diff --git a/appveyor.yml b/appveyor.yml index 7efac30b67..6c211a2cf7 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -26,8 +26,10 @@ install: - "python --version" - "python -c \"import struct; print(struct.calcsize('P') * 8)\"" - # install depenencies - - "conda create -n test_env --yes --quiet python=%PYTHON_VERSION% pip numpy scipy=0.16.0 pandas nose pytz ephem numba" + # install dependencies, then pvlib + - "conda config --add channels http://conda.anaconda.org/conda-forge" + + - "conda create -n test_env --yes --quiet python=%PYTHON_VERSION% pip numpy scipy=0.16.0 pandas nose pytz ephem numba siphon" - "activate test_env" - "conda list" diff --git a/ci/requirements-py27-min.yml b/ci/requirements-py27-min.yml index fab0813e94..21bc4ce450 100644 --- a/ci/requirements-py27-min.yml +++ b/ci/requirements-py27-min.yml @@ -1,4 +1,7 @@ name: test_env +channels: + - defaults + - http://conda.anaconda.org/Unidata dependencies: - python=2.7 - numpy==1.8.2 diff --git a/ci/requirements-py27.yml b/ci/requirements-py27.yml index 52d8d575f2..9c162e680f 100644 --- a/ci/requirements-py27.yml +++ b/ci/requirements-py27.yml @@ -1,6 +1,9 @@ name: test_env +channels: + - defaults + - http://conda.anaconda.org/conda-forge dependencies: - - python=2.7 + - python=2.7 - numpy - scipy - pandas @@ -8,5 +11,6 @@ dependencies: - pytz - ephem - numba + - siphon - pip: - coveralls \ No newline at end of file diff --git a/ci/requirements-py34.yml b/ci/requirements-py34.yml index d0d27ceec9..0fc5d4b205 100644 --- a/ci/requirements-py34.yml +++ b/ci/requirements-py34.yml @@ -1,4 +1,7 @@ name: test_env +channels: + - defaults + - http://conda.anaconda.org/conda-forge dependencies: - python=3.4 - numpy @@ -8,5 +11,6 @@ dependencies: - pytz - ephem - numba + - siphon - pip: - coveralls \ No newline at end of file diff --git a/ci/requirements-py35.yml b/ci/requirements-py35.yml index c6d348f406..140d6a3a6c 100644 --- a/ci/requirements-py35.yml +++ b/ci/requirements-py35.yml @@ -1,4 +1,7 @@ name: test_env +channels: + - defaults + - http://conda.anaconda.org/conda-forge dependencies: - python=3.5 - numpy @@ -8,5 +11,6 @@ dependencies: - pytz - ephem - numba + - siphon - pip: - coveralls \ No newline at end of file diff --git a/docs/environment.yml b/docs/environment.yml index 2fd1519aae..2e1e7ee9a8 100644 --- a/docs/environment.yml +++ b/docs/environment.yml @@ -1,4 +1,7 @@ name: pvlib +channels: + - defaults + - conda-forge dependencies: - python=2.7 - numpy @@ -11,4 +14,5 @@ dependencies: - sphinx - numpydoc - matplotlib - - seaborn \ No newline at end of file + - seaborn + - siphon diff --git a/docs/sphinx/source/forecasts.rst b/docs/sphinx/source/forecasts.rst new file mode 100644 index 0000000000..64effef77d --- /dev/null +++ b/docs/sphinx/source/forecasts.rst @@ -0,0 +1,412 @@ +.. _forecasts: + +*********** +Forecasting +*********** + +pvlib-python provides a set of functions and classes that make it easy +to obtain weather forecast data and convert that data into a PV power +forecast. Users can retrieve standardized weather forecast data relevant +to PV power modeling from NOAA/NCEP/NWS models including the GFS, NAM, +RAP, HRRR, and the NDFD. A PV power forecast can then be obtained using +the weather data as inputs to the comprehensive modeling capabilities of +PVLIB-Python. Standardized, open source, reference implementations of +forecast methods using publicly available data may help advance the +state-of-the-art of solar power forecasting. + +pvlib-python uses Unidata's `Siphon +`_ library to simplify access +to forecast data hosted on the Unidata `THREDDS catalog +`_. + +This document demonstrates how to use pvlib-python to create a PV power +forecast using these tools. The `forecast +`_ and `forecast_to_power +`_ Jupyter notebooks +provide additional example code. + + +Accessing Forecast Data +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The Siphon library provides access to, among others, forecasts from the +Global Forecast System (GFS), North American Model (NAM), High +Resolution Rapid Refresh (HRRR), Rapid Refresh (RAP), and National +Digital Forecast Database (NDFD) on a Unidata THREDDS server. +Unfortunately, many of these models use different names to describe the +same quantity (or a very similar one), and not all variables are present +in all models. For example, on the THREDDS server, the GFS has a field +named +``Total_cloud_cover_entire_atmosphere_Mixed_intervals_Average``, +while the RAP has a field named +``Total_cloud_cover_entire_atmosphere_single_layer``, and a +similar field in the HRRR is named +``Total_cloud_cover_entire_atmosphere``. + +PVLIB-Python aims to simplify the access of the model fields relevant +for solar power forecasts. Model data accessed with PVLIB-Python is +returned as a pandas DataFrame with consistent column names: +``temperature, wind_speed, total_clouds, low_clouds, mid_clouds, +high_clouds, dni, dhi, ghi``. To accomplish this, we use an +object-oriented framework in which each weather model is represented by +a class that inherits from a parent +:py:class:`~pvlib.forecast.ForecastModel` class. +The parent :py:class:`~pvlib.forecast.ForecastModel` class contains the +common code for accessing and parsing the data using Siphon, while the +child model-specific classes (:py:class:`~pvlib.forecast.GFS`, +:py:class:`~pvlib.forecast.HRRR`, etc.) contain the code necessary to +map and process that specific model's data to the standardized fields. + +The code below demonstrates how simple it is to access and plot forecast +data using PVLIB-Python. First, we set up make the basic imports and +then set the location and time range data. + +.. ipython:: python + + import pandas as pd + import matplotlib.pyplot as plt + import datetime + + # seaborn makes the plots look nicer + import seaborn as sns; sns.set_color_codes() + + # import pvlib forecast models + from pvlib.forecast import GFS, NAM, NDFD, HRRR, RAP + + # specify location (Tucson, AZ) + latitude, longitude, tz = 32.2, -110.9, 'US/Arizona' + + # specify time range. + start = pd.Timestamp(datetime.date.today(), tz=tz) + end = start + pd.Timedelta(days=7) + + +Next, we instantiate a GFS model object and get the forecast data +from Unidata. + +.. ipython:: python + + # GFS model, defaults to 0.5 degree resolution + # 0.25 deg available + model = GFS() + + # retrieve data. returns pandas.DataFrame object + raw_data = model.get_data(latitude, longitude, start, end) + + print(raw_data.head()) + +It will be useful to process this data before using it with pvlib. For +example, the column names are non-standard, the temperature is in +Kelvin, the wind speed is broken into east/west and north/south +components, and most importantly, most of the irradiance data is +missing. The forecast module provides a number of methods to fix these +problems. + +.. ipython:: python + + data = raw_data + + # rename the columns according the key/value pairs in model.variables. + data = model.rename(data) + + # convert temperature + data['temperature'] = model.kelvin_to_celsius(data['temperature']) + + # convert wind components to wind speed + data['wind_speed'] = model.uv_to_speed(data) + + # calculate irradiance estimates from cloud cover. + # uses Location.get_solarposition and irradiance.liujordan + # this step is discussed in more detail in the next section + irrad_data = model.cloud_cover_to_irradiance(data['total_clouds']) + data = data.join(irrad_data, how='outer') + + # keep only the final data + data = data.ix[:, model.output_variables] + + print(data.head()) + +Much better. + +The GFS class's +:py:func:`~pvlib.forecast.GFS.process_data` method combines these steps +in a single function. In fact, each forecast model class +implements its own ``process_data`` method since the data from each +weather model is slightly different. The ``process_data`` functions are +designed to be explicit about how the data is being processed, and users +are **strongly** encouraged to read the source code of these methods. + +.. ipython:: python + + data = model.process_data(raw_data) + + print(data.head()) + +Users can easily implement their own ``process_data`` methods on +inherited classes or implement similar stand-alone functions. + +The forecast model classes also implement a +:py:func:`~pvlib.forecast.ForecastModel.get_processed_data` method that +combines the :py:func:`~pvlib.forecast.ForecastModel.get_data` and +:py:func:`~pvlib.forecast.ForecastModel.process_data` calls. + +.. ipython:: python + + data = model.get_processed_data(latitude, longitude, start, end) + + print(data.head()) + + +Cloud cover and radiation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +All of the weather models currently accessible by pvlib include one or +more cloud cover forecasts. For example, below we plot the GFS cloud +cover forecasts. + +.. ipython:: python + + # plot cloud cover percentages + cloud_vars = ['total_clouds', 'low_clouds', + 'mid_clouds', 'high_clouds'] + data[cloud_vars].plot(); + plt.ylabel('Cloud cover %'); + plt.xlabel('Forecast Time ({})'.format(tz)); + plt.title('GFS 0.5 deg forecast for lat={}, lon={}' + .format(latitude, longitude)); + @savefig gfs_cloud_cover.png width=6in + plt.legend(); + +However, many of forecast models do not include radiation components in +their output fields, or if they do then the radiation fields suffer from +poor solar position or radiative transfer algorithms. It is often more +accurate to create empirically derived radiation forecasts from the +weather models' cloud cover forecasts. + +PVLIB-Python currently uses the Liu-Jordan [Liu60]_ model to convert +total cloud cover forecasts to irradiance forecasts. We encourage pvlib +users to implement new cloud cover to irradiance algorithms. The figure +below shows the result of the Liu-Jordan total cloud cover to irradiance +conversion. + +.. ipython:: python + + # plot irradiance data + irrad_vars = ['dni', 'ghi', 'dhi'] + data[irrad_vars].plot(); + plt.ylabel('Irradiance ($W/m^2$)'); + plt.xlabel('Forecast Time ({})'.format(tz)); + plt.title('GFS 0.5 deg forecast for lat={}, lon={}' + .format(latitude, longitude)); + @savefig gfs_irrad.png width=6in + plt.legend(); + + +Most weather model output has a fairly coarse time resolution, at least +an hour. The irradiance forecasts