface = surface_outside(rh_ambient=rh_ambient, temp_ambient=temp_ambient, temp_module=temp_module) - backsheet = (back_encap + surface)/2 + backsheet = (RHback_series + surface)/2 return backsheet def module( @@ -580,10 +580,10 @@ def module( sky_model='isotropic', temp_model='sapm', mount_type='open_rack_glass_glass', - WVTRo=7970633554, - EaWVTR=55.0255, - So=1.81390702, - l=0.5, + WVTRo=7970633554, + EaWVTR=55.0255, + So=1.81390702, + l=0.5, Eas=16.729, wind_speed_factor=1): """Calculate the Relative Humidity of solar module backsheet from timeseries data. @@ -603,9 +603,9 @@ def module( temp_model : str, optional Options: 'sapm', 'pvsyst', 'faiman', 'sandia'. mount_type : str, optional - Options: 'insulated_back_glass_polymer', + Options: 'insulated_back_glass_polymer', 'open_rack_glass_polymer' - 'close_mount_glass_glass', + 'close_mount_glass_glass', 'open_rack_glass_glass' WVTRo : float Water Vapor Transfer Rate prefactor (g/m2/day). @@ -624,13 +624,13 @@ def module( Encapsulant solubility activation energy in [kJ/mol] Eas = 16.729(kJ/mol) is the suggested value for EVA. wind_speed_factor : float, optional - Wind speed correction factor to account for different wind speed measurement heights + Wind speed correction factor to account for different wind speed measurement heights between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM) Returns -------- rh_backsheet : float series or array - relative humidity of the PV backsheet as a time-series + relative humidity of the PV backsheet as a time-series """ #solar_position = spectral.solar_position(weather_df, meta) @@ -638,31 +638,31 @@ def module( #temp_module = temperature.module(weather_df, poa, temp_model, mount_type, wind_speed_factor) poa = spectral.poa_irradiance(weather_df=weather_df, meta=meta, - tilt=tilt, azimuth=azimuth, sky_model=sky_model) - + tilt=tilt, azimuth=azimuth, sky_model=sky_model) + temp_module = temperature.module(weather_df, meta, poa=poa, temp_model=temp_model, conf=mount_type, wind_speed_factor=wind_speed_factor) - + rh_surface_outside = surface_outside( rh_ambient=weather_df['relative_humidity'], temp_ambient=weather_df['temp_air'], temp_module=temp_module) rh_front_encap = front_encap( - rh_ambient=weather_df['relative_humidity'], - temp_ambient=weather_df['temp_air'], + rh_ambient=weather_df['relative_humidity'], + temp_ambient=weather_df['temp_air'], temp_module=temp_module, - So=So, + So=So, Eas=Eas) rh_back_encap = back_encap( rh_ambient=weather_df['relative_humidity'], - temp_ambient=weather_df['temp_air'], - temp_module=temp_module, + temp_ambient=weather_df['temp_air'], + temp_module=temp_module, WVTRo=WVTRo, - EaWVTR=EaWVTR, - So=So, - l=l, + EaWVTR=EaWVTR, + So=So, + l=l, Eas=Eas) rh_backsheet = backsheet_from_encap( @@ -677,106 +677,109 @@ def module( return results -def run_module( - project_points, - out_dir, - tag, - weather_db, - weather_satellite, - weather_names, - max_workers=None, - tilt=None, - azimuth=180, - sky_model='isotropic', - temp_model='sapm', - mount_type='open_rack_glass_glass', - WVTRo=7970633554, - EaWVTR=55.0255, - So=1.81390702, - l=0.5, - Eas=16.729, - wind_speed_factor=1 -): - - """Run the relative humidity calculation for a set of project points.""" - - #inputs - weather_arg = {} - weather_arg['satellite'] = weather_satellite - weather_arg['names'] = weather_names - weather_arg['NREL_HPC'] = True #TODO: add argument or auto detect - weather_arg['attributes'] = [ - 'temp_air', - 'wind_speed', - 'dhi', 'ghi', - 'dni','relative_humidity' - ] - - #TODO: is there a better way to add the meta data? - nsrdb_fnames, hsds = weather.get_NSRDB_fnames( - weather_arg['satellite'], - weather_arg['names'], - weather_arg['NREL_HPC']) - - with NSRDBX(nsrdb_fnames[0], hsds=hsds) as f: - meta = f.meta[f.meta.index.isin(project_points.gids)] - ti = f.time_index - - all_fields = ['RH_surface_outside', - 'RH_front_encap', - 'RH_back_encap', - 'RH_backsheet'] - - out_fp = Path(out_dir) / f"out_rel_humidity{tag}.h5" - shapes = {n : (len(ti), len(project_points)) for n in all_fields} - attrs = {n : {'units': '%'} for n in all_fields} - chunks = {n : None for n in all_fields} - dtypes = {n : "float32" for n in all_fields} - - Outputs.init_h5( - out_fp, - all_fields, - shapes, - attrs, - chunks, - dtypes, - meta=meta.reset_index(), - time_index=ti - ) - - future_to_point = {} - with ProcessPoolExecutor(max_workers=max_workers) as executor: - for point in project_points: - gid = int(point.gid) - weather_df, meta = weather.load( - database = weather_db, - id = gid, - **weather_arg) - future = executor.submit( - module, - weather_df, - meta, - tilt, - azimuth, - sky_model, - temp_model, - mount_type, - WVTRo, - EaWVTR, - So, - l, - Eas, - wind_speed_factor - ) - future_to_point[future] = gid - - with Outputs(out_fp, mode="a") as out: - for future in as_completed(future_to_point): - result = future.result() - gid = future_to_point.pop(future) - - ind = project_points.index(gid) - for dset, data in result.items(): - out[dset, :, ind] = data.values - - return out_fp.as_posix() \ No newline at end of file + + + +# def run_module( +# project_points, +# out_dir, +# tag, +# weather_db, +# weather_satellite, +# weather_names, +# max_workers=None, +# tilt=None, +# azimuth=180, +# sky_model='isotropic', +# temp_model='sapm', +# mount_type='open_rack_glass_glass', +# WVTRo=7970633554, +# EaWVTR=55.0255, +# So=1.81390702, +# l=0.5, +# Eas=16.729, +# wind_speed_factor=1 +# ): + +# """Run the relative humidity calculation for a set of project points.""" + +# #inputs +# weather_arg = {} +# weather_arg['satellite'] = weather_satellite +# weather_arg['names'] = weather_names +# weather_arg['NREL_HPC'] = True #TODO: add argument or auto detect +# weather_arg['attributes'] = [ +# 'temp_air', +# 'wind_speed', +# 'dhi', 'ghi', +# 'dni','relative_humidity' +# ] + +# #TODO: is there a better way to add the meta data? +# nsrdb_fnames, hsds = weather.get_NSRDB_fnames( +# weather_arg['satellite'], +# weather_arg['names'], +# weather_arg['NREL_HPC']) + +# with NSRDBX(nsrdb_fnames[0], hsds=hsds) as f: +# meta = f.meta[f.meta.index.isin(project_points.gids)] +# ti = f.time_index + +# all_fields = ['RH_surface_outside', +# 'RH_front_encap', +# 'RH_back_encap', +# 'RH_backsheet'] + +# out_fp = Path(out_dir) / f"out_rel_humidity{tag}.h5" +# shapes = {n : (len(ti), len(project_points)) for n in all_fields} +# attrs = {n : {'units': '%'} for n in all_fields} +# chunks = {n : None for n in all_fields} +# dtypes = {n : "float32" for n in all_fields} + +# Outputs.init_h5( +# out_fp, +# all_fields, +# shapes, +# attrs, +# chunks, +# dtypes, +# meta=meta.reset_index(), +# time_index=ti +# ) + +# future_to_point = {} +# with ProcessPoolExecutor(max_workers=max_workers) as executor: +# for point in project_points: +# gid = int(point.gid) +# weather_df, meta = weather.load( +# database = weather_db, +# id = gid, +# **weather_arg) +# future = executor.submit( +# module, +# weather_df, +# meta, +# tilt, +# azimuth, +# sky_model, +# temp_model, +# mount_type, +# WVTRo, +# EaWVTR, +# So, +# l, +# Eas, +# wind_speed_factor +# ) +# future_to_point[future] = gid + +# with Outputs(out_fp, mode="a") as out: +# for future in as_completed(future_to_point): +# result = future.result() +# gid = future_to_point.pop(future) + +# ind = project_points.index(gid) +# for dset, data in result.items(): +# out[dset, :, ind] = data.values + +# return out_fp.as_posix() \ No newline at end of file diff --git a/pvdeg/standards.py b/pvdeg/standards.py index 07a1344a..a24e5449 100644 --- a/pvdeg/standards.py +++ b/pvdeg/standards.py @@ -4,6 +4,7 @@ import numpy as np import pandas as pd +import dask.dataframe as dd import pvlib from rex import NSRDBX from rex import Outputs @@ -19,14 +20,18 @@ def eff_gap(T_0, T_inf, level=1, T98=None, x_0=6.1): ''' - Calculate a minimum installation distance for rooftop mounded PV systems. + Calculate a minimum installation standoff distance for rooftop mounded PV systems for + a given levl according to IEC TS 63126 or the standoff to achieve a given 98ᵗʰ percentile + temperature. If the given T₉₈ is that for a specific system, then it is a calculation of + the effective gap of that system. The 98th percentile calculations for T_0 and T_inf are + also calculated. Parameters ---------- level : int, optional - Options 1, or 2. Specifies T98 temperature boundary for level 1 or level 2 according to IEC TS 63216. + Options 1, or 2. Specifies T₉₈ temperature boundary for level 1 or level 2 according to IEC TS 63216. T98 : float, optional - Instead of the level the T98 temperature can be specified directly (overwrites level). + Instead of the level the T₉₈ temperature can be specified directly (overwrites level). x0 : float, optional Thermal decay constant (cm), [Kempe, PVSC Proceedings 2023] @@ -51,20 +56,27 @@ def eff_gap(T_0, T_inf, level=1, T98=None, x_0=6.1): T98_0 = T_0.quantile(q=0.98, interpolation='linear') T98_inf = T_inf.quantile(q=0.98, interpolation='linear') - x = -x_0 * np.log(1-(T98_0-T98)/(T98_0-T98_inf)) + try: + x = -x_0 * np.log(1-(T98_0-T98)/(T98_0-T98_inf)) + except RuntimeWarning as e: + x = np.nan return x, T98_0, T98_inf -def calc_standoff( - weather_df, - meta, +def standoff( + weather_df=None, + meta=None, + weather_kwarg=None, tilt=None, azimuth=180, sky_model='isotropic', temp_model='sapm', module_type='glass_polymer', # self.module + conf_0= 'insulated_back_glass_polymer', + conf_inf= 'open_rack_glass_polymer', level=1, + T98=None, x_0=6.1, wind_speed_factor=1): ''' @@ -83,9 +95,13 @@ def calc_standoff( sky_model : str, optional Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'. temp_model : str, optional - Options: 'sapm', 'pvsyst', 'faiman', 'sandia'. + Options: 'sapm'. 'pvsyst' and 'faiman' will be added later. module_type : str, optional Options: 'glass_polymer', 'glass_glass'. + conf_0 : str, optional + Default: 'insulated_back_glass_polymer' + conf_inf : str, optional + Default: 'open_rack_glass_polymer' level : int, optional Options 1, or 2. Temperature level 1 or level 2 according to IEC TS 63216. x0 : float, optional @@ -93,10 +109,11 @@ def calc_standoff( wind_speed_factor : float, optional Wind speed correction factor to account for different wind speed measurement heights between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM) + The NSRD uses calculations at 2m (i.e module height) Returns ------- x : float - Minimum installation distance in centimeter per IEC TS 63126. + Minimum installation distance in centimeter per IEC TS 63126 when the default settings are used. Effective gap "x" for the lower limit for Level 1 or Level 0 modules (IEC TS 63216) References @@ -104,6 +121,17 @@ def calc_standoff( M. Kempe, et al. Close Roof Mounted System Temperature Estimation for Compliance to IEC TS 63126, PVSC Proceedings 2023 ''' + + parameters = ['temp_air', 'wind_speed', 'dhi', 'ghi', 'dni'] + + if isinstance(weather_df, dd.DataFrame): + weather_df = weather_df[parameters].compute() + weather_df.set_index('time', inplace=True) + elif isinstance(weather_df, pd.DataFrame): + weather_df = weather_df[parameters] + elif weather_df is None: + weather_df, meta = weather.get(**weather_kwarg) + if module_type == 'glass_polymer': conf_0 = 'insulated_back_glass_polymer' conf_inf = 'open_rack_glass_polymer' @@ -116,114 +144,123 @@ def calc_standoff( azimuth=azimuth, sky_model=sky_model) T_0 = temperature.module(weather_df, meta, poa, temp_model, conf_0, wind_speed_factor) T_inf = temperature.module(weather_df, meta, poa, temp_model, conf_inf, wind_speed_factor) - x, T98_0, T98_inf = eff_gap(T_0, T_inf, level, x_0) + x, T98_0, T98_inf = eff_gap(T_0, T_inf, level=level, T98=T98, x_0=x_0) - return {'x':x, 'T98_0':T98_0, 'T98_inf':T98_inf} + res = {'x': x, + 'T98_0': T98_0, + 'T98_inf': T98_inf, + 'latitude': meta['latitude'], + 'longitude': meta['longitude']} + df_res = pd.DataFrame.from_dict(res, orient='index').T -def run_calc_standoff( - project_points, - out_dir, - tag, - #weather_db, - #weather_satellite, - #weather_names, - max_workers=None, - tilt=None, - azimuth=180, - sky_model='isotropic', - temp_model='sapm', - module_type='glass_polymer', - level=1, - x_0=6.1, - wind_speed_factor=1 -): - - """ - parallelization utilizing gaps #TODO: write docstring - """ - - #inputs - weather_arg = {} - #weather_arg['satellite'] = weather_satellite - #weather_arg['names'] = weather_names - weather_arg['NREL_HPC'] = True #TODO: add argument or auto detect - weather_arg['attributes'] = [ - 'air_temperature', - 'wind_speed', - 'dhi', - 'ghi', - 'dni', - 'relative_humidity' - ] - - all_fields = ['x', 'T98_0', 'T98_inf'] - - out_fp = Path(out_dir) / f"out_standoff{tag}.h5" - shapes = {n : (len(project_points), ) for n in all_fields} - attrs = {'x' : {'units': 'cm'}, - 'T98_0' : {'units': 'Celsius'}, - 'T98_inf' : {'units': 'Celsius'}} - chunks = {n : None for n in all_fields} - dtypes = {n : "float32" for n in all_fields} - - # #TODO: is there a better way to add the meta data? - # nsrdb_fnames, hsds = weather.get_NSRDB_fnames( - # weather_arg['satellite'], - # weather_arg['names'], - # weather_arg['NREL_HPC']) - - # with NSRDBX(nsrdb_fnames[0], hsds=hsds) as f: - # meta = f.meta[f.meta.index.isin(project_points.gids)] - - Outputs.init_h5( - out_fp, - all_fields, - shapes, - attrs, - chunks, - dtypes, - #meta=meta.reset_index() - meta=project_points.df - ) - - future_to_point = {} - with ProcessPoolExecutor(max_workers=max_workers) as executor: - for idx, point in project_points.df.iterrows(): - database = point.weather_db - gid = idx #int(point.gid) - df_weather_kwargs = point.drop('weather_db', inplace=False).filter(like='weather_') - df_weather_kwargs.index = df_weather_kwargs.index.map( - lambda arg: arg.lstrip('weather_')) - weather_kwarg = weather_arg | df_weather_kwargs.to_dict() - - weather_df, meta = weather.load( - database = database, - id = gid, - #satellite = point.satellite, #TODO: check input - **weather_kwarg) - future = executor.submit( - calc_standoff, - weather_df, - meta, - tilt, - azimuth, - sky_model, - temp_model, - module_type, - level, - x_0, - wind_speed_factor - ) - future_to_point[future] = gid - - with Outputs(out_fp, mode="a") as out: - for future in as_completed(future_to_point): - result = future.result() - gid = future_to_point.pop(future) - - #ind = project_points.index(gid) - for dset, data in result.items(): - out[dset, idx] = np.array([data]) - - return out_fp.as_posix() + return df_res + + + +# def run_calc_standoff( +# project_points, +# out_dir, +# tag, +# #weather_db, +# #weather_satellite, +# #weather_names, +# max_workers=None, +# tilt=None, +# azimuth=180, +# sky_model='isotropic', +# temp_model='sapm', +# module_type='glass_polymer', +# level=1, +# x_0=6.1, +# wind_speed_factor=1 +# ): + +# """ +# parallelization utilizing gaps #TODO: write docstring +# """ + +# #inputs +# weather_arg = {} +# #weather_arg['satellite'] = weather_satellite +# #weather_arg['names'] = weather_names +# weather_arg['NREL_HPC'] = True #TODO: add argument or auto detect +# weather_arg['attributes'] = [ +# 'air_temperature', +# 'wind_speed', +# 'dhi', +# 'ghi', +# 'dni', +# 'relative_humidity' +# ] + +# all_fields = ['x', 'T98_0', 'T98_inf'] + +# out_fp = Path(out_dir) / f"out_standoff{tag}.h5" +# shapes = {n : (len(project_points), ) for n in all_fields} +# attrs = {'x' : {'units': 'cm'}, +# 'T98_0' : {'units': 'Celsius'}, +# 'T98_inf' : {'units': 'Celsius'}} +# chunks = {n : None for n in all_fields} +# dtypes = {n : "float32" for n in all_fields} + +# # #TODO: is there a better way to add the meta data? +# # nsrdb_fnames, hsds = weather.get_NSRDB_fnames( +# # weather_arg['satellite'], +# # weather_arg['names'], +# # weather_arg['NREL_HPC']) + +# # with NSRDBX(nsrdb_fnames[0], hsds=hsds) as f: +# # meta = f.meta[f.meta.index.isin(project_points.gids)] + +# Outputs.init_h5( +# out_fp, +# all_fields, +# shapes, +# attrs, +# chunks, +# dtypes, +# #meta=meta.reset_index() +# meta=project_points.df +# ) + +# future_to_point = {} +# with ProcessPoolExecutor(max_workers=max_workers) as executor: +# for idx, point in project_points.df.iterrows(): +# database = point.weather_db +# gid = idx #int(point.gid) +# df_weather_kwargs = point.drop('weather_db', inplace=False).filter(like='weather_') +# df_weather_kwargs.index = df_weather_kwargs.index.map( +# lambda arg: arg.lstrip('weather_')) +# weather_kwarg = weather_arg | df_weather_kwargs.to_dict() + +# weather_df, meta = weather.load( +# database = database, +# id = gid, +# #satellite = point.satellite, #TODO: check input +# **weather_kwarg) +# future = executor.submit( +# calc_standoff, +# weather_df, +# meta, +# tilt, +# azimuth, +# sky_model, +# temp_model, +# module_type, +# level, +# x_0, +# wind_speed_factor +# ) +# future_to_point[future] = gid + +# with Outputs(out_fp, mode="a") as out: +# for future in as_completed(future_to_point): +# result = future.result() +# gid = future_to_point.pop(future) + +# #ind = project_points.index(gid) +# for dset, data in result.items(): +# out[dset, idx] = np.array([data]) + +# return out_fp.as_posix() diff --git a/pvdeg/utilities.py b/pvdeg/utilities.py index 8d92d281..089c4250 100644 --- a/pvdeg/utilities.py +++ b/pvdeg/utilities.py @@ -30,13 +30,31 @@ def gid_downsampling(meta, n): gids_sub = meta[ (meta['longitude'].isin(lon_sub)) & (meta['latitude'].isin(lat_sub)) - ].index + ].index.values meta_sub = meta.loc[gids_sub] return meta_sub, gids_sub +def meta_as_dict(rec): + """ + Turn a numpy recarray record into a dict. + + Parameters: + ----------- + rec : (np.recarray) + numpy structured array with labels as dtypes + + Returns: + -------- + : (dict) + dictionary of numpy structured array + """ + + return {name:rec[name].item() for name in rec.dtype.names} + + def get_kinetics(name=None, fname='kinetic_parameters.json'): """ Returns a list of LETID/B-O LID kinetic parameters from kinetic_parameters.json @@ -397,4 +415,4 @@ def ts_gid_df(file, gid): res.gid = gid res.lat = meta.latitude[gid] res.lon = meta.longitude[gid] - return res + return res \ No newline at end of file diff --git a/pvdeg/weather.py b/pvdeg/weather.py index 243302c6..c87c455d 100644 --- a/pvdeg/weather.py +++ b/pvdeg/weather.py @@ -9,9 +9,13 @@ from rex import NSRDBX, Outputs from pvdeg import humidity -def get(database, id, **kwargs): +import h5py +import dask.dataframe as dd +import xarray as xr + +def get(database, id=None, geospatial=False, **kwargs): """ - Load weather data directly from NSRDB or through any other PVLIB i/o + Load weather data directly from NSRDB or through any other PVLIB i/o tools function Parameters: @@ -21,7 +25,12 @@ def get(database, id, **kwargs): id : (int or tuple) If NSRDB, id is the gid for the desired location If PVGIS, id is a tuple of (latitude, longitude) for the desired location - **kwargs : + geospatial : (bool) + If True, initialize weather data via xarray dataset and meta data via + dask dataframe. This is useful for large scael geospatial analyses on + distributed compute systems. Geospaital analyses are only supported for + NSRDB data and locally stored h5 files that follow pvlib conventions. + **kwargs : Additional keyword arguments to pass to the get_weather function (see pvlib.iotools.get_psm3 for PVGIS, and get_NSRDB for NSRDB) @@ -32,7 +41,7 @@ def get(database, id, **kwargs): meta : (dict) Dictionary of metadata for the weather data """ - + META_MAP = {'elevation' : 'altitude'} if type(id) is tuple: @@ -43,34 +52,52 @@ def get(database, id, **kwargs): elif type(id) is int: gid = id location = None - else: - raise TypeError( - 'Project points needs to be either location tuple (latitude, longitude), or gid integer.') - - #TODO: decide wether to follow NSRDB or pvlib conventions... - # e.g. temp_air vs. air_temperature - # "map variables" will guarantee PVLIB conventions (automatic in coming update) which is "temp_air" - if database == 'NSRDB': - weather_df, meta = get_NSRDB(gid=gid, location=location, **kwargs) - elif database == 'PVGIS': - weather_df, _, meta, _ = iotools.get_pvgis_tmy(latitude=lat, longitude=lon, - map_variables=True, **kwargs) - meta = meta['location'] - elif database == 'PSM3': - weather_df, meta = iotools.get_psm3(latitude=lat, longitude=lon, **kwargs) - else: - raise NameError('Weather database not found.') - - if 'relative_humidity' not in weather_df.columns: - print('Column "relative_humidity" not found in DataFrame. Calculating...') - weather_df = humidity._ambient(weather_df) - - # map meta-names as needed - for key in [*meta.keys()]: - if key in META_MAP.keys(): - meta[META_MAP[key]] = meta.pop(key) - - return weather_df, meta + elif id is None: + if not geospatial: + raise TypeError( + 'Specify location via tuple (latitude, longitude), or gid integer.') + + if not geospatial: + #TODO: decide wether to follow NSRDB or pvlib conventions... + # e.g. temp_air vs. air_temperature + # "map variables" will guarantee PVLIB conventions (automatic in coming update) which is "temp_air" + if database == 'NSRDB': + weather_df, meta = get_NSRDB(gid=gid, location=location, **kwargs) + elif database == 'PVGIS': + weather_df, _, meta, _ = iotools.get_pvgis_tmy(latitude=lat, longitude=lon, + map_variables=True, **kwargs) + meta = meta['location'] + elif database == 'PSM3': + weather_df, meta = iotools.get_psm3(latitude=lat, longitude=lon, **kwargs) + elif database == 'local': + fp = kwargs.pop('file') + fn, fext = os.path.splitext(fp) + weather_df, meta = read(gid=gid, file_in=fp, file_type=fext[1:], **kwargs) + else: + raise NameError('Weather database not found.') + + if 'relative_humidity' not in weather_df.columns: + print('Column "relative_humidity" not found in DataFrame. Calculating...') + weather_df = humidity._ambient(weather_df) + + # map meta-names as needed + for key in [*meta.keys()]: + if key in META_MAP.keys(): + meta[META_MAP[key]] = meta.pop(key) + + return weather_df, meta + + elif geospatial: + if database == 'NSRDB': + weather_ds, meta_df = get_NSRDB(geospatial=geospatial, **kwargs) + elif database == 'local': + fp = kwargs.pop('file') + weather_ds, meta_df = ini_h5_geospatial(fp) + else: + raise NameError(f'Geospatial analysis not implemented for {database}.') + + return weather_ds, meta_df + def read(file_in, file_type, **kwargs): @@ -90,7 +117,7 @@ def read(file_in, file_type, **kwargs): supported = ['psm3','tmy3','epw','h5'] file_type = file_type.upper() - + if file_type in ['PSM3','PSM']: weather_df, meta = iotools.read_psm3(filename=file_in, map_variables=True) elif file_type in ['TMY3','TMY']: @@ -101,7 +128,7 @@ def read(file_in, file_type, **kwargs): weather_df, meta = read_h5(file=file_in, **kwargs) else: print(f'File-Type not recognized. supported types:\n{supported}') - + if not isinstance(meta, dict): meta = meta.to_dict() @@ -111,7 +138,7 @@ def read(file_in, file_type, **kwargs): def read_h5(gid, file, attributes=None, **_): """ Read a locally stored h5 weather file that follows NSRDB conventions. - + Parameters: ----------- file_path : (str) @@ -129,9 +156,13 @@ def read_h5(gid, file, attributes=None, **_): Dictionary of metadata for the weather data """ - fp = os.path.join(os.path.dirname(__file__), file) + if os.path.dirname(file): + fp = file + else: + fp = os.path.join(os.path.dirname(__file__), + os.path.basename(file)) - with Outputs(fp, mode='r') as f: + with Outputs(fp, mode='r') as f: meta = f.meta.loc[gid] index = f.time_index dattr = f.attrs @@ -147,11 +178,86 @@ def read_h5(gid, file, attributes=None, **_): weather_df = pd.DataFrame(index=index, columns=attributes) for dset in attributes: - with Outputs(fp, mode='r') as f: + with Outputs(fp, mode='r') as f: weather_df[dset] = f[dset, :, gid] return weather_df, meta.to_dict() +def