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            "License :: OSI Approved :: MIT License",
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        "description": "# Photovoltaic Climate Zones (PVCZ)\n\nThis package provides the photovoltaic climate zones (PVCZ) and climate stressor data which describes the degree of environmental degradation expected on a PV module located in different locations on the world. \n\n# Install\npvcz can be installed with pip\n```python\npip install pvcz\n```\n\n# About\nThe data is calcuated from the global land data accumulation service (GLDAS) at 0.25 degree resolution across the world.\n\nFor a full description, see the file 'Karin2019 - Photovoltaic Degradation Climate Zones - PVSC' which describes the methods.\n\n# Climate stressors\nThis dataset is provided as a csv file and as a pickle file containing climate stressors specific to PV degradation.\n\n- lat: latitude in fractional degrees.\n- lon: longitude in fractional degrees.\n- T_equiv_rack_1p1eV: Arrhenius-weighted module equivalent temperature calculated using open-rack polymer-back temperature model and activation energy 1.1 eV, in C\n- T_equiv_roof_1p1eV: Arrhenius-weighted module equivalent temperature calculated using close-roof-mount glass-back temperature model and activation energy 1.1 eV, in\nCs\n- T_equiv_rack_XXXeV: Arrhenius-weighted module equivalent temperature calculated using open-rack polymer-back temperature model and activation energy XXX eV, in C\n- specific_humidity_mean: Average specific humidity, in g/kg.\n- specific_humidity_rms: Root-mean-squared specific humidity, in g/kg.\n- T_velocity_rack: Average rate of change of module temperature using open-rack polymer-back temperature model, in C/hr.\n- T_velocity_roof: Average rate of change of module temperature using roof-mount glass-back temperature model, in C/hr.\n- GHI_mean: Mean global horizontal irradiance, in kWh/m2/day.\n- wind_speed: ASCE wind speed with a mean recurrence interval of 25 years, in m/s.\n- T_ambient_min: Minimum ambient temperature, in C\n- T_ambient_max: Maximum ambient temperature, in C\n- T_ambient_mean: Mean ambient temperature, in C\n- KG_zone: Koppen Geiger zone, in text\n- KG_numeric_zone: Koppen Geiger zone, as a number.\n- T_equiv_rack_zone: Temperature zone for open-rack modules as a number 0 through 9, equivalent to temperature zones T1 through T10 respectively.\n- T_equiv_roof_zone: Temperature zone for close- roof-mount modules as a number 0 through 9, equivalent to temperature zones T1 through T10 respectively.\n- specific_humidity_mean_zone: Specific humid- ity zone, as a number 0 through 4, equivalent to temperature zones H1 through H5 respectively.\n- wind_speed_zone: Wind speed zone as a number 0 through 4, equivalent to wind zones W1 through W5 respectively.\n- pvcz: Photovoltaic climate zone, combined Temperature (rack) and humidity zones as a number 0 through 49, corresponding to temperature zones T1:H1, T2:H1, ... , T10:H5, see next variable as well.\n- pvcz_labeled: Photovoltaic climate zone, combined Temperature (rack) and humidity zones as an alpha- numeric key, e.g. T5:H2.\n\n# Examples\n\nThe following code snippet shows how to find the climate stressors and zones closest to a particular latitude and longitude.\n\n```python\nimport pvcz\n\n# Note df is a flattened list of lat/lon values that only includes those over land\ndf = pvcz.get_pvcz_data()\n\n# Point of interest specified by lat/lon coordinates.\nlat_poi = 32\nlon_poi = -95.23\n\n# Find the closest location on land to a point of interest\nclosest_index = pvcz.arg_closest_point(lat_poi, lon_poi, df['lat'], df['lon'])\n\n# Get the stressor data from this location\nlocation_data = df.iloc[closest_index]\nprint(location_data)\n```\n\nThe following code makes a map of a particular stressor. \n\n```python\nimport numpy as np\nimport pvcz\nfrom mpl_toolkits.basemap import Basemap\nimport matplotlib\nmatplotlib.use('TkAgg')\nimport matplotlib.pyplot as plt\n\n# Get the data.\n# Note df is a flattened list of lat/lon values that only includes those over land\ndf = pvcz.get_pvcz_data()\ninfo = pvcz.get_pvcz_info()\n\n# For some uses (like making a map), it is convenient to have a 2D grid of lat/long values\ndata = {}\nfor v in df:\n    data[v] = pvcz.convert_flat_to_grid(df[v],\n                                        info['keepers'],\n                                        info['lon_all'],\n                                        info['lat_all'])\n\n# Make a plot\n\n# grid the lat/lon coordinates.\nxg, yg = np.meshgrid(info['lon_all'], info['lat_all'])\n\n# Set up the map\nfig = plt.figure(0, figsize=(5.5, 4.5))\nplt.clf()\nax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\nm = Basemap(projection='cyl', llcrnrlat=-60, urcrnrlat=90, \\\n            llcrnrlon=-180, urcrnrlon=180, resolution='c')\nm.drawcoastlines(linewidth=0.5)\nm.drawcountries()\n\n# Draw the filled contour lines (the map).\ncs = m.contourf(xg, yg, data['T_equiv_rack'],\n                levels=40, cmap=\"jet\", latlon=True)\n\ncbar = m.colorbar(cs,location='bottom',pad=\"5%\")\ncbar.set_label('Equivalent Temperature, 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