have the same time resolution as the +weather data. However, it is straightforward to interpolate the cloud +cover forecasts onto a higher resolution time domain, and then +recalculate the irradiance. + +.. ipython:: python + + from pvlib import irradiance + total_clouds = data['total_clouds'].resample('5min').interpolate() + solar_position = model.location.get_solarposition(total_clouds.index) + irrad_data = irradiance.liujordan(solar_position['apparent_zenith'], total_clouds) + irrad_data[irrad_vars].plot(); + plt.ylabel('Irradiance ($W/m^2$)'); + plt.xlabel('Forecast Time ({})'.format(tz)); + plt.title('GFS 0.5 deg forecast for lat={}, lon={}' + .format(latitude, longitude)); + @savefig gfs_irrad_high_res.png width=6in + plt.legend(); + + +We reiterate that the open source code enables users to customize the +model processing to their liking. + +.. [Liu60] B. Y. Liu and R. C. Jordan, The interrelationship and + characteristic distribution of direct, diffuse, and total solar + radiation, *Solar Energy* **4**, 1 (1960). + + +Weather Models +~~~~~~~~~~~~~~ + +Next, we provide a brief description of the weather models available to +pvlib users. Note that the figures are generated when this documentation +is compiled so they will vary over time. + +GFS +--- +The Global Forecast System (GFS) is the US model that provides forecasts +for the entire globe. There is a lot of hype about how "the Euro" +(ECMWF) model is superior to the GFS. The GFS is still a great model. On +standard meteorology metrics, the ECMWF is superior to the GFS by about +a day. In other words, the accuracy of the GFS at 6 days out is +comparable to the ECMWF at 5 days out. The GFS is updated every 6 hours. +The GFS is run at two resolutions, 0.25 deg and 0.5 deg, and is +available with 3 hour time resolution. Forecasts from GFS model were +shown above. Use the GFS, among others, if you want forecasts for 1-7 +days. + + +HRRR +---- +The High Resolution Rapid Refresh (HRRR) model is perhaps the most +accurate model, however, it is only available for ~15 hours. It is +updated every hour and runs at 3 km resolution. The HRRR excels in +severe weather situations. A major upgrade to the HRRR model is expected +in Spring, 2016. See the `NOAA ESRL HRRR page +`_ for more information. Use the +HRRR, among others, if you want forecasts for less than 24 hours. +The HRRR model covers the continental United States. + +.. ipython:: python + + model = HRRR() + data = model.get_processed_data(latitude, longitude, start, end) + + data[irrad_vars].plot(); + plt.ylabel('Irradiance ($W/m^2$)'); + plt.xlabel('Forecast Time ({})'.format(tz)); + plt.title('HRRR 3 km forecast for lat={}, lon={}' + .format(latitude, longitude)); + @savefig hrrr_irrad.png width=6in + plt.legend(); + + +RAP +--- +The Rapid Refresh (RAP) model is the parent model for the HRRR. It is +updated every hour and runs at 40, 20, and 13 km resolutions. Only the +20 and 40 km resolutions are currently available in pvlib. It is also +excels in severe weather situations. A major upgrade to the RAP model is +expected in Spring, 2016. See the `NOAA ESRL HRRR page +`_ for more information. Use the +RAP, among others, if you want forecasts for less than 24 hours. +The RAP model covers most of North America. + +.. ipython:: python + + model = RAP() + data = model.get_processed_data(latitude, longitude, start, end) + + data[irrad_vars].plot(); + plt.ylabel('Irradiance ($W/m^2$)'); + plt.xlabel('Forecast Time ({})'.format(tz)); + plt.title('RAP 13 km forecast for lat={}, lon={}' + .format(latitude, longitude)); + @savefig rap_irrad.png width=6in + plt.legend(); + + +NAM +--- +The North American Mesoscale model is a somewhat older model that is +target of frequent criticism, justly or not. It is updated every 6 hours +and runs at 20 km resolution. Use the NAM as part of an ensemble forecast. +The NAM model covers North America. + +.. ipython:: python + + model = NAM() + data = model.get_processed_data(latitude, longitude, start, end) + + data[irrad_vars].plot(); + plt.ylabel('Irradiance ($W/m^2$)'); + plt.xlabel('Forecast Time ({})'.format(tz)); + plt.title('NAM 20 km forecast for lat={}, lon={}' + .format(latitude, longitude)); + @savefig nam_irrad.png width=6in + plt.legend(); + + +NDFD +---- +The National Digital Forecast Database is not a model, but rather a +collection of forecasts made by National Weather Service offices +across the country. It is updated every 6 hours. +Use the NDFD, among others, for forecasts at all time horizons. +The NDFD is available for the United States. + +.. ipython:: python + + model = NDFD() + data = model.get_processed_data(latitude, longitude, start, end) + + data[irrad_vars].plot(); + plt.ylabel('Irradiance ($W/m^2$)'); + plt.xlabel('Forecast Time ({})'.format(tz)); + plt.title('NDFD forecast for lat={}, lon={}' + .format(latitude, longitude)); + @savefig ndfd_irrad.png width=6in + plt.legend(); + + +PV Power Forecast +~~~~~~~~~~~~~~~~~ + +Finally, we demonstrate the application of the weather forecast data to +a PV power forecast. Please see the remainder of the pvlib documentation +for details. + +.. ipython:: python + + from pvlib.pvsystem import PVSystem, retrieve_sam + from pvlib.tracking import SingleAxisTracker + from pvlib.modelchain import ModelChain + + sandia_modules = retrieve_sam('sandiamod') + cec_inverters = retrieve_sam('cecinverter') + module = sandia_modules['Canadian_Solar_CS5P_220M___2009_'] + inverter = cec_inverters['SMA_America__SC630CP_US_315V__CEC_2012_'] + + # model a big tracker for more fun + system = SingleAxisTracker(module_parameters=module, + inverter_parameters=inverter, + series_modules=15, + parallel_modules=300) + + # fx is a common abbreviation for forecast + fx_model = GFS() + fx_data = fx_model.get_processed_data(latitude, longitude, start, end) + + # use a ModelChain object to calculate modeling intermediates + mc = ModelChain(system, fx_model.location) + + # extract relevant data for model chain + irradiance = fx_data[['ghi', 'dni', 'dhi']] + weather = fx_data[['wind_speed', 'temperature']].rename( + columns={'temperature': 'temp_air'}) + mc.run_model(fx_data.index, irradiance=irradiance, weather=weather); + +Now we plot a couple of modeling intermediates and the forecast power. +Here's the forecast plane of array irradiance... + +.. ipython:: python + + mc.total_irrad.plot(); + @savefig poa_irrad.png width=6in + plt.ylabel('Plane of array irradiance ($W/m**2$)'); + +...the cell and module temperature... + +.. ipython:: python + + mc.temps.plot(); + @savefig pv_temps.png width=6in + plt.ylabel('Temperature (C)'); + +...and finally AC power... + +.. ipython:: python + + mc.ac.plot(); + plt.ylim(0, None); + @savefig ac_power.png width=6in + plt.ylabel('AC Power (W)'); + diff --git a/docs/sphinx/source/index.rst b/docs/sphinx/source/index.rst index 519609c32b..a6152930e9 100644 --- a/docs/sphinx/source/index.rst +++ b/docs/sphinx/source/index.rst @@ -76,6 +76,7 @@ Contents modules classes comparison_pvlib_matlab + forecasts variables_style_rules diff --git a/docs/sphinx/source/modules.rst b/docs/sphinx/source/modules.rst index 3ae22dd0f4..e0a23ea8ac 100644 --- a/docs/sphinx/source/modules.rst +++ b/docs/sphinx/source/modules.rst @@ -17,6 +17,14 @@ clearsky :undoc-members: :show-inheritance: +forecast +---------------- + +.. automodule:: pvlib.forecast + :members: + :undoc-members: + :show-inheritance: + irradiance ----------------- @@ -56,7 +64,7 @@ solarposition :members: :undoc-members: :show-inheritance: - + tmy -------------------- @@ -80,4 +88,3 @@ tools :members: :undoc-members: :show-inheritance: - \ No newline at end of file diff --git a/docs/sphinx/source/whatsnew/v0.4.0.txt b/docs/sphinx/source/whatsnew/v0.4.0.txt new file mode 100644 index 0000000000..7d86864fe2 --- /dev/null +++ b/docs/sphinx/source/whatsnew/v0.4.0.txt @@ -0,0 +1,17 @@ +.. _whatsnew_0400: + +v0.4.0 (2016) +------------------------- + + + +Enhancements +~~~~~~~~~~~~ + +* Added ''forecast.py'' module which defines forecast model classes. + +Contributors +~~~~~~~~~~~~ + +* Derek Groenendyk +* Will Holmgren diff --git a/docs/tutorials/forecast.ipynb b/docs/tutorials/forecast.ipynb new file mode 100644 index 0000000000..268a8e7d14 --- /dev/null +++ b/docs/tutorials/forecast.ipynb @@ -0,0 +1,6597 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Forecast Tutorial\n", + "\n", + "This tutorial will walk through forecast data from Unidata forecast model data using the forecast.py module within pvlib.\n", + "\n", + "Table of contents:\n", + "1. [Setup](#Setup)\n", + "2. [Intialize and Test Each Forecast Model](#Instantiate-GFS-forecast-model)\n", + "\n", + "This tutorial has been tested against the following package versions:\n", + "* Python 3.4.3\n", + "* IPython 4.0.1\n", + "* pandas 0.18.0\n", + "* matplotlib 1.5.1\n", + "* netcdf4 1.2.1\n", + "* siphon 0.3.2\n", + "\n", + "It should work with other Python and Pandas versions. It requires pvlib >= 0.3.0 and IPython >= 3.0.\n", + "\n", + "Authors:\n", + "* Derek Groenendyk (@moonraker), University of Arizona, November 2015\n", + "* Will Holmgren (@wholmgren), University of Arizona, November 2015, January 2016, April 2016" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "try:\n", + " import seaborn as sns\n", + " sns.set(rc={\"figure.figsize\": (12, 6)})\n", + "except ImportError:\n", + " print('We suggest you install seaborn using conda or pip and rerun this cell')\n", + "\n", + "# built in python modules\n", + "import datetime\n", + "import os\n", + "\n", + "# python add-ons\n", + "import numpy as np\n", + "import pandas as pd\n", + "try:\n", + " import netCDF4\n", + " from netCDF4 import num2date\n", + "except ImportError:\n", + " print('We suggest you install netCDF4 using conda rerun this cell')\n", + "\n", + "# for accessing UNIDATA THREDD servers\n", + "from siphon.catalog import TDSCatalog\n", + "from siphon.ncss import NCSS\n", + "\n", + "import pvlib\n", + "from pvlib.forecast import GFS, HRRR_ESRL, NAM, NDFD, HRRR, RAP" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(Timestamp('2016-04-03 00:00:00-0700', tz='US/Arizona'), Timestamp('2016-04-10 00:00:00-0700', tz='US/Arizona'))\n" + ] + } + ], + "source": [ + "# Choose a location and time.