ini_h5_geospatial(fps): + """ + initialize an h5 weather file that follows NSRDB conventions for + geospatial analyses. + + Parameters: + ----------- + file_path : (str) + file path and name of h5 file to be read + gid : (int) + gid for the desired location + attributes : (list) + List of weather attributes to extract from NSRDB + + Returns: + -------- + weather_df : (pd.DataFrame) + DataFrame of weather data + meta : (dict) + Dictionary of metadata for the weather data + """ + dss = [] + for i, fp in enumerate(fps): + hf = h5py.File(fp, 'r') + attr = list(hf) + attr_to_read = [elem for elem in attr if elem not in ['meta', 'time_index']] + + chunks = [] + shapes = [] + for var in attr_to_read: + chunks.append(hf[var].chunks) + shapes.append(hf[var].shape) + chunks = min(set(chunks)) + shapes = min(set(shapes)) + + if i == 0: + time_index = pd.to_datetime(hf['time_index'][...].astype(str)).values + meta_df = pd.read_hdf(fp, key='meta') + coords = {'gid': meta_df.index.values, 'time': time_index} + coords_len = {'time': time_index.shape[0], 'gid': meta_df.shape[0]} + + ds = xr.open_dataset(fp, + engine='h5netcdf', + phony_dims='sort', + chunks={'phony_dim_0':chunks[0], 'phony_dim_1':chunks[1]}, + drop_variables=['time_index', 'meta'], + mask_and_scale=False, + decode_cf=True) + + for var in ds.data_vars: + if hasattr(getattr(ds, var),'psm_scale_factor'): + scale_factor = 1/ds[var].psm_scale_factor + getattr(ds,var).attrs['scale_factor'] = scale_factor + + if tuple(coords_len.values()) == (ds.dims['phony_dim_0'], ds.dims['phony_dim_1']): + rename = {'phony_dim_0':'time', 'phony_dim_1':'gid'} + elif tuple(coords_len.values()) == (ds.dims['phony_dim_1'], ds.dims['phony_dim_0']): + rename = {'phony_dim_0':'gid', 'phony_dim_1':'time'} + else: + raise ValueError('Dimensions do not match') + ds = ds.rename({'phony_dim_0':rename['phony_dim_0'], 'phony_dim_1':rename['phony_dim_1']}) + ds = ds.assign_coords(coords) + + # TODO: In case re-chunking becomes necessary + # ax0 = list(ds.dims.keys())[list(ds.dims.values()).index(shapes[0])] + # ax1 = list(ds.dims.keys())[list(ds.dims.values()).index(shapes[1])] + # ds = ds.chunk(chunks={ax0:chunks[0], ax1:chunks[1]}) + dss.append(ds) + + ds = xr.merge(dss) + ds = xr.decode_cf(ds) + weather_ds = ds + + return weather_ds, meta_df + def get_NSRDB_fnames(satellite, names, NREL_HPC = False, **_): """ @@ -191,24 +297,24 @@ def get_NSRDB_fnames(satellite, names, NREL_HPC = False, **_): else: hpc_fp = '/nrel/nsrdb/' hsds = True - - if type(names) == int: - nsrdb_fp = os.path.join(hpc_fp, sat_map[satellite], '*_{}.h5'.format(names)) + + if type(names) in [int, float]: + nsrdb_fp = os.path.join(hpc_fp, sat_map[satellite], '*_{}.h5'.format(int(names))) nsrdb_fnames = glob.glob(nsrdb_fp) else: nsrdb_fp = os.path.join(hpc_fp, sat_map[satellite], '*_{}*.h5'.format(names.lower())) nsrdb_fnames = glob.glob(nsrdb_fp) - + if len(nsrdb_fnames) == 0: raise FileNotFoundError( "Couldn't find NSRDB input files! \nSearched for: '{}'".format(nsrdb_fp)) - + return nsrdb_fnames, hsds -def get_NSRDB(satellite, names, NREL_HPC, gid=None, location=None, attributes=None, **_): +def get_NSRDB(satellite, names, NREL_HPC, gid=None, location=None, geospatial=False, attributes=None, **_): """ - Get NSRDB weather data from different satellites and years. + Get NSRDB weather data from different satellites and years. Provide either gid or location tuple. Parameters: @@ -241,49 +347,69 @@ def get_NSRDB(satellite, names, NREL_HPC, gid=None, location=None, attributes=No META_MAP = {'elevation' : 'altitude'} - nsrdb_fnames, hsds = get_NSRDB_fnames(satellite, names, NREL_HPC) + if not geospatial: + nsrdb_fnames, hsds = get_NSRDB_fnames(satellite, names, NREL_HPC) - dattr = {} - for i, file in enumerate(nsrdb_fnames): - with NSRDBX(file, hsds=hsds) as f: - if i == 0: - if gid == None: #TODO: add exception handling - gid = f.lat_lon_gid(location) - meta = f['meta', gid].iloc[0] - index = f.time_index + dattr = {} + for i, file in enumerate(nsrdb_fnames): + with NSRDBX(file, hsds=hsds) as f: + if i == 0: + if gid == None: #TODO: add exception handling + gid = f.lat_lon_gid(location) + meta = f['meta', gid].iloc[0] + index = f.time_index - lattr = f.datasets - for attr in lattr: - dattr[attr] = file + lattr = f.datasets + for attr in lattr: + dattr[attr] = file - if attributes == None: - attributes = list(dattr.keys()) - try: - attributes.remove('meta') - attributes.remove('tmy_year_short') - except ValueError: - pass + if attributes == None: + attributes = list(dattr.keys()) + try: + attributes.remove('meta') + attributes.remove('tmy_year_short') + except ValueError: + pass - weather_df = pd.DataFrame(index=index) + weather_df = pd.DataFrame(index=index) - for dset in attributes: + for dset in attributes: - # switch dset names to pvlib standard - if dset in [*DSET_MAP.keys()]: - column_name = DSET_MAP[dset] - else: - column_name = dset + # switch dset names to pvlib standard + if dset in [*DSET_MAP.keys()]: + column_name = DSET_MAP[dset] + else: + column_name = dset - with NSRDBX(dattr[dset], hsds=hsds) as f: - weather_df[column_name] = f[dset, :, gid] + with NSRDBX(dattr[dset], hsds=hsds) as f: + weather_df[column_name] = f[dset, :, gid] - # switch meta key names to pvlib standard - re_idx = [] - for key in [*meta.index]: - if key in META_MAP.keys(): - re_idx.append(META_MAP[key]) - else: - re_idx.append(key) - meta.index = re_idx + # switch meta key names to pvlib standard + re_idx = [] + for key in [*meta.index]: + if key in META_MAP.keys(): + re_idx.append(META_MAP[key]) + else: + re_idx.append(key) + meta.index = re_idx + + return weather_df, meta.to_dict() + + elif geospatial: + + nsrdb_fnames, hsds = get_NSRDB_fnames(satellite, names, NREL_HPC) + weather_ds, meta_df = ini_h5_geospatial(nsrdb_fnames) + + if attributes is not None: + weather_ds = weather_ds[attributes] + + for dset in weather_ds.data_vars: + if dset in DSET_MAP.keys(): + weather_ds = weather_ds.rename({dset: DSET_MAP[dset]}) + + for mset in meta_df.columns: + if mset in META_MAP.keys(): + meta_df.rename(columns={mset: META_MAP[mset]}, inplace=True) + + return weather_ds, meta_df - return weather_df, meta.to_dict() \ No newline at end of file diff --git a/pvdeg_tutorials/tutorials/6 - Geospatial Analysis.ipynb b/pvdeg_tutorials/tutorials/6 - Geospatial Analysis.ipynb new file mode 100644 index 00000000..1cabd53c --- /dev/null +++ b/pvdeg_tutorials/tutorials/6 - Geospatial Analysis.ipynb @@ -0,0 +1,878 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6 - Geospatial analysis pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-13T20:12:46.350659Z", + "start_time": "2019-06-13T20:11:46.936643Z" + } + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pvdeg\n", + "import csv\n", + "import h5py" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import dask\n", + "import dask.array as da\n", + "import dask.dataframe as dd\n", + "import xarray as xr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Single location example" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Get weather data\n", + "weather_db = 'NSRDB'\n", + "weather_id = (39.741931, -105.169891)\n", + "#weather_id = 1933572\n", + "weather_arg = {'satellite': 'Americas',\n", + " 'names': 2021,\n", + " 'NREL_HPC': True,\n", + " 'attributes': ['air_temperature', 'wind_speed', 'dhi', 'ghi', 'dni', 'relative_humidity']}\n", + "\n", + "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "standoff = pvdeg.standards.standoff(weather_df=weather_df, meta=meta)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Geospatial example" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Get weather data\n", + "weather_db = 'NSRDB'\n", + "weather_id = (39.741931, -105.169891)\n", + "#weather_id = 1933572\n", + "weather_arg = {'satellite': 'Americas',\n", + " 'names': 2021,\n", + " 'NREL_HPC': True,\n", + " 'attributes': ['air_temperature', 'wind_speed', 'dhi', 'ghi', 'dni', 'relative_humidity']}\n", + "\n", + "weather_ds, meta_df = pvdeg.weather.get(weather_db, geospatial=True, **weather_arg)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "meta_USA = meta_df[meta_df['country'] == 'United States']\n", + "weather_USA = weather_ds.sel(gid=meta_USA.index)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dashboard: http://127.0.0.1:8787/status\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pvdeg.geospatial.start_dask()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "meta_test = meta_USA.iloc[0:10000]\n", + "weather_test = weather_USA.sel(gid=meta_test.index)\n", + "\n", + "geo = {'func': pvdeg.standards.standoff,\n", + " 'weather_ds': weather_test,\n", + " 'meta_df': meta_test}\n", + "\n", + "standoff_res = pvdeg.geospatial.analysis(**geo)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import cartopy.crs as ccrs\n", + "\n", + "air = xr.tutorial.open_dataset(\"air_temperature\").air\n", + "\n", + "p = air.isel(time=0).plot(\n", + " subplot_kws=dict(projection=ccrs.Orthographic(-80, 35), facecolor=\"gray\"),\n", + " transform=ccrs.PlateCarree(),)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 504e8f45745ec7e389dedfb2e2435735971d42ba Mon Sep 17 00:00:00 2001 From: martin-springer Date: Sun, 24 Sep 2023 23:08:35 -0400 Subject: [PATCH 03/10] resolving merge conflicts --- pvdeg/standards.py | 1 - pvdeg/weather.py | 2 -- 2 files changed, 3 deletions(-) diff --git a/pvdeg/standards.py b/pvdeg/standards.py index ab628a78..59b045f9 100644 --- a/pvdeg/standards.py +++ b/pvdeg/standards.py @@ -81,7 +81,6 @@ def standoff( conf_inf= 'open_rack_glass_polymer', level=1, T98=None, - T98=None, x_0=6.1, wind_speed_factor=1): ''' diff --git a/pvdeg/weather.py b/pvdeg/weather.py index ff83f1e3..40ea02af 100644 --- a/pvdeg/weather.py +++ b/pvdeg/weather.py @@ -172,7 +172,6 @@ def read_h5(gid, file, attributes=None, **_): fp = os.path.join(os.path.dirname(__file__), os.path.basename(file)) - with Outputs(fp, mode='r') as f: with Outputs(fp, mode='r') as f: meta = f.meta.loc[gid] index = f.time_index @@ -189,7 +188,6 @@ def read_h5(gid, file, attributes=None, **_): weather_df = pd.DataFrame(index=index, columns=attributes) for dset in attributes: - with Outputs(fp, mode='r') as f: with Outputs(fp, mode='r') as f: weather_df[dset] = f[dset, :, gid] From f281c0d6258d63dcc76b53864efdd66f29d5bd31 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Mon, 25 Sep 2023 10:01:20 -0400 Subject: [PATCH 04/10] pytables is called tables in pip... --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 15e3cabc..380f7e95 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,7 +12,7 @@ matplotlib jupyterlab notebook NREL-rex -pytables +tables xarray netCDF4 h5py From 8a0bc5f35c4171192cbd06fe8b557b8d7fc0b9e6 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Mon, 25 Sep 2023 10:03:56 -0400 Subject: [PATCH 05/10] add dask to requirements --- requirements.txt | 2 ++ 1 file changed, 2 insertions(+) diff --git a/requirements.txt b/requirements.txt index 380f7e95..b0d4558a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,8 @@ numpy numba pandas +dask +dask-jobqueue pvlib python-dateutil pytz From e5819424ea0d93e3b931de8e9cc8005a2022dbe3 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Mon, 25 Sep 2023 10:06:25 -0400 Subject: [PATCH 06/10] update test standards --- tests/test_standards.