\n", + "# Tucson, AZ\n", + "latitude = 32.2\n", + "longitude = -110.9 \n", + "tz = 'US/Arizona'\n", + "\n", + "start = pd.Timestamp(datetime.date.today(), tz=tz) # today's date\n", + "end = start + pd.Timedelta(days=7) # 7 days from today\n", + "print(start, end)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GFS (0.5 deg)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# GFS model, defaults to 0.5 degree resolution\n", + "fm = GFS()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# retrieve data\n", + "data = fm.get_data(latitude, longitude, start, end)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Downward_Short-Wave_Radiation_Flux_surface_Mixed_intervals_AverageTemperature_surfaceTotal_cloud_cover_boundary_layer_cloud_Mixed_intervals_AverageTotal_cloud_cover_convective_cloudTotal_cloud_cover_entire_atmosphere_Mixed_intervals_AverageTotal_cloud_cover_high_cloud_Mixed_intervals_AverageTotal_cloud_cover_low_cloud_Mixed_intervals_AverageTotal_cloud_cover_middle_cloud_Mixed_intervals_AverageWind_speed_gust_surfaceu-component_of_wind_isobaricv-component_of_wind_isobaric
2016-04-03 09:00:00-07:000.0284.3999940.00.00.00.00.00.04.700000-4.511.39
2016-04-03 12:00:00-07:000.0283.0000000.00.00.00.00.00.05.600000-4.982.62
2016-04-03 15:00:00-07:00100.0291.8999940.00.00.00.00.00.06.300000-4.511.46
2016-04-03 18:00:00-07:00401.0309.5000000.00.00.00.00.00.03.000000-3.470.68
2016-04-03 21:00:00-07:00990.0317.7999880.00.00.00.00.00.01.800000-0.610.15
2016-04-04 00:00:00-07:00829.0305.1000060.00.00.00.00.00.02.5000000.47-1.77
2016-04-04 03:00:00-07:0090.0290.0000000.00.00.00.00.00.02.400000-1.01-2.22
2016-04-04 06:00:00-07:0045.0286.7000120.00.00.00.00.00.01.4000000.62-1.23
2016-04-04 09:00:00-07:000.0284.7000120.00.00.00.00.00.01.800000-1.76-0.51
2016-04-04 12:00:00-07:000.0283.0000000.00.00.00.00.00.02.000000-1.711.10
2016-04-04 15:00:00-07:00100.0295.8999940.00.00.00.00.00.02.700000-2.190.79
2016-04-04 18:00:00-07:00399.0312.6000060.00.00.00.00.00.03.500000-0.432.27
2016-04-04 21:00:00-07:00980.0316.0000000.00.00.00.00.00.03.5000002.070.90
2016-04-05 00:00:00-07:00828.0306.3999940.00.08.08.00.00.04.0000003.180.05
2016-04-05 03:00:00-07:0090.0291.6000060.00.060.060.00.00.02.7000002.21-1.58
2016-04-05 06:00:00-07:0044.0288.2999880.00.067.067.00.00.01.6109181.600.06
2016-04-05 09:00:00-07:000.0285.7000120.00.064.064.00.00.01.6000001.570.06
2016-04-05 12:00:00-07:000.0283.5000000.00.056.056.00.00.00.300000-0.110.24
2016-04-05 15:00:00-07:0080.0295.5000000.00.089.089.00.00.01.000000-0.900.35
2016-04-05 18:00:00-07:00329.0310.7999880.00.095.095.00.00.01.300000-0.16-1.64
2016-04-05 21:00:00-07:00920.0313.6000060.00.096.096.00.00.04.1000002.66-2.96
2016-04-06 00:00:00-07:00775.0304.1000060.00.090.090.00.00.05.2000002.12-5.37
2016-04-06 03:00:00-07:0070.0291.6000060.00.099.099.00.00.02.100000-0.63-1.99
2016-04-06 06:00:00-07:0036.0289.1000060.00.099.099.00.00.01.6000000.26-1.54
2016-04-06 09:00:00-07:000.0286.8999940.00.0100.0100.00.00.00.900000-0.64-0.67
2016-04-06 12:00:00-07:000.0284.2000120.00.088.088.00.00.01.400000-1.42-0.20
2016-04-06 15:00:00-07:00100.0296.8999940.00.076.076.00.00.02.500000-2.110.43
2016-04-06 18:00:00-07:00388.0312.2000120.00.083.083.00.00.05.700000-4.98-0.24
2016-04-06 21:00:00-07:00960.0316.5000000.00.086.086.00.00.05.000000-4.370.12
2016-04-07 00:00:00-07:00783.0307.2999880.00.093.093.00.00.05.000000-4.240.45
2016-04-07 03:00:00-07:0070.0296.2000120.00.097.097.00.01.07.800000-5.851.98
2016-04-07 06:00:00-07:0036.0294.7999880.00.099.099.00.09.08.500000-3.304.93
2016-04-07 09:00:00-07:000.0291.7999880.00.0100.0100.00.078.03.800000-0.393.84
2016-04-07 12:00:00-07:000.0291.0000000.00.0100.0100.00.088.01.700000-0.301.66
2016-04-07 15:00:00-07:0020.0293.5000000.00.0100.0100.00.070.00.2000000.010.22
2016-04-07 18:00:00-07:00181.0305.3999940.00.092.088.00.076.05.300000-5.03-2.78
2016-04-07 21:00:00-07:00360.0299.1000060.00.0100.0100.01.099.03.400000-3.450.34
2016-04-08 00:00:00-07:00396.0300.2999880.00.081.080.00.078.03.400000-3.60-1.35
2016-04-08 03:00:00-07:0090.0290.1000060.00.015.014.00.02.06.800000-5.01-0.13
2016-04-08 06:00:00-07:0044.0289.6000060.00.054.053.00.028.05.900000-3.991.65
2016-04-08 09:00:00-07:000.0288.3999940.00.098.098.00.084.03.200000-2.901.50
2016-04-08 12:00:00-07:000.0286.2999880.00.097.097.00.058.03.000000-2.541.60
2016-04-08 15:00:00-07:0090.0294.5000000.00.092.092.00.03.03.400000-1.811.74
2016-04-08 18:00:00-07:00383.0307.2999880.01.049.048.00.01.05.2000002.413.85
2016-04-08 21:00:00-07:00780.0304.8999940.00.042.00.042.00.06.2000004.623.60
2016-04-09 00:00:00-07:00632.0300.0000000.00.059.021.039.06.05.9000005.203.04
2016-04-09 03:00:00-07:0080.0289.6000060.00.028.027.00.00.03.1000002.671.59
2016-04-09 06:00:00-07:0041.0286.5000000.00.014.014.00.00.01.1000000.261.08
2016-04-09 09:00:00-07:000.0284.7000120.00.00.00.00.00.01.800000-0.301.80
2016-04-09 12:00:00-07:000.0283.6000060.00.00.00.00.00.02.800000-1.752.21
2016-04-09 15:00:00-07:00110.0294.0000000.00.00.00.00.00.08.200000-0.625.23
2016-04-09 18:00:00-07:00406.0305.0000000.00.00.00.00.00.010.7000004.308.53
2016-04-09 21:00:00-07:00980.0306.5000000.00.00.00.00.00.013.2000007.668.91