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/tests/test_standards.py b/tests/test_standards.py index 4fcc3b4f..a814cf03 100644 --- a/tests/test_standards.py +++ b/tests/test_standards.py @@ -2,7 +2,7 @@ import json import pytest import pandas as pd -import pvdeg +import pvdeg from pvdeg import TEST_DATA_DIR ''' @@ -21,8 +21,8 @@ with open(os.path.join(TEST_DATA_DIR, 'meta.json'), 'r') as file: META = json.load(file) -def test_calc_standoff(): - result_l1 = pvdeg.standards.calc_standoff( +def test_standoff(): + result_l1 = pvdeg.standards.standoff( WEATHER, META, tilt=None, @@ -33,8 +33,8 @@ def test_calc_standoff(): level=1, x_0=6.1, wind_speed_factor=1.71) - - result_l2 = pvdeg.standards.calc_standoff( + + result_l2 = pvdeg.standards.standoff( WEATHER, META, tilt=None, @@ -45,13 +45,13 @@ def test_calc_standoff(): level=2, x_0=6.1, wind_speed_factor=1.71) - - expected_result_l1 = {'x': 2.3835484140461736, - 'T98_0': 79.03006155479213, + + expected_result_l1 = {'x': 2.3835484140461736, + 'T98_0': 79.03006155479213, 'T98_inf': 51.11191792458173} - - expected_result_l2 = {'x': -0.20832926385165268, - 'T98_0': 79.03006155479213, + + expected_result_l2 = {'x': -0.20832926385165268, + 'T98_0': 79.03006155479213, 'T98_inf': 51.11191792458173} assert expected_result_l1 == pytest.approx(result_l1) From c017e46e5ef5c083851b031de0387b88d384276b Mon Sep 17 00:00:00 2001 From: martin-springer Date: Mon, 25 Sep 2023 10:16:44 -0400 Subject: [PATCH 07/10] fix tests --- pvdeg/weather.py | 2 +- tests/data/h5_pytest.h5 | Bin 125608 -> 125608 bytes tests/test_collection.py | 4 ++-- tests/test_standards.py | 23 ++++++++++++++++++----- 4 files changed, 21 insertions(+), 8 deletions(-) diff --git a/pvdeg/weather.py b/pvdeg/weather.py index 40ea02af..f5672813 100644 --- a/pvdeg/weather.py +++ b/pvdeg/weather.py @@ -124,7 +124,7 @@ def read(file_in, file_type, **kwargs): if file_type in ['PSM3','PSM']: weather_df, meta = iotools.read_psm3(filename=file_in, map_variables=True) elif file_type in ['TMY3','TMY']: - weather_df, meta = iotools.read_tmy3(filename=file_in, map_variables=True) + weather_df, meta = iotools.read_tmy3(filename=file_in) #map variable not worki - check pvlib for map_variables elif file_type == 'EPW': weather_df, meta = iotools.read_epw(filename=file_in) elif file_type == 'H5': diff --git a/tests/data/h5_pytest.h5 b/tests/data/h5_pytest.h5 index 49c9dcbfbf53c0f33313ee84d0eef02ef60750c5..b0f1fda7b98a6cdf68464cbb5666cff3df9799b5 100644 GIT binary patch delta 695 zcmZ2+m3_rk_6-l18P9Eg$n4L;cy6*GhX$kJeLX1qBNQ zBLgF29R))JD`QJ5Ba_JsIcyotC*SALnJmDm!1!RZA?JC%i5ECFN!&=Fib)!qPjm!` vQ^hEb%`es$$WzHEjr-IxYI_DBBh}1WVM{%;UgS{EER8wTGb`gXqk}vEOaH~H delta 731 zcmZ2+m3_rk_6-l1882>r$n4L;cyY2JhX$k3GX;&5)U?FXoDv-c-^3yX z1tSF`10!P{1p{L%LklZIv&r)~Y#A*k-{H_ Date: Wed, 27 Sep 2023 01:07:06 -0600 Subject: [PATCH 08/10] DuraMAT Live Demo --- pvdeg/fatigue.py | 14 +- pvdeg/geospatial.py | 75 +- pvdeg/standards.py | 4 +- pvdeg/weather.py | 7 +- .../tutorials/6 - Geospatial Analysis.ipynb | 878 ------------------ .../tutorials/ASTM Live Demo.ipynb | 364 +++++++- .../tutorials/DuraMAT Live Demo.ipynb | 507 ++++++++++ pvdeg_tutorials/tutorials/images/xarray.webp | Bin 0 -> 25530 bytes 8 files changed, 921 insertions(+), 928 deletions(-) delete mode 100644 pvdeg_tutorials/tutorials/6 - Geospatial Analysis.ipynb create mode 100644 pvdeg_tutorials/tutorials/DuraMAT Live Demo.ipynb create mode 100644 pvdeg_tutorials/tutorials/images/xarray.webp diff --git a/pvdeg/fatigue.py b/pvdeg/fatigue.py index d2a06da9..c819c150 100644 --- a/pvdeg/fatigue.py +++ b/pvdeg/fatigue.py @@ -27,10 +27,10 @@ def _avg_daily_temp_change(time_range, temp_cell): Average of Daily Maximum Temperature for 1-year (Celsius) """ - + if time_range.dtype == 'object': time_range = pd.to_datetime(time_range) - + # Setup frame for vector processing timeAndTemp_df = pd.DataFrame(columns=['Cell Temperature']) timeAndTemp_df['Cell Temperature'] = temp_cell @@ -111,7 +111,7 @@ def solder_fatigue(weather_df, meta, This function uses the default values for 60-min input intervals from Table 4 of the above paper. For other use cases, please refer to the paper for recommended values of C1 and the reversal temperature. - + Parameters ------------ weather_df : pd.dataframe @@ -143,9 +143,9 @@ def solder_fatigue(weather_df, meta, """ - # TODO this, and many other functions with temp_cell or temp_module would benefit from an + # TODO this, and many other functions with temp_cell or temp_module would benefit from an # optional parameter "conf = 'open_rack_glass_glass' or equivalent" - + # TODO Make this function have more utility. # People want to run all the scenarios from the bosco paper. # Currently have everything hard coded for hourly calculation @@ -158,10 +158,10 @@ def solder_fatigue(weather_df, meta, if time_range is None: time_range = weather_df.index - + if temp_cell is None: temp_cell = temperature.cell(weather_df, meta) - + temp_amplitude, temp_max_avg = _avg_daily_temp_change(time_range, temp_cell) temp_max_avg = convert_temperature(temp_max_avg, 'Celsius', 'Kelvin') diff --git a/pvdeg/geospatial.py b/pvdeg/geospatial.py index d9d68395..1c0e1ac5 100644 --- a/pvdeg/geospatial.py +++ b/pvdeg/geospatial.py @@ -10,6 +10,10 @@ import pandas as pd from dask.distributed import Client, LocalCluster +import matplotlib.pyplot as plt +import cartopy.crs as ccrs +import cartopy.io.shapereader as shpreader + def start_dask(hpc=None): """ @@ -97,7 +101,6 @@ def calc_gid(ds_gid, meta_gid, func, **kwargs): """ df_weather = ds_gid.to_dataframe() - df_res = func(weather_df=df_weather, meta=meta_gid, **kwargs) ds_res = xr.Dataset.from_dataframe(df_res) @@ -170,14 +173,15 @@ def analysis(weather_ds, meta_df, func, template=None, **func_kwargs): stacked = weather_ds.map_blocks(calc_block, kwargs=kwargs, template=template).compute() - lats = stacked.latitude.values.flatten() - lons = stacked.longitude.values.flatten() - #stacked = stacked.drop(['gid']) - stacked = stacked.drop_vars(['latitude', 'longitude']) - stacked.coords['gid'] = pd.MultiIndex.from_arrays([lats, lons], names=['latitude', 'longitude']) - - res = stacked.unstack('gid', sparse=True) + # lats = stacked.latitude.values.flatten() + # lons = stacked.longitude.values.flatten() + stacked = stacked.drop(['gid']) + # stacked = stacked.drop_vars(['latitude', 'longitude']) + stacked.coords['gid'] = pd.MultiIndex.from_arrays([ + meta_df['latitude'], meta_df['longitude']], + names=['latitude', 'longitude']) + res = stacked.unstack('gid') #, sparse=True return res @@ -210,7 +214,7 @@ def output_template(ds_gids, shapes, attrs=dict(), add_dims=dict()): output_template = xr.Dataset( data_vars = {var: (dim, da.empty([dims_size[d] for d in dim]) ) for var, dim in shapes.items()}, - coords = {'gid': ds_gids['gid']}, + coords = {dim: ds_gids[dim] for dim in dims}, attrs = attrs ).chunk({dim: ds_gids.chunks[dim] for dim in dims}) @@ -227,16 +231,16 @@ def template_parameters(func): Dictionary of variable names and their associated dimensions. attrs : dict Dictionary of attributes for each variable (e.g. units). + add_dims : dict + Dictionary of dimensions to add to the output template. """ if func == standards.standoff: shapes = {'x': ('gid',), - 'T98_inf': ('gid',), - 'T98_0': ('gid',), - 'latitude': ('gid',), - 'longitude':('gid',), - } + 'T98_inf': ('gid',), + 'T98_0': ('gid',), + } attrs = {'x' : {'units': 'cm'}, 'T98_0' : {'units': 'Celsius'}, @@ -263,4 +267,45 @@ def template_parameters(func): 'attrs': attrs, 'add_dims': add_dims} - return parameters \ No newline at end of file + return parameters + + +def plot_USA(xr_res, + cmap='viridis', + vmin=None, + vmax=None, + title=None, + cb_title=None, + fp=None): + + fig = plt.figure() + ax = fig.add_axes([0, 0, 1, 1], + projection=ccrs.LambertConformal(), + frameon=False) + ax.patch.set_visible(False) + ax.set_extent([-120, -74, 22, 50], ccrs.Geodetic()) + + shapename = 'admin_1_states_provinces_lakes' + states_shp = shpreader.natural_earth( + resolution='110m', category='cultural', name=shapename) + ax.add_geometries( + shpreader.Reader(states_shp).geometries(), + ccrs.PlateCarree(), facecolor='w', edgecolor='gray') + + cm = xr_res.plot(transform=ccrs.PlateCarree(), + zorder=10, + add_colorbar=False, + cmap=cmap, + vmin=vmin, + vmax=vmax, + subplot_kws={"projection": ccrs.LambertConformal( + central_longitude=-95, central_latitude=45)}) + + cb = plt.colorbar(cm, shrink=0.5) + cb.set_label(cb_title) + ax.set_title(title) + + if fp is not None: + plt.savefig(fp, dpi=600) + + return fig, ax diff --git a/pvdeg/standards.py b/pvdeg/standards.py index 59b045f9..ea5661f6 100644 --- a/pvdeg/standards.py +++ b/pvdeg/standards.py @@ -152,9 +152,7 @@ def standoff( res = {'x': x, 'T98_0': T98_0, - 'T98_inf': T98_inf, - 'latitude': meta['latitude'], - 'longitude': meta['longitude']} + 'T98_inf': T98_inf} df_res = pd.DataFrame.from_dict(res, orient='index').T diff --git a/pvdeg/weather.py b/pvdeg/weather.py index f5672813..135d4152 100644 --- a/pvdeg/weather.py +++ b/pvdeg/weather.py @@ -264,6 +264,10 @@ def ini_h5_geospatial(fps): ds = xr.merge(dss) ds = xr.decode_cf(ds) + + # Rechunk time axis + ds = ds.chunk(chunks={'time': -1, 'gid': ds.chunks['gid']}) + weather_ds = ds return weather_ds, meta_df @@ -308,9 +312,6 @@ def get_NSRDB_fnames(satellite, names, NREL_HPC = False, **_): hpc_fp = '/nrel/nsrdb/' hsds = True - if type(names) in [int, float]: - nsrdb_fp = os.path.join(hpc_fp, sat_map[satellite], '*_{}.h5'.format(int(names))) - if type(names) in [int, float]: nsrdb_fp = os.path.join(hpc_fp, sat_map[satellite], '*_{}.h5'.format(int(names))) nsrdb_fnames = glob.glob(nsrdb_fp) diff --git a/pvdeg_tutorials/tutorials/6 - Geospatial Analysis.ipynb b/pvdeg_tutorials/tutorials/6 - Geospatial Analysis.ipynb deleted file mode 100644 index 1cabd53c..00000000 --- a/pvdeg_tutorials/tutorials/6 - Geospatial Analysis.ipynb +++ /dev/null @@ -1,878 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 6 - Geospatial analysis pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2019-06-13T20:12:46.350659Z", - "start_time": "2019-06-13T20:11:46.936643Z" - } - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import pvdeg\n", - "import csv\n", - "import h5py" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import dask\n", - "import dask.array as da\n", - "import dask.dataframe as dd\n", - "import xarray as xr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Single location example" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Get weather data\n", - "weather_db = 'NSRDB'\n", - "weather_id = (39.741931, -105.169891)\n", - "#weather_id = 1933572\n", - "weather_arg = {'satellite': 'Americas',\n", - " 'names': 2021,\n", - " 'NREL_HPC': True,\n", - " 'attributes': ['air_temperature', 'wind_speed', 'dhi', 'ghi', 'dni', 'relative_humidity']}\n", - "\n", - "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "standoff = pvdeg.standards.standoff(weather_df=weather_df, meta=meta)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Geospatial example" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Get weather data\n", - "weather_db = 'NSRDB'\n", - "weather_id = (39.741931, -105.169891)\n", - 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\n", - " Local directory: /tmp/dask-scratch-space-132739/worker-k4hfvjfn\n", - "
\n", - " Tasks executing: \n", - " \n", - " Tasks in memory: \n", - "
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" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pvdeg.geospatial.start_dask()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "meta_test = meta_USA.iloc[0:10000]\n", - "weather_test = weather_USA.sel(gid=meta_test.index)\n", - "\n", - "geo = {'func': pvdeg.standards.standoff,\n", - " 'weather_ds': weather_test,\n", - " 'meta_df': meta_test}\n", - "\n", - "standoff_res = pvdeg.geospatial.analysis(**geo)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import cartopy.crs as ccrs\n", - "\n", - "air = xr.tutorial.open_dataset(\"air_temperature\").air\n", - "\n", - "p = air.isel(time=0).plot(\n", - " subplot_kws=dict(projection=ccrs.Orthographic(-80, 35), facecolor=\"gray\"),\n", - " transform=ccrs.PlateCarree(),)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/pvdeg_tutorials/tutorials/ASTM Live Demo.ipynb b/pvdeg_tutorials/tutorials/ASTM Live Demo.ipynb index cf1cb1b8..99038eed 100644 --- a/pvdeg_tutorials/tutorials/ASTM Live Demo.ipynb +++ b/pvdeg_tutorials/tutorials/ASTM Live Demo.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -176,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -184,7 +184,7 @@ " latitude=33.4484, longitude=-112.0740,\n", " api_key=NREL_API_KEY,\n", " email='silvana.ovaitt@nrel.gov', # <-- any email works here fine\n", - " names='2020',\n", + " names='2021',\n", " map_variables=True,\n", " attributes=[],\n", " leap_day=False)\n" @@ -192,27 +192,322 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'Source': 'NSRDB',\n", + " 'Location ID': '323705',\n", + " 'City': '-',\n", + " 'State': '-',\n", + " 'Country': '-',\n", + " 'Time Zone': -7,\n", + " 'Local Time Zone': -7,\n", + " 'Clearsky DHI Units': 'w/m2',\n", + " 'Clearsky DNI Units': 'w/m2',\n", + " 'Clearsky GHI Units': 'w/m2',\n", + " 'Dew Point Units': 'c',\n", + " 'DHI Units': 'w/m2',\n", + " 'DNI Units': 'w/m2',\n", + " 'GHI Units': 'w/m2',\n", + " 'Solar Zenith Angle Units': 'Degree',\n", + " 'Temperature Units': 'c',\n", + " 'Pressure Units': 'mbar',\n", + " 'Relative Humidity Units': '%',\n", + " 'Precipitable Water Units': 'cm',\n", + " 'Wind Direction Units': 'Degrees',\n", + " 'Wind Speed Units': 'm/s',\n", + " 'Cloud Type -15': 'N/A',\n", + " 'Cloud Type 0': 'Clear',\n", + " 'Cloud Type 1': 'Probably Clear',\n", + " 'Cloud Type 2': 'Fog',\n", + " 'Cloud Type 3': 'Water',\n", + " 'Cloud Type 4': 'Super-Cooled Water',\n", + " 'Cloud Type 5': 'Mixed',\n", + " 'Cloud Type 6': 'Opaque Ice',\n", + " 'Cloud Type 7': 'Cirrus',\n", + " 'Cloud Type 8': 'Overlapping',\n", + " 'Cloud Type 9': 'Overshooting',\n", + " 'Cloud Type 10': 'Unknown',\n", + " 'Cloud Type 11': 'Dust',\n", + " 'Cloud Type 12': 'Smoke',\n", + " 'Fill Flag 0': 'N/A',\n", + " 'Fill Flag 1': 'Missing Image',\n", + " 'Fill Flag 2': 'Low Irradiance',\n", + " 'Fill Flag 3': 'Exceeds Clearsky',\n", + " 'Fill Flag 4': 'Missing CLoud Properties',\n", + " 'Fill Flag 5': 'Rayleigh Violation',\n", + " 'Surface Albedo Units': 'N/A',\n", + " 'Version': 'v3.2.2',\n", + " 'latitude': 33.45,\n", + " 'longitude': -112.06,\n", + " 'altitude': 334}" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "meta" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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5 rows × 24 columns

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" + ], + "text/plain": [ + " Year Month Day Hour Minute temp_air \\\n", + "2021-01-01 00:30:00-07:00 2021 1 1 0 30 6.5 \n", + "2021-01-01 01:30:00-07:00 2021 1 1 1 30 6.0 \n", + "2021-01-01 02:30:00-07:00 2021 1 1 2 30 5.5 \n", + "2021-01-01 03:30:00-07:00 2021 1 1 3 30 5.1 \n", + "2021-01-01 04:30:00-07:00 2021 1 1 4 30 4.7 \n", + "\n", + " dhi_clear dni_clear ghi_clear Cloud Type ... \\\n", + "2021-01-01 00:30:00-07:00 0.0 0.0 0.0 0 ... \n", + "2021-01-01 01:30:00-07:00 0.0 0.0 0.0 4 ... \n", + "2021-01-01 02:30:00-07:00 0.0 0.0 0.0 4 ... \n", + "2021-01-01 03:30:00-07:00 0.0 0.0 0.0 4 ... \n", + "2021-01-01 04:30:00-07:00 0.0 0.0 0.0 0 ... \n", + "\n", + " ghi relative_humidity solar_zenith albedo \\\n", + "2021-01-01 00:30:00-07:00 0.0 35.09 169.51 0.16 \n", + "2021-01-01 01:30:00-07:00 0.0 36.34 163.48 0.16 \n", + "2021-01-01 02:30:00-07:00 0.0 37.37 152.07 0.16 \n", + "2021-01-01 03:30:00-07:00 0.0 38.47 139.71 0.16 \n", + "2021-01-01 04:30:00-07:00 0.0 39.97 127.21 0.16 \n", + "\n", + " pressure precipitable_water wind_direction \\\n", + "2021-01-01 00:30:00-07:00 968.0 1.0 38.0 \n", + "2021-01-01 01:30:00-07:00 968.0 1.0 42.0 \n", + "2021-01-01 02:30:00-07:00 968.0 1.0 45.0 \n", + "2021-01-01 03:30:00-07:00 968.0 1.0 46.0 \n", + "2021-01-01 04:30:00-07:00 969.0 1.0 46.0 \n", + "\n", + " wind_speed \\\n", + "2021-01-01 00:30:00-07:00 1.8 \n", + "2021-01-01 01:30:00-07:00 1.8 \n", + "2021-01-01 02:30:00-07:00 1.8 \n", + "2021-01-01 03:30:00-07:00 1.7 \n", + "2021-01-01 04:30:00-07:00 1.8 \n", + "\n", + " Global Horizontal UV Irradiance (280-400nm) \\\n", + "2021-01-01 00:30:00-07:00 0.0 \n", + "2021-01-01 01:30:00-07:00 0.0 \n", + "2021-01-01 02:30:00-07:00 0.0 \n", + "2021-01-01 03:30:00-07:00 0.0 \n", + "2021-01-01 04:30:00-07:00 0.0 \n", + "\n", + " Global Horizontal UV Irradiance (295-385nm) \n", + "2021-01-01 00:30:00-07:00 0.0 \n", + "2021-01-01 01:30:00-07:00 0.0 \n", + "2021-01-01 02:30:00-07:00 0.0 \n", + "2021-01-01 03:30:00-07:00 0.0 \n", + "2021-01-01 04:30:00-07:00 0.0 \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "weather_df.head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax1 = plt.subplots(figsize=(9, 6))\n", "# Instantiate a second axes that shares the same x-axis\n", @@ -237,18 +532,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "standoff = pvdeg.standards.calc_standoff(weather_df=weather_df, meta=meta)" + "standoff = pvdeg.standards.standoff(weather_df=weather_df, meta=meta)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Minimum installation distance: 0 10.055513\n", + "Name: x, dtype: float64\n" + ] + } + ], "source": [ "print(\"Minimum installation distance:\", standoff['x'])" ] @@ -272,11 +576,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ - "standoff = pvdeg.standards.calc_standoff(weather_df=weather_df, meta=meta,\n", + "standoff = pvdeg.standards.standoff(weather_df=weather_df, meta=meta,\n", " level=2,\n", " tilt=None,\n", " azimuth=180,\n", @@ -289,17 +593,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Minimum installation distance: 0 3.760911\n", + "Name: x, dtype: float64\n" + ] + } + ], "source": [ "print(\"Minimum installation distance:\", standoff['x'])" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -313,7 +633,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/pvdeg_tutorials/tutorials/DuraMAT Live Demo.ipynb b/pvdeg_tutorials/tutorials/DuraMAT Live Demo.ipynb new file mode 100644 index 00000000..eeef2b9f --- /dev/null +++ b/pvdeg_tutorials/tutorials/DuraMAT Live Demo.ipynb @@ -0,0 +1,507 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DuraMAT Workshop Live Demo - Geospatial analysis\n", + "\n", + "![PVDeg Logo](../PVD_logo.png)\n", + "\n", + "***\n", + "2023.09.26\n", + "***\n", + "\n", + "**Steps:**\n", + "1. Initialize weather data into xarray\n", + "2. Calculate installation standoff for New Mexico\n", + "3. Plot results\n", + "\n", + "**Xarray: multi-dimensional data frame**\n", + "\n", + "![Xarray](./images/xarray.webp)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-13T20:12:46.350659Z", + "start_time": "2019-06-13T20:11:46.936643Z" + } + }, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'pvdeg'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpvdeg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mda\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataframe\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pvdeg'" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pvdeg\n", + "import dask.array as da\n", + "import dask.dataframe as dd\n", + "import xarray as xr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 Start distributed compute cluster - DASK" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "pvdeg.geospatial.start_dask()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "# Get weather data\n", + "weather_db = 'NSRDB'\n", + "\n", + "weather_arg = {'satellite': 'Americas',\n", + " 'names': 2022,\n", + " 'NREL_HPC': True,\n", + " 'attributes': ['air_temperature', 'wind_speed', 'dhi', 'ghi', 'dni', 'relative_humidity']}\n", + "\n", + "weather_ds, meta_df = pvdeg.weather.get(weather_db, geospatial=True, **weather_arg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "weather_ds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "meta_df['state'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "meta_NM = meta_df[meta_df['state'] == 'New Mexico']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "meta_NM_sub, gids_NM_sub = pvdeg.utilities.gid_downsampling(meta_NM, 4)\n", + "weather_NM_sub = weather_ds.sel(gid=meta_NM_sub.index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "geo = {'func': pvdeg.standards.standoff,\n", + " 'weather_ds': weather_NM_sub,\n", + " 'meta_df': meta_NM_sub}\n", + "\n", + "standoff_res = pvdeg.geospatial.analysis(**geo)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "standoff_res" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "fig, ax = pvdeg.geospatial.plot_USA(standoff_res['x'], \n", + " cmap='viridis', vmin=0, vmax=None, \n", + " title='Minimum estimated air standoff to qualify as level 1 system', \n", + " cb_title='Standoff (cm)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Relative Humidity Example - Time dimension" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "# State bar of new mexico: (35.16482, -106.58979)\n", + "\n", + "weather_db = 'NSRDB'\n", + "weather_id = (35.16482, -106.58979) #NREL (39.741931, -105.169891)\n", + "weather_arg = {'satellite': 'Americas',\n", + " 'names': 2022,\n", + " 'NREL_HPC': True,\n", + " 'attributes': ['air_temperature', 'wind_speed', 'dhi', 'ghi', 'dni', 'relative_humidity']}\n", + "\n", + "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, geospatial=False, **weather_arg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "RH_module = pvdeg.humidity.module(weather_df=weather_df, meta=meta)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "RH_module" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "RH_module.plot(ls='--')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "geo = {'func': pvdeg.humidity.module,\n", + " 'weather_ds': weather_NM_sub,\n", + " 'meta_df': meta_NM_sub}\n", + "\n", + "RH_module = pvdeg.geospatial.analysis(**geo)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "RH_module" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "from matplotlib.animation import FuncAnimation\n", + "from matplotlib.animation import PillowWriter\n", + "import matplotlib.animation as animation\n", + "import datetime\n", + "ims = []\n", + "for n in range(1, 13):\n", + " for i, np_t in enumerate(RH_module.time):\n", + " t = pd.Timestamp(np_t.values).time()\n", + " d = pd.Timestamp(np_t.values).day\n", + " m = pd.Timestamp(np_t.values).month\n", + " if m == n:\n", + " if d == 15:\n", + " if t == datetime.time(12):\n", + " fig, ax = pvdeg.geospatial.plot_USA(RH_module['RH_surface_outside'].sel(time=np_t),\n", + " cmap='viridis', vmin=0, vmax=100, \n", + " title=f'RH_surface_outside - {d} 12:00', \n", + " cb_title='Relative humidity (%)')\n", + " im = plt.show()\n", + " ims.append([im])\n", + "\n", + "fig = plt.figure()\n", + "ani = animation.ArtistAnimation(fig, ims, interval=1000, blit=True,\n", + " repeat_delay=1000)\n", + "\n", + "ani.save('./images/RH_animation.gif', writer=PillowWriter(fps=1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "from IPython.display import HTML\n", + "HTML('')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mFailed to start the Kernel. \n", + "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "RH_module.sel(latitude=35.16, longitude=-106.58, method='nearest')['RH_front_encap'].plot()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + 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b/pvdeg_tutorials/tutorials/ASTM Live Demo.ipynb index 99038eed..7f6a6e30 100644 --- a/pvdeg_tutorials/tutorials/ASTM Live Demo.ipynb +++ b/pvdeg_tutorials/tutorials/ASTM Live Demo.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -176,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -192,7 +192,430 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 13, + "metadata": {}, + 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2021-01-01 01:30:00-07:002021111306.00.00.00.04...0.036.34163.480.16968.01.042.01.80.00.0
2021-01-01 02:30:00-07:002021112305.50.00.00.04...0.037.37152.070.16968.01.045.01.80.00.0
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2021-01-01 04:30:00-07:002021114304.70.00.00.00...0.039.97127.210.16969.01.046.01.80.00.0
..................................................................
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" + ], + "text/plain": [ + " Year Month Day Hour Minute temp_air \\\n", + "2021-01-01 00:30:00-07:00 2021 1 1 0 30 6.5 \n", + "2021-01-01 01:30:00-07:00 2021 1 1 1 30 6.0 \n", + "2021-01-01 02:30:00-07:00 2021 1 1 2 30 5.5 \n", + "2021-01-01 03:30:00-07:00 2021 1 1 3 30 5.1 \n", + "2021-01-01 04:30:00-07:00 2021 1 1 4 30 4.7 \n", + "... ... ... ... ... ... ... \n", + "2021-12-31 19:30:00-07:00 2021 12 31 19 30 9.1 \n", + "2021-12-31 20:30:00-07:00 2021 12 31 20 30 8.5 \n", + "2021-12-31 21:30:00-07:00 2021 12 31 21 30 7.8 \n", + "2021-12-31 22:30:00-07:00 2021 12 31 22 30 7.4 \n", + "2021-12-31 23:30:00-07:00 2021 12 31 23 30 7.0 \n", + "\n", + " dhi_clear dni_clear ghi_clear Cloud Type ... \\\n", + "2021-01-01 00:30:00-07:00 0.0 0.0 0.0 0 ... \n", + "2021-01-01 01:30:00-07:00 0.0 0.0 0.0 4 ... \n", + "2021-01-01 02:30:00-07:00 0.0 0.0 0.0 4 ... \n", + "2021-01-01 03:30:00-07:00 0.0 0.0 0.0 4 ... \n", + "2021-01-01 04:30:00-07:00 0.0 0.0 0.0 0 ... \n", + "... ... ... ... ... ... \n", + "2021-12-31 19:30:00-07:00 0.0 0.0 0.0 7 ... \n", + "2021-12-31 20:30:00-07:00 0.0 0.0 0.0 7 ... \n", + "2021-12-31 21:30:00-07:00 0.0 0.0 0.0 7 ... \n", + "2021-12-31 22:30:00-07:00 0.0 0.0 0.0 7 ... \n", + "2021-12-31 23:30:00-07:00 0.0 0.0 0.0 0 ... \n", + "\n", + " ghi relative_humidity solar_zenith albedo \\\n", + "2021-01-01 00:30:00-07:00 0.0 35.09 169.51 0.16 \n", + "2021-01-01 01:30:00-07:00 0.0 36.34 163.48 0.16 \n", + "2021-01-01 02:30:00-07:00 0.0 37.37 152.07 0.16 \n", + "2021-01-01 03:30:00-07:00 0.0 38.47 139.71 0.16 \n", + "2021-01-01 04:30:00-07:00 0.0 39.97 127.21 0.16 \n", + "... ... ... ... ... \n", + "2021-12-31 19:30:00-07:00 0.0 27.14 114.11 0.16 \n", + "2021-12-31 20:30:00-07:00 0.0 28.57 126.45 0.16 \n", + "2021-12-31 21:30:00-07:00 0.0 29.85 138.95 0.16 \n", + "2021-12-31 22:30:00-07:00 0.0 31.44 151.32 0.16 \n", + "2021-12-31 23:30:00-07:00 0.0 33.22 162.85 0.16 \n", + "\n", + " pressure precipitable_water wind_direction \\\n", + "2021-01-01 00:30:00-07:00 968.0 1.0 38.0 \n", + "2021-01-01 01:30:00-07:00 968.0 1.0 42.0 \n", + "2021-01-01 02:30:00-07:00 968.0 1.0 45.0 \n", + "2021-01-01 03:30:00-07:00 968.0 1.0 46.0 \n", + "2021-01-01 04:30:00-07:00 969.0 1.0 46.0 \n", + "... ... ... ... \n", + "2021-12-31 19:30:00-07:00 967.0 0.9 28.0 \n", + "2021-12-31 20:30:00-07:00 967.0 0.9 31.0 \n", + "2021-12-31 21:30:00-07:00 967.0 0.9 33.0 \n", + "2021-12-31 22:30:00-07:00 968.0 1.0 33.0 \n", + "2021-12-31 23:30:00-07:00 968.0 1.0 34.0 \n", + "\n", + " wind_speed \\\n", + "2021-01-01 00:30:00-07:00 1.8 \n", + "2021-01-01 01:30:00-07:00 1.8 \n", + "2021-01-01 02:30:00-07:00 1.8 \n", + "2021-01-01 03:30:00-07:00 1.7 \n", + "2021-01-01 04:30:00-07:00 1.8 \n", + "... ... \n", + "2021-12-31 19:30:00-07:00 1.2 \n", + "2021-12-31 20:30:00-07:00 1.3 \n", + "2021-12-31 21:30:00-07:00 1.4 \n", + "2021-12-31 22:30:00-07:00 1.5 \n", + "2021-12-31 23:30:00-07:00 1.6 \n", + "\n", + " Global Horizontal UV Irradiance (280-400nm) \\\n", + "2021-01-01 00:30:00-07:00 0.0 \n", + "2021-01-01 01:30:00-07:00 0.0 \n", + "2021-01-01 02:30:00-07:00 0.0 \n", + "2021-01-01 03:30:00-07:00 0.0 \n", + "2021-01-01 04:30:00-07:00 0.0 \n", + "... ... \n", + "2021-12-31 19:30:00-07:00 0.0 \n", + "2021-12-31 20:30:00-07:00 0.0 \n", + "2021-12-31 21:30:00-07:00 0.0 \n", + "2021-12-31 22:30:00-07:00 0.0 \n", + "2021-12-31 23:30:00-07:00 0.0 \n", + "\n", + " Global Horizontal UV Irradiance (295-385nm) \n", + "2021-01-01 00:30:00-07:00 0.0 \n", + "2021-01-01 01:30:00-07:00 0.0 \n", + "2021-01-01 02:30:00-07:00 0.0 \n", + "2021-01-01 03:30:00-07:00 0.0 \n", + "2021-01-01 04:30:00-07:00 0.0 \n", + "... ... \n", + "2021-12-31 19:30:00-07:00 0.0 \n", + "2021-12-31 20:30:00-07:00 0.0 \n", + "2021-12-31 21:30:00-07:00 0.0 \n", + "2021-12-31 22:30:00-07:00 0.0 \n", + "2021-12-31 23:30:00-07:00 0.0 \n", + "\n", + "[8760 rows x 24 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weather_df" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -246,7 +669,7 @@ " 'altitude': 334}" ] }, - "execution_count": 27, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -257,7 +680,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -483,7 +906,7 @@ "[5 rows x 24 columns]" ] }, - "execution_count": 28, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -494,7 +917,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -532,7 +955,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -541,14 +964,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Minimum installation distance: 0 10.055513\n", + "Minimum installation distance: 0 8.260315\n", "Name: x, dtype: float64\n" ] } @@ -576,7 +999,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -593,14 +1016,14 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Minimum installation distance: 0 3.760911\n", + "Minimum installation distance: 0 3.029357\n", "Name: x, dtype: float64\n" ] } @@ -619,9 +1042,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "pvdeg (py310)", "language": "python", - "name": "python3" + "name": "py310" }, "language_info": { "codemirror_mode": { @@ -633,7 +1056,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/pvdeg_tutorials/tutorials/DuraMAT Live Demo.ipynb b/pvdeg_tutorials/tutorials/DuraMAT Live Demo.ipynb index eeef2b9f..3b4746a5 100644 --- a/pvdeg_tutorials/tutorials/DuraMAT Live Demo.ipynb +++ b/pvdeg_tutorials/tutorials/DuraMAT Live Demo.ipynb @@ -24,26 +24,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2019-06-13T20:12:46.350659Z", "start_time": "2019-06-13T20:11:46.936643Z" } }, - 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+       "  * time               (time) datetime64[ns] 2022-01-01 ... 2022-12-31T23:30:00\n",
+       "Data variables:\n",
+       "    temp_air           (time, gid) float32 dask.array<chunksize=(17520, 500), meta=np.ndarray>\n",
+       "    wind_speed         (time, gid) float32 dask.array<chunksize=(17520, 500), meta=np.ndarray>\n",
+       "    dhi                (time, gid) float32 dask.array<chunksize=(17520, 500), meta=np.ndarray>\n",
+       "    ghi                (time, gid) float32 dask.array<chunksize=(17520, 500), meta=np.ndarray>\n",
+       "    dni                (time, gid) float32 dask.array<chunksize=(17520, 500), meta=np.ndarray>\n",
+       "    relative_humidity  (time, gid) float32 dask.array<chunksize=(17520, 500), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    full_version_record:  {"rex": "0.2.80", "pandas": "2.0.0", "numpy": "1.23...\n",
+       "    package:              rex\n",
+       "    version:              4.0.0
" + ], + "text/plain": [ + "\n", + "Dimensions: (time: 17520, gid: 2018267)\n", + "Coordinates:\n", + " * gid (gid) int64 0 1 2 3 4 ... 2018263 2018264 2018265 2018266\n", + " * time (time) datetime64[ns] 2022-01-01 ... 2022-12-31T23:30:00\n", + "Data variables:\n", + " temp_air (time, gid) float32 dask.array\n", + " wind_speed (time, gid) float32 dask.array\n", + " dhi (time, gid) float32 dask.array\n", + " ghi (time, gid) float32 dask.array\n", + " dni (time, gid) float32 dask.array\n", + " relative_humidity (time, gid) float32 dask.array\n", + "Attributes:\n", + " full_version_record: {\"rex\": \"0.2.80\", \"pandas\": \"2.0.0\", \"numpy\": \"1.23...\n", + " package: rex\n", + " version: 4.0.0" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -131,18 +1778,166 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mFailed to start the Kernel. \n", - "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", - "\u001b[1;31mView Jupyter log for further details." - ] + "data": { + "text/plain": [ + "array([nan, 'Northern', 'Eastern', 'Alaska', 'Alo', 'Singave', 'Midway',\n", + " 'Uvéa', \"Vava'u\", \"Ha'apai\", 'Niuas', 'Vaisigano', \"Satupa'itea\",\n", + " 'Gagaifomauga', 'Palauli', \"Gaga'emauga\", \"Fa'asaleleaga\",\n", + " 'Aiga-i-le-Tai', \"A'ana\", 'Tuamasaga', 'Atua', \"Va'a-o-Fonoti\",\n", + " 'Western', \"Manu'a\", 'Palmyra', 'Hawaii', 'Îles Sous-le-Vent',\n", + " 'Îles du Vent', 'Îles Tuamotu-Gambier', 'Îles Marquises',\n", + " 'British Columbia', 'Washington', 'Oregon', 'California',\n", + " 'Alberta', 'Nevada', 'Baja California', 'Idaho', 'Montana',\n", + " 'Baja California Sur', 'Sonora', 'Arizona', 'Utah', 'Wyoming',\n", + " 'Saskatchewan', 'Sinaloa', 'Colorado', 'New Mexico', 'Chihuahua',\n", + " 'Durango', 'Texas', 'Nayarit', 'Jalisco', 'Colima', 'Zacatecas',\n", + " 'North Dakota', 'South Dakota', 'Nebraska', 'Coahuila',\n", + " 'Michoacán', 'Oklahoma', 'Aguascalientes', 'San Luis Potosí',\n", + " 'Guerrero', 'Guanajuato', 'Kansas', 'Manitoba', 'Nuevo León',\n", + " 'Querétaro', 'México', 'Tamaulipas', 'Hidalgo', 'Morelos',\n", + " 'Distrito Federal', 'Puebla', 'Veracruz', 'Tlaxcala', 'Oaxaca',\n", + " 'Minnesota', 'Iowa', 'Missouri', 'Ontario', 'Arkansas', 'Chiapas',\n", + " 'Tabasco', 'Louisiana', 'Wisconsin', 'Campeche', 'San Marcos',\n", + " 'Retalhuleu', 'Huehuetenango', 'Quezaltenango', 'Suchitepéquez',\n", + " 'Mississippi', 'Galápagos', 'Totonicapán', 'Illinois', 'Sololá',\n", + " 'Escuintla', 'Petén', 'Quiché', 'Chimaltenango', 'Sacatepéquez',\n", + " 'Alta Verapaz', 'Baja Verapaz', 'Guatemala', 'Santa Rosa',\n", + " 'Michigan', 'Yucatán', 'El Progreso', 'Tennessee', 'Jalapa',\n", + " 'Jutiapa', 'Ahuachapán', 'Sonsonate', 'Zacapa', 'Chiquimula',\n", + " 'Santa Ana', 'Izabal', 'La Libertad', 'Kentucky', 'Chalatenango',\n", + " 'Quintana Roo', 'Ocotepeque', 'San Salvador', 'Toledo', 'Copán',\n", + " 'Cayo', 'Orange Walk', 'Cuscatlán', 'La Paz', 'Cabañas', 'Lempira',\n", + " 'San Vicente', 'Usulután', 'Stann Creek', 'Santa Bárbara',\n", + " 'Belize', 'Corozal', 'Intibucá', 'San Miguel', 'Alabama', 'Cortés',\n", + " 'Morazán', 'Indiana', 'Comayagua', 'La Unión', 'Yoro', 'Atlántida',\n", + " 'Valle', 'Chinandega', 'Florida', 'Francisco Morazán', 'Choluteca',\n", + " 'El Paraíso', 'León', 'Puntarenas', 'Islas de la Bahía', 'Olancho',\n", + " 'Nueva Segovia', 'Madriz', 'Estelí', 'Managua', 'Colón', 'Carazo',\n", + " 'Matagalpa', 'Masaya', 'Jinotega', 'Rivas', 'Granada',\n", + " 'Lago Nicaragua', 'Boaco', 'Guanacaste', 'Chontales', 'Georgia',\n", + " 'Atlántico Norte', 'Alajuela', 'Atlántico Sur', 'Río San Juan',\n", + " 'Gracias a Dios', 'Pinar del Río', 'Ohio', 'San José',\n", + " 'North Carolina', 'Heredia', 'Cartago', 'Limón', 'Virginia',\n", + " 'South Carolina', 'Isla de la Juventud', 'Chiriquí', 'La Habana',\n", + " 'Bocas del Toro', 'West Virginia', 'Ciudad de la Habana',\n", + " 'Mayabeque', 'Ngöbe Buglé', 'Matanzas', 'Veraguas',\n", + " 'San Andrés y Providencia', 'West Bay', 'George Town',\n", + " 'North Side', 'Piura', 'East End', 'Tumbes', 'Herrera',\n", + " 'Santa Elena', 'Cienfuegos', 'Manabi', 'Coclé', 'Villa Clara',\n", + " 'Los Santos', 'Québec', 'Lambayeque', 'Guayas', 'Pennsylvania',\n", + " 'Loja', 'El Oro', 'Panamá Oeste', 'Sancti Spíritus', 'Esmeraldas',\n", + " 'Little Cayman', 'Cayman Brac', 'Los Rios', 'New York', 'Azuay',\n", + " 'Panamá', 'Santo Domingo de los Tsachilas', 'Maryland', 'Cañar',\n", + " 'Zamora Chinchipe', 'Cajamarca', 'Bolivar', 'Camagüey',\n", + " 'Pichincha', 'Cotopaxi', 'Nunavut', 'Biminis', 'Kuna Yala',\n", + " 'Imbabura', 'Ciego de Ávila', 'Chimborazo', 'West Grand Bahama',\n", + " 'Nariño', 'Morona Santiago', 'Tungurahua', 'Amazonas',\n", + " 'City of Freeport', 'Ancash', 'Carchi', 'Darién', 'North Andros',\n", + " 'East Grand Bahama', 'Napo', 'Westmoreland', 'Hanover', 'Emberá',\n", + " 'Central Andros', 'Valle del Cauca', 'Pastaza', 'Cauca',\n", + " 'Saint James', 'Saint Elizabeth', 'Sucumbios', 'Berry Islands',\n", + " 'Mangrove Cay', 'Chocó', 'Lima', 'South Andros', 'Las Tunas',\n", + " 'Loreto', 'North Abaco', 'Trelawny', 'San Martín', 'Granma',\n", + " 'Manchester', 'Orellana', \"Moore's Island\", 'South Abaco',\n", + " 'New Providence', 'Saint Ann', 'Clarendon', 'Central Abaco',\n", + " 'Huánuco', 'Saint Catherine', 'Lima Province', 'Callao',\n", + " 'Antioquia', 'Putumayo', 'District of Columbia', 'Saint Mary',\n", + " 'Hope Town', 'Santiago de Cuba', 'Saint Andrew', 'North Eleuthera',\n", + " 'Exuma', 'Kingston', 'Portland', 'Holguín', 'Pasco',\n", + " 'Saint Thomas', 'Huila', 'Córdoba', 'Junín', 'Ica',\n", + " 'South Eleuthera', 'Risaralda', 'Caquetá', 'Central Eleuthera',\n", + " 'Tolima', 'Cat Island', 'Caldas', 'Ucayali', 'Quindío',\n", + " 'Ragged Island', 'Delaware', 'Huancavelica', 'Sucre', 'Bolívar',\n", + " 'New Jersey', 'Guantánamo', 'Long Island', 'Atlántico', 'Ayacucho',\n", + " 'Navassa', 'Arequipa', 'Magdalena', 'Rum Cay', 'Cundinamarca',\n", + " 'Meta', 'Boyacá', 'Santander', \"Grand'Anse\", 'Sud',\n", + " 'Crooked Island', 'Acklins', 'Cesar', 'Acre', 'Cusco', 'Apurímac',\n", + " 'Nippes', 'Connecticut', 'Inagua', 'La Guajira', 'Guaviare',\n", + " 'Norte de Santander', 'Massachusetts', 'Vermont', 'Nord-Ouest',\n", + " 'Zulia', 'Ouest', 'Mayaguana', \"L'Artibonite\", 'Casanare',\n", + " 'Sud-Est', 'Nord', 'New Hampshire',\n", + " 'Providenciales and West Caicos', 'Táchira', 'Apure', 'Arauca',\n", + " 'Madre de Dios', 'Centre', 'Nord-Est', 'North Caicos', 'Vaupés',\n", + " 'Independencia', 'Mérida', 'Middle Caicos', 'La Estrelleta',\n", + " 'Rhode Island', 'Barinas', 'Monte Cristi', 'Dajabón', 'Pedernales',\n", + " 'South Caicos and East Caicos', 'San Juan', 'Bahoruco',\n", + " 'Santiago Rodríguez', 'Moquegua', 'Barahona', 'Puerto Plata',\n", + " 'Santiago', 'Falcón', 'Grand Turk', 'Valverde', 'Azua', 'Puno',\n", + " 'Tacna', 'Maine', 'Trujillo', 'Vichada', 'La Vega', 'Lara',\n", + " 'Guainía', 'San José de Ocoa', 'Espaillat', 'Monseñor Nouel',\n", + " 'Peravia', 'Salcedo', 'Duarte', 'Sánchez Ramírez', 'San Cristóbal',\n", + " 'Arica y Parinacota', 'Tarapacá', 'Monte Plata',\n", + " 'María Trinidad Sánchez', 'Portuguesa', 'Santo Domingo',\n", + " 'Distrito Nacional', 'Samaná', 'Hato Mayor',\n", + " 'San Pedro de Macorís', 'Pando', 'El Seybo', 'Yaracuy',\n", + " 'La Romana', 'Oruro', 'New Brunswick', 'Cojedes', 'La Altagracia',\n", + " 'Potosí', 'Antofagasta', 'Bonaire', 'Carabobo', 'Guárico',\n", + " 'Mayagüez', 'Aragua', 'Newfoundland and Labrador', 'El Beni',\n", + " 'Vargas', 'Rincón', 'Aguada', 'Añasco', 'Cabo Rojo', 'Aguadilla',\n", + " 'Miranda', 'Moca', 'Hormigueros', 'San Germán', 'Lajas',\n", + " 'Distrito Capital', 'Isabela', 'Las Marías', 'San Sebastián',\n", + " 'Maricao', 'Sabana Grande', 'Cochabamba', 'Quebradillas',\n", + " 'Guánica', 'Dependencias Federales', 'Camuy', 'Lares', 'Yauco',\n", + " 'Hatillo', 'Utuado', 'Adjuntas', 'Guayanilla', 'Rondônia',\n", + " 'Arecibo', 'Peñuelas', 'Ponce', 'Jayuya', 'Barceloneta', 'Ciales',\n", + " 'Manatí', 'Juana Díaz', 'Orocovis', 'Villalba', 'Morovis',\n", + " 'Vega Baja', 'Coamo', 'Santa Isabel', 'Nova Scotia', 'Vega Alta',\n", + " 'Barranquitas', 'Dorado', 'Toa Alta', 'Naranjito', 'Aibonito',\n", + " 'Salinas', 'Comerío', 'Toa Baja', 'Cidra', 'Cayey', 'Bayamón',\n", + " 'Aguas Buenas', 'Guayama', 'Cataño', 'Guaynabo', 'Caguas',\n", + " 'Patillas', 'Arroyo', 'Trujillo Alto', 'Gurabo', 'San Lorenzo',\n", + " 'Loíza', 'Carolina', 'Yabucoa', 'Maunabo', 'Juncos', 'Canóvanas',\n", + " 'Las Piedras', 'Río Grande', 'Humacao', 'Naguabo', 'Luquillo',\n", + " 'Fajardo', 'Anzoátegui', 'Ceiba', 'Chuquisaca', 'Vieques',\n", + " 'Culebra', 'Tarija', 'Southampton', 'Saint Croix', 'Roraima',\n", + " 'Santa Cruz', 'Saint John', 'Hamilton', 'Jost Van Dyke',\n", + " \"Saint George's\", 'Saint George municipality', 'Tortola',\n", + " 'Other Islands', 'Prince Edward Island', 'Anegada',\n", + " 'Nueva Esparta', 'Monagas', 'Sint Eustatius',\n", + " 'Saint Anne Sandy Point', 'Saint Paul Capisterre',\n", + " 'Saint Thomas Middle Island', 'Christ Church Nichola Town',\n", + " 'Saint Mary Cayon', 'Saint Peter Basseterre',\n", + " 'Saint George Basseterre', 'Saint Thomas Lowland',\n", + " 'Saint John Figtree', 'Saint James Windward',\n", + " 'Saint George Gingerland', 'Delta Amacuro', 'Boquerón',\n", + " 'Saint Peter', 'Saint Anthon', 'Saint Georges', 'Siparia',\n", + " 'Alto Paraguay', 'Barbuda', 'Saint Paul', 'Basse-Terre',\n", + " 'Saint George', 'Saint Philip', 'Diego Martin', 'Saint Mark',\n", + " 'Saint David', 'Saint Patrick', 'Point Fortin', 'Mato Grosso',\n", + " 'Pointe-à-Pitre', 'San Juan-Laventille', 'Port of Spain',\n", + " 'Penal-Debe', 'Chaguanas', 'Couva-Tabaquite-Talparo',\n", + " 'San Fernando', 'Saint Joseph', 'Tunapuna/Piarco', 'Princes Town',\n", + " 'Cuyuni-Mazaruni', 'Saint-Pierre', 'Grenadines', 'Sangre Grande',\n", + " 'Mayaro/Rio Claro', 'Le Trinité', 'Charlotte', 'Fort-de-France',\n", + " 'Le Marin', 'Soufrière', 'Choiseul', 'Castries', 'Anse-la-Raye',\n", + " 'Laborie', 'Micoud', 'Vieux Fort', 'Gros Islet', 'Dennery',\n", + " 'Tobago', 'Barima-Waini', 'Potaro-Siparuni',\n", + " 'Upper Takutu-Upper Essequibo', 'Saint Lucy', 'Saint Michael',\n", + " 'Christ Church', 'Santa Catarina', 'Pomeroon-Supenaam',\n", + " 'Upper Demerara-Berbice', 'Pará',\n", + " 'Essequibo Islands-West Demerara', 'East Berbice-Corentyne',\n", + " 'Demerara-Mahaica', 'Mahaica-Berbice', 'Mato Grosso do Sul',\n", + " 'Sipaliwini', 'Nickerie', 'Coronie', 'Miquelon-Langlade', 'Para',\n", + " 'Saramacca', 'Brokopondo', 'Wanica', 'Paramaribo', 'Commewijne',\n", + " 'Amapá', 'Marowijne', 'Saint-Laurent-du-Maroni', 'Cayenne',\n", + " 'Goiás', 'São Paulo', 'Minas Gerais', 'Tocantins', 'Maranhão',\n", + " 'Bahia', 'Piauí', 'Kujalleq', 'Paraíba', 'Rio de Janeiro',\n", + " 'Espírito Santo', 'Ceará', 'Pernambuco', 'Rio Grande do Norte',\n", + " 'Sergipe', 'Alagoas', 'Azores', 'Porto Novo', 'Ribeira Grande',\n", + " 'São Vicente', 'Paúl', 'Brava', 'São Filipe',\n", + " 'Tarrafal de São Nicolau', 'Mosteiros', 'Santa Catarina do Fogo ',\n", + " 'Ribeira Brava', 'Tarrafal', 'Ribeira Grande de Santiago',\n", + " 'São Miguel', 'São Salvador do Mundo', 'São Lourenço dos Órgãos',\n", + " 'São Domingos', 'Praia', 'Maio', 'Sal', 'Boa Vista'], dtype=object)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -151,40 +1946,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [ - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mFailed to start the Kernel. \n", - "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", - "\u001b[1;31mView Jupyter log for further details." - ] - } - ], + "outputs": [], "source": [ "meta_NM = meta_df[meta_df['state'] == 'New Mexico']" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [ - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mFailed to start the Kernel. \n", - "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", - "\u001b[1;31mView Jupyter log for further details." - ] - } - ], + "outputs": [], "source": [ "meta_NM_sub, gids_NM_sub = pvdeg.utilities.gid_downsampling(meta_NM, 4)\n", "weather_NM_sub = weather_ds.sel(gid=meta_NM_sub.index)" @@ -192,20 +1965,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mFailed to start the Kernel. \n", - "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", - "\u001b[1;31mView Jupyter log for further details." - ] - } - ], + "outputs": [], "source": [ "geo = {'func': pvdeg.standards.standoff,\n", " 'weather_ds': weather_NM_sub,\n", @@ -216,18 +1978,544 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [ { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mFailed to start the Kernel. \n", - "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", - "\u001b[1;31mView Jupyter log for further details." - ] + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "\n", + "Dimensions: (latitude: 18, longitude: 19)\n", + "Coordinates:\n", + " * latitude (latitude) float32 31.37 31.69 32.01 32.33 ... 36.17 36.49 36.81\n", + " * longitude (longitude) float32 -109.0 -108.7 -108.4 ... -103.9 -103.6 -103.3\n", + "Data variables:\n", + " x (latitude, longitude) float64 3.665 3.872 4.039 ... 3.403 3.744\n", + " T98_inf (latitude, longitude) float64 54.15 54.4 54.89 ... 53.41 54.45\n", + " T98_0 (latitude, longitude) float64 83.05 83.83 84.19 ... 82.39 83.18\n", + "Attributes:\n", + " x: {'units': 'cm'}\n", + " T98_0: {'units': 'Celsius'}\n", + " T98_inf: {'units': 'Celsius'}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -236,18 +2524,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [ { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mFailed to start the Kernel. \n", - "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", - "\u001b[1;31mView Jupyter log for further details." - ] + "data": { + "image/png": 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"... ... \n", + "2022-12-31 21:30:00+00:00 25.763630 \n", + "2022-12-31 22:00:00+00:00 34.031870 \n", + "2022-12-31 22:30:00+00:00 38.818731 \n", + "2022-12-31 23:00:00+00:00 44.944090 \n", + "2022-12-31 23:30:00+00:00 50.736736 \n", + "\n", + "[17520 rows x 4 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -335,18 +2735,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [ { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mFailed to start the Kernel. \n", - "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", - "\u001b[1;31mView Jupyter log for further details." - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -355,20 +2765,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [ - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mFailed to start the Kernel. \n", - "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", - "\u001b[1;31mView Jupyter log for further details." - ] - } - ], + "outputs": [], "source": [ "geo = {'func': pvdeg.humidity.module,\n", " 'weather_ds': weather_NM_sub,\n", @@ -379,18 +2778,594 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [ { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mFailed to start the Kernel. \n", - "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", - "\u001b[1;31mView Jupyter log for further details." - ] + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:             (latitude: 18, longitude: 19, time: 17520)\n",
+       "Coordinates:\n",
+       "  * latitude            (latitude) float32 31.37 31.69 32.01 ... 36.49 36.81\n",
+       "  * longitude           (longitude) float32 -109.0 -108.7 ... -103.6 -103.3\n",
+       "  * time                (time) datetime64[ns] 2022-01-01 ... 2022-12-31T23:30:00\n",
+       "Data variables:\n",
+       "    RH_surface_outside  (time, latitude, longitude) float64 77.43 ... 31.64\n",
+       "    RH_front_encap      (time, latitude, longitude) float64 33.46 ... 27.26\n",
+       "    RH_back_encap       (time, latitude, longitude) float64 77.43 ... 19.44\n",
+       "    RH_backsheet        (time, latitude, longitude) float64 77.43 ... 25.54
" + ], + "text/plain": [ + "\n", + "Dimensions: (latitude: 18, longitude: 19, time: 17520)\n", + "Coordinates:\n", + " * latitude (latitude) float32 31.37 31.69 32.01 ... 36.49 36.81\n", + " * longitude (longitude) float32 -109.0 -108.7 ... -103.6 -103.3\n", + " * time (time) datetime64[ns] 2022-01-01 ... 2022-12-31T23:30:00\n", + "Data variables:\n", + " RH_surface_outside (time, latitude, longitude) float64 77.43 ... 31.64\n", + " RH_front_encap (time, latitude, longitude) float64 33.46 ... 27.26\n", + " RH_back_encap (time, latitude, longitude) float64 77.43 ... 19.44\n", + " RH_backsheet (time, latitude, longitude) float64 77.43 ... 25.54" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -399,95 +3374,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [ - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mFailed to start the Kernel. \n", - "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", - "\u001b[1;31mView Jupyter log for further details." - ] - } - ], + "outputs": [], "source": [ - "from matplotlib.animation import FuncAnimation\n", - "from matplotlib.animation import PillowWriter\n", - "import matplotlib.animation as animation\n", - "import datetime\n", - "ims = []\n", - "for n in range(1, 13):\n", - " for i, np_t in enumerate(RH_module.time):\n", - " t = pd.Timestamp(np_t.values).time()\n", - " d = pd.Timestamp(np_t.values).day\n", - " m = pd.Timestamp(np_t.values).month\n", - " if m == n:\n", - " if d == 15:\n", - " if t == datetime.time(12):\n", - " fig, ax = pvdeg.geospatial.plot_USA(RH_module['RH_surface_outside'].sel(time=np_t),\n", - " cmap='viridis', vmin=0, vmax=100, \n", - " title=f'RH_surface_outside - {d} 12:00', \n", - " cb_title='Relative humidity (%)')\n", - " im = plt.show()\n", - " ims.append([im])\n", - "\n", - "fig = plt.figure()\n", - "ani = animation.ArtistAnimation(fig, ims, interval=1000, blit=True,\n", - " repeat_delay=1000)\n", + "# from matplotlib.animation import FuncAnimation\n", + "# from matplotlib.animation import PillowWriter\n", + "# import matplotlib.animation as animation\n", + "# import datetime\n", + "# ims = []\n", + "# for n in range(1, 13):\n", + "# for i, np_t in enumerate(RH_module.time):\n", + "# t = pd.Timestamp(np_t.values).time()\n", + "# d = pd.Timestamp(np_t.values).day\n", + "# m = pd.Timestamp(np_t.values).month\n", + "# if m == n:\n", + "# if d == 15:\n", + "# if t == datetime.time(12):\n", + "# fig, ax = pvdeg.geospatial.plot_USA(RH_module['RH_surface_outside'].sel(time=np_t),\n", + "# cmap='viridis', vmin=0, vmax=100, \n", + "# title=f'RH_surface_outside - 2022-{m}-{d} 12:00', \n", + "# cb_title='Relative humidity (%)')\n", + "# plt.savefig(f'./images/RH_animation_{n}.png', dpi=600)\n", "\n", - "ani.save('./images/RH_animation.gif', writer=PillowWriter(fps=1))" + "# import imageio\n", + "# ims = [imageio.imread(f'./images/RH_animation_{n}.png') for n in range(1, 13)]\n", + "# imageio.mimwrite(f'./images/RH_animation.gif', ims, format='GIF', duration=1000, loop=10)" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mFailed to start the Kernel. \n", - "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", - "\u001b[1;31mView Jupyter log for further details." - ] - } - ], "source": [ - "from IPython.display import HTML\n", - "HTML('')" + "![PVDeg Logo](./images/RH_animation.gif)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mFailed to start the Kernel. \n", - "\u001b[1;31mUnable to start Kernel 'py310 (Python 3.10.13)' due to a timeout waiting for the ports to get used. \n", - "\u001b[1;31mView Jupyter log for further details." - ] - } - ], - "source": [ - "RH_module.sel(latitude=35.16, longitude=-106.58, method='nearest')['RH_front_encap'].plot()" - ] + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "pvdeg (py310)", "language": "python", - "name": "python3" + "name": "py310" }, "language_info": { "codemirror_mode": { diff --git a/requirements.txt b/requirements.txt index b0d4558a..ab0eac8f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -19,3 +19,4 @@ xarray netCDF4 h5py h5netcdf +cartopy From 88d2114cc2e4a4746937fbffa1d1844418784df1 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Fri, 13 Oct 2023 12:29:55 -0600 Subject: [PATCH 10/10] fix pytests standards --- tests/data/h5_pytest.h5 | Bin 125608 -> 125608 bytes tests/test_standards.py | 8 ++------ 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/tests/data/h5_pytest.h5 b/tests/data/h5_pytest.h5 index b0f1fda7b98a6cdf68464cbb5666cff3df9799b5..69b6730582597c294a14d916047cc37d8c112405 100644 GIT binary patch delta 2102 zcmZ2+m3_rk_6bRhtQ(W}GEKH%mSJR=9LnqtB zV~|i@X>LI!NW@Uj$V3mKs_KMy2ktY>JTXK1XTk(-#Ar=t*@TA*NPs9< z(os+5!Kx}tGV=34V#a!wdWL2S8o7y? zc{&QdiA4$u779iNM#ee{h6Yx~mR3e4nhMdr!OjY0dWM!jm1ZUiNtq=I8jij$W+s}k zAk&g_GLy5FoPnlU=ovyR$S{RiX{=|iX8=+I5=a4A2y~PQ#9hV7nJ{-5f}C0l^zed0 z1;z)P4;EhM+a$3ff|ZBCfq`Z6U1sNr`zj`{Xqdy!0uCC5&EAc3*(PzQ4(~vC&NuOb zz~&PjuHwVnpV#>&zgUlm+wKjA