2016-04-10 00:00:00-07:00825.0298.3999940.00.00.00.00.00.013.0000008.937.03
2016-04-10 03:00:00-07:00100.0288.0000000.00.00.00.00.00.07.7000004.842.11
2016-04-10 06:00:00-07:0047.0285.5000000.00.05.06.00.00.07.5000002.664.17
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291.600006 \n", + "2016-04-05 06:00:00-07:00 288.299988 \n", + "2016-04-05 09:00:00-07:00 285.700012 \n", + "2016-04-05 12:00:00-07:00 283.500000 \n", + "2016-04-05 15:00:00-07:00 295.500000 \n", + "2016-04-05 18:00:00-07:00 310.799988 \n", + "2016-04-05 21:00:00-07:00 313.600006 \n", + "2016-04-06 00:00:00-07:00 304.100006 \n", + "2016-04-06 03:00:00-07:00 291.600006 \n", + "2016-04-06 06:00:00-07:00 289.100006 \n", + "2016-04-06 09:00:00-07:00 286.899994 \n", + "2016-04-06 12:00:00-07:00 284.200012 \n", + "2016-04-06 15:00:00-07:00 296.899994 \n", + "2016-04-06 18:00:00-07:00 312.200012 \n", + "2016-04-06 21:00:00-07:00 316.500000 \n", + "2016-04-07 00:00:00-07:00 307.299988 \n", + "2016-04-07 03:00:00-07:00 296.200012 \n", + "2016-04-07 06:00:00-07:00 294.799988 \n", + "2016-04-07 09:00:00-07:00 291.799988 \n", + "2016-04-07 12:00:00-07:00 291.000000 \n", + "2016-04-07 15:00:00-07:00 293.500000 \n", + "2016-04-07 18:00:00-07:00 305.399994 \n", + "2016-04-07 21:00:00-07:00 299.100006 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09:00:00-07:00 0.0 \n", + "2016-04-09 12:00:00-07:00 0.0 \n", + "2016-04-09 15:00:00-07:00 0.0 \n", + "2016-04-09 18:00:00-07:00 0.0 \n", + "2016-04-09 21:00:00-07:00 0.0 \n", + "2016-04-10 00:00:00-07:00 0.0 \n", + "2016-04-10 03:00:00-07:00 0.0 \n", + "2016-04-10 06:00:00-07:00 0.0 \n", + "\n", + " Total_cloud_cover_entire_atmosphere_Mixed_intervals_Average \\\n", + "2016-04-03 09:00:00-07:00 0.0 \n", + "2016-04-03 12:00:00-07:00 0.0 \n", + "2016-04-03 15:00:00-07:00 0.0 \n", + "2016-04-03 18:00:00-07:00 0.0 \n", + "2016-04-03 21:00:00-07:00 0.0 \n", + "2016-04-04 00:00:00-07:00 0.0 \n", + "2016-04-04 03:00:00-07:00 0.0 \n", + "2016-04-04 06:00:00-07:00 0.0 \n", + "2016-04-04 09:00:00-07:00 0.0 \n", + "2016-04-04 12:00:00-07:00 0.0 \n", + "2016-04-04 15:00:00-07:00 0.0 \n", + "2016-04-04 18:00:00-07:00 0.0 \n", + "2016-04-04 21:00:00-07:00 0.0 \n", + "2016-04-05 00:00:00-07:00 8.0 \n", + "2016-04-05 03:00:00-07:00 60.0 \n", + "2016-04-05 06:00:00-07:00 67.0 \n", + "2016-04-05 09:00:00-07:00 64.0 \n", + "2016-04-05 12:00:00-07:00 56.0 \n", + "2016-04-05 15:00:00-07:00 89.0 \n", + "2016-04-05 18:00:00-07:00 95.0 \n", + "2016-04-05 21:00:00-07:00 96.0 \n", + "2016-04-06 00:00:00-07:00 90.0 \n", + "2016-04-06 03:00:00-07:00 99.0 \n", + "2016-04-06 06:00:00-07:00 99.0 \n", + "2016-04-06 09:00:00-07:00 100.0 \n", + "2016-04-06 12:00:00-07:00 88.0 \n", + "2016-04-06 15:00:00-07:00 76.0 \n", + "2016-04-06 18:00:00-07:00 83.0 \n", + "2016-04-06 21:00:00-07:00 86.0 \n", + "2016-04-07 00:00:00-07:00 93.0 \n", + "2016-04-07 03:00:00-07:00 97.0 \n", + "2016-04-07 06:00:00-07:00 99.0 \n", + "2016-04-07 09:00:00-07:00 100.0 \n", + "2016-04-07 12:00:00-07:00 100.0 \n", + "2016-04-07 15:00:00-07:00 100.0 \n", + "2016-04-07 18:00:00-07:00 92.0 \n", + "2016-04-07 21:00:00-07:00 100.0 \n", + "2016-04-08 00:00:00-07:00 81.0 \n", + "2016-04-08 03:00:00-07:00 15.0 \n", + "2016-04-08 06:00:00-07:00 54.0 \n", + "2016-04-08 09:00:00-07:00 98.0 \n", + "2016-04-08 12:00:00-07:00 97.0 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"2016-04-04 15:00:00-07:00 0.0 \n", + "2016-04-04 18:00:00-07:00 0.0 \n", + "2016-04-04 21:00:00-07:00 0.0 \n", + "2016-04-05 00:00:00-07:00 8.0 \n", + "2016-04-05 03:00:00-07:00 60.0 \n", + "2016-04-05 06:00:00-07:00 67.0 \n", + "2016-04-05 09:00:00-07:00 64.0 \n", + "2016-04-05 12:00:00-07:00 56.0 \n", + "2016-04-05 15:00:00-07:00 89.0 \n", + "2016-04-05 18:00:00-07:00 95.0 \n", + "2016-04-05 21:00:00-07:00 96.0 \n", + "2016-04-06 00:00:00-07:00 90.0 \n", + "2016-04-06 03:00:00-07:00 99.0 \n", + "2016-04-06 06:00:00-07:00 99.0 \n", + "2016-04-06 09:00:00-07:00 100.0 \n", + "2016-04-06 12:00:00-07:00 88.0 \n", + "2016-04-06 15:00:00-07:00 76.0 \n", + "2016-04-06 18:00:00-07:00 83.0 \n", + "2016-04-06 21:00:00-07:00 86.0 \n", + "2016-04-07 00:00:00-07:00 93.0 \n", + "2016-04-07 03:00:00-07:00 97.0 \n", + "2016-04-07 06:00:00-07:00 99.0 \n", + "2016-04-07 09:00:00-07:00 100.0 \n", + "2016-04-07 12:00:00-07:00 100.0 \n", + "2016-04-07 15:00:00-07:00 100.0 \n", + "2016-04-07 18:00:00-07:00 88.0 \n", + "2016-04-07 21:00:00-07:00 100.0 \n", + "2016-04-08 00:00:00-07:00 80.0 \n", + "2016-04-08 03:00:00-07:00 14.0 \n", + "2016-04-08 06:00:00-07:00 53.0 \n", + "2016-04-08 09:00:00-07:00 98.0 \n", + "2016-04-08 12:00:00-07:00 97.0 \n", + "2016-04-08 15:00:00-07:00 92.0 \n", + "2016-04-08 18:00:00-07:00 48.0 \n", + "2016-04-08 21:00:00-07:00 0.0 \n", + "2016-04-09 00:00:00-07:00 21.0 \n", + "2016-04-09 03:00:00-07:00 27.0 \n", + "2016-04-09 06:00:00-07:00 14.0 \n", + "2016-04-09 09:00:00-07:00 0.0 \n", + "2016-04-09 12:00:00-07:00 0.0 \n", + "2016-04-09 15:00:00-07:00 0.0 \n", + "2016-04-09 18:00:00-07:00 0.0 \n", + "2016-04-09 21:00:00-07:00 0.0 \n", + "2016-04-10 00:00:00-07:00 0.0 \n", + "2016-04-10 03:00:00-07:00 0.0 \n", + "2016-04-10 06:00:00-07:00 6.0 \n", + "\n", + " Total_cloud_cover_low_cloud_Mixed_intervals_Average \\\n", + "2016-04-03 09:00:00-07:00 0.0 \n", + "2016-04-03 12:00:00-07:00 0.0 \n", + "2016-04-03 15:00:00-07:00 0.0 \n", + "2016-04-03 18:00:00-07:00 0.0 \n", + "2016-04-03 21:00:00-07:00 0.0 \n", + "2016-04-04 00:00:00-07:00 0.0 \n", + "2016-04-04 03:00:00-07:00 0.0 \n", + "2016-04-04 06:00:00-07:00 0.0 \n", + "2016-04-04 09:00:00-07:00 0.0 \n", + "2016-04-04 12:00:00-07:00 0.0 \n", + "2016-04-04 15:00:00-07:00 0.0 \n", + "2016-04-04 18:00:00-07:00 0.0 \n", + "2016-04-04 21:00:00-07:00 0.0 \n", + "2016-04-05 00:00:00-07:00 0.0 \n", + "2016-04-05 03:00:00-07:00 0.0 \n", + "2016-04-05 06:00:00-07:00 0.0 \n", + "2016-04-05 09:00:00-07:00 0.0 \n", + "2016-04-05 12:00:00-07:00 0.0 \n", + "2016-04-05 15:00:00-07:00 0.0 \n", + "2016-04-05 18:00:00-07:00 0.0 \n", + "2016-04-05 21:00:00-07:00 0.0 \n", + "2016-04-06 00:00:00-07:00 0.0 \n", + "2016-04-06 03:00:00-07:00 0.0 \n", + "2016-04-06 06:00:00-07:00 0.0 \n", + "2016-04-06 09:00:00-07:00 0.0 \n", + "2016-04-06 12:00:00-07:00 0.0 \n", + "2016-04-06 15:00:00-07:00 0.0 \n", + "2016-04-06 18:00:00-07:00 0.0 \n", + "2016-04-06 21:00:00-07:00 0.0 \n", + "2016-04-07 00:00:00-07:00 0.0 \n", + "2016-04-07 03:00:00-07:00 0.0 \n", + "2016-04-07 06:00:00-07:00 0.0 \n", + "2016-04-07 09:00:00-07:00 0.0 \n", + "2016-04-07 12:00:00-07:00 0.0 \n", + "2016-04-07 15:00:00-07:00 0.0 \n", + "2016-04-07 18:00:00-07:00 0.0 \n", + "2016-04-07 21:00:00-07:00 1.0 \n", + "2016-04-08 00:00:00-07:00 0.0 \n", + "2016-04-08 03:00:00-07:00 0.0 \n", + "2016-04-08 06:00:00-07:00 0.0 \n", + "2016-04-08 09:00:00-07:00 0.0 \n", + "2016-04-08 12:00:00-07:00 0.0 \n", + "2016-04-08 15:00:00-07:00 0.0 \n", + "2016-04-08 18:00:00-07:00 0.0 \n", + "2016-04-08 21:00:00-07:00 42.0 \n", + "2016-04-09 00:00:00-07:00 39.0 \n", + "2016-04-09 03:00:00-07:00 0.0 \n", + "2016-04-09 06:00:00-07:00 0.0 \n", + "2016-04-09 09:00:00-07:00 0.0 \n", + "2016-04-09 12:00:00-07:00 0.0 \n", + "2016-04-09 15:00:00-07:00 0.0 \n", + "2016-04-09 18:00:00-07:00 0.0 \n", + "2016-04-09 21:00:00-07:00 0.0 \n", + "2016-04-10 00:00:00-07:00 0.0 \n", + "2016-04-10 03:00:00-07:00 0.0 \n", + "2016-04-10 06:00:00-07:00 0.0 \n", + "\n", + " Total_cloud_cover_middle_cloud_Mixed_intervals_Average \\\n", + "2016-04-03 09:00:00-07:00 0.0 \n", + "2016-04-03 12:00:00-07:00 0.0 \n", + "2016-04-03 15:00:00-07:00 0.0 \n", + "2016-04-03 18:00:00-07:00 0.0 \n", + "2016-04-03 21:00:00-07:00 0.0 \n", + "2016-04-04 00:00:00-07:00 0.0 \n", + "2016-04-04 03:00:00-07:00 0.0 \n", + "2016-04-04 06:00:00-07:00 0.0 \n", + "2016-04-04 09:00:00-07:00 0.0 \n", + "2016-04-04 12:00:00-07:00 0.0 \n", + "2016-04-04 15:00:00-07:00 0.0 \n", + "2016-04-04 18:00:00-07:00 0.0 \n", + "2016-04-04 21:00:00-07:00 0.0 \n", + "2016-04-05 00:00:00-07:00 0.0 \n", + "2016-04-05 03:00:00-07:00 0.0 \n", + "2016-04-05 06:00:00-07:00 0.0 \n", + "2016-04-05 09:00:00-07:00 0.0 \n", + "2016-04-05 12:00:00-07:00 0.0 \n", + "2016-04-05 15:00:00-07:00 0.0 \n", + "2016-04-05 18:00:00-07:00 0.0 \n", + "2016-04-05 21:00:00-07:00 0.0 \n", + "2016-04-06 00:00:00-07:00 0.0 \n", + "2016-04-06 03:00:00-07:00 0.0 \n", + "2016-04-06 06:00:00-07:00 0.0 \n", + "2016-04-06 09:00:00-07:00 0.0 \n", + "2016-04-06 12:00:00-07:00 0.0 \n", + "2016-04-06 15:00:00-07:00 0.0 \n", + "2016-04-06 18:00:00-07:00 0.0 \n", + "2016-04-06 21:00:00-07:00 0.0 \n", + "2016-04-07 00:00:00-07:00 0.0 \n", + "2016-04-07 03:00:00-07:00 1.0 \n", + "2016-04-07 06:00:00-07:00 9.0 \n", + "2016-04-07 09:00:00-07:00 78.0 \n", + "2016-04-07 12:00:00-07:00 88.0 \n", + "2016-04-07 15:00:00-07:00 70.0 \n", + "2016-04-07 18:00:00-07:00 76.0 \n", + "2016-04-07 21:00:00-07:00 99.0 \n", + "2016-04-08 00:00:00-07:00 78.0 \n", + "2016-04-08 03:00:00-07:00 2.0 \n", + "2016-04-08 06:00:00-07:00 28.0 \n", + "2016-04-08 09:00:00-07:00 84.0 \n", + "2016-04-08 12:00:00-07:00 58.0 \n", + "2016-04-08 15:00:00-07:00 3.0 \n", + "2016-04-08 18:00:00-07:00 1.0 \n", + "2016-04-08 21:00:00-07:00 0.0 \n", + "2016-04-09 00:00:00-07:00 6.0 \n", + "2016-04-09 03:00:00-07:00 0.0 \n", + "2016-04-09 06:00:00-07:00 0.0 \n", + "2016-04-09 09:00:00-07:00 0.0 \n", + "2016-04-09 12:00:00-07:00 0.0 \n", + "2016-04-09 15:00:00-07:00 0.0 \n", + "2016-04-09 18:00:00-07:00 0.0 \n", + "2016-04-09 21:00:00-07:00 0.0 \n", + "2016-04-10 00:00:00-07:00 0.0 \n", + "2016-04-10 03:00:00-07:00 0.0 \n", + "2016-04-10 06:00:00-07:00 0.0 \n", + "\n", + " Wind_speed_gust_surface \\\n", + "2016-04-03 09:00:00-07:00 4.700000 \n", + "2016-04-03 12:00:00-07:00 5.600000 \n", + "2016-04-03 15:00:00-07:00 6.300000 \n", + "2016-04-03 18:00:00-07:00 3.000000 \n", + "2016-04-03 21:00:00-07:00 1.800000 \n", + "2016-04-04 00:00:00-07:00 2.500000 \n", + "2016-04-04 03:00:00-07:00 2.400000 \n", + "2016-04-04 06:00:00-07:00 1.400000 \n", + "2016-04-04 09:00:00-07:00 1.800000 \n", + "2016-04-04 12:00:00-07:00 2.000000 \n", + "2016-04-04 15:00:00-07:00 2.700000 \n", + "2016-04-04 18:00:00-07:00 3.500000 \n", + "2016-04-04 21:00:00-07:00 3.500000 \n", + "2016-04-05 00:00:00-07:00 4.000000 \n", + "2016-04-05 03:00:00-07:00 2.700000 \n", + "2016-04-05 06:00:00-07:00 1.610918 \n", + "2016-04-05 09:00:00-07:00 1.600000 \n", + "2016-04-05 12:00:00-07:00 0.300000 \n", + "2016-04-05 15:00:00-07:00 1.000000 \n", + "2016-04-05 18:00:00-07:00 1.300000 \n", + "2016-04-05 21:00:00-07:00 4.100000 \n", + "2016-04-06 00:00:00-07:00 5.200000 \n", + "2016-04-06 03:00:00-07:00 2.100000 \n", + "2016-04-06 06:00:00-07:00 1.600000 \n", + "2016-04-06 09:00:00-07:00 0.900000 \n", + "2016-04-06 12:00:00-07:00 1.400000 \n", + "2016-04-06 15:00:00-07:00 2.500000 \n", + "2016-04-06 18:00:00-07:00 5.700000 \n", + "2016-04-06 21:00:00-07:00 5.000000 \n", + "2016-04-07 00:00:00-07:00 5.000000 \n", + "2016-04-07 03:00:00-07:00 7.800000 \n", + "2016-04-07 06:00:00-07:00 8.500000 \n", + "2016-04-07 09:00:00-07:00 3.800000 \n", + "2016-04-07 12:00:00-07:00 1.700000 \n", + "2016-04-07 15:00:00-07:00 0.200000 \n", + "2016-04-07 18:00:00-07:00 5.300000 \n", + "2016-04-07 21:00:00-07:00 3.400000 \n", + "2016-04-08 00:00:00-07:00 3.400000 \n", + "2016-04-08 03:00:00-07:00 6.800000 \n", + "2016-04-08 06:00:00-07:00 5.900000 \n", + "2016-04-08 09:00:00-07:00 3.200000 \n", + "2016-04-08 12:00:00-07:00 3.000000 \n", + "2016-04-08 15:00:00-07:00 3.400000 \n", + "2016-04-08 18:00:00-07:00 5.200000 \n", + "2016-04-08 21:00:00-07:00 6.200000 \n", + "2016-04-09 00:00:00-07:00 5.900000 \n", + "2016-04-09 03:00:00-07:00 3.100000 \n", + "2016-04-09 06:00:00-07:00 1.100000 \n", + "2016-04-09 09:00:00-07:00 1.800000 \n", + "2016-04-09 12:00:00-07:00 2.800000 \n", + "2016-04-09 15:00:00-07:00 8.200000 \n", + "2016-04-09 18:00:00-07:00 10.700000 \n", + "2016-04-09 21:00:00-07:00 13.200000 \n", + "2016-04-10 00:00:00-07:00 13.000000 \n", + "2016-04-10 03:00:00-07:00 7.700000 \n", + "2016-04-10 06:00:00-07:00 7.500000 \n", + "\n", + " u-component_of_wind_isobaric \\\n", + "2016-04-03 09:00:00-07:00 -4.51 \n", + "2016-04-03 12:00:00-07:00 -4.98 \n", + "2016-04-03 15:00:00-07:00 -4.51 \n", + "2016-04-03 18:00:00-07:00 -3.47 \n", + "2016-04-03 21:00:00-07:00 -0.61 \n", + "2016-04-04 00:00:00-07:00 0.47 \n", + "2016-04-04 03:00:00-07:00 -1.01 \n", + "2016-04-04 06:00:00-07:00 0.62 \n", + "2016-04-04 09:00:00-07:00 -1.76 \n", + "2016-04-04 12:00:00-07:00 -1.71 \n", + "2016-04-04 15:00:00-07:00 -2.19 \n", + "2016-04-04 18:00:00-07:00 -0.43 \n", + "2016-04-04 21:00:00-07:00 2.07 \n", + "2016-04-05 00:00:00-07:00 3.18 \n", + "2016-04-05 03:00:00-07:00 2.21 \n", + "2016-04-05 06:00:00-07:00 1.60 \n", + "2016-04-05 09:00:00-07:00 1.57 \n", + "2016-04-05 12:00:00-07:00 -0.11 \n", + "2016-04-05 15:00:00-07:00 -0.90 \n", + "2016-04-05 18:00:00-07:00 -0.16 \n", + "2016-04-05 21:00:00-07:00 2.66 \n", + "2016-04-06 00:00:00-07:00 2.12 \n", + "2016-04-06 03:00:00-07:00 -0.63 \n", + "2016-04-06 06:00:00-07:00 0.26 \n", + "2016-04-06 09:00:00-07:00 -0.64 \n", + "2016-04-06 12:00:00-07:00 -1.42 \n", + "2016-04-06 15:00:00-07:00 -2.11 \n", + "2016-04-06 18:00:00-07:00 -4.98 \n", + "2016-04-06 21:00:00-07:00 -4.37 \n", + "2016-04-07 00:00:00-07:00 -4.24 \n", + "2016-04-07 03:00:00-07:00 -5.85 \n", + "2016-04-07 06:00:00-07:00 -3.30 \n", + "2016-04-07 09:00:00-07:00 -0.39 \n", + "2016-04-07 12:00:00-07:00 -0.30 \n", + "2016-04-07 15:00:00-07:00 0.01 \n", + "2016-04-07 18:00:00-07:00 -5.03 \n", + "2016-04-07 21:00:00-07:00 -3.45 \n", + "2016-04-08 00:00:00-07:00 -3.60 \n", + "2016-04-08 03:00:00-07:00 -5.01 \n", + "2016-04-08 06:00:00-07:00 -3.99 \n", + "2016-04-08 09:00:00-07:00 -2.90 \n", + "2016-04-08 12:00:00-07:00 -2.54 \n", + "2016-04-08 15:00:00-07:00 -1.81 \n", + "2016-04-08 18:00:00-07:00 2.41 \n", + "2016-04-08 21:00:00-07:00 4.62 \n", + "2016-04-09 00:00:00-07:00 5.20 \n", + "2016-04-09 03:00:00-07:00 2.67 \n", + "2016-04-09 06:00:00-07:00 0.26 \n", + "2016-04-09 09:00:00-07:00 -0.30 \n", + "2016-04-09 12:00:00-07:00 -1.75 \n", + "2016-04-09 15:00:00-07:00 -0.62 \n", + "2016-04-09 18:00:00-07:00 4.30 \n", + "2016-04-09 21:00:00-07:00 7.66 \n", + "2016-04-10 00:00:00-07:00 8.93 \n", + "2016-04-10 03:00:00-07:00 4.84 \n", + "2016-04-10 06:00:00-07:00 2.66 \n", + "\n", + " v-component_of_wind_isobaric \n", + "2016-04-03 09:00:00-07:00 1.39 \n", + "2016-04-03 12:00:00-07:00 2.62 \n", + "2016-04-03 15:00:00-07:00 1.46 \n", + "2016-04-03 18:00:00-07:00 0.68 \n", + "2016-04-03 21:00:00-07:00 0.15 \n", + "2016-04-04 00:00:00-07:00 -1.77 \n", + "2016-04-04 03:00:00-07:00 -2.22 \n", + "2016-04-04 06:00:00-07:00 -1.23 \n", + "2016-04-04 09:00:00-07:00 -0.51 \n", + "2016-04-04 12:00:00-07:00 1.10 \n", + "2016-04-04 15:00:00-07:00 0.79 \n", + "2016-04-04 18:00:00-07:00 2.27 \n", + "2016-04-04 21:00:00-07:00 0.90 \n", + "2016-04-05 00:00:00-07:00 0.05 \n", + "2016-04-05 03:00:00-07:00 -1.58 \n", + "2016-04-05 06:00:00-07:00 0.06 \n", + "2016-04-05 09:00:00-07:00 0.06 \n", + "2016-04-05 12:00:00-07:00 0.24 \n", + "2016-04-05 15:00:00-07:00 0.35 \n", + "2016-04-05 18:00:00-07:00 -1.64 \n", + "2016-04-05 21:00:00-07:00 -2.96 \n", + "2016-04-06 00:00:00-07:00 -5.37 \n", + "2016-04-06 03:00:00-07:00 -1.99 \n", + "2016-04-06 06:00:00-07:00 -1.54 \n", + "2016-04-06 09:00:00-07:00 -0.67 \n", + "2016-04-06 12:00:00-07:00 -0.20 \n", + "2016-04-06 15:00:00-07:00 0.43 \n", + "2016-04-06 18:00:00-07:00 -0.24 \n", + "2016-04-06 21:00:00-07:00 0.12 \n", + "2016-04-07 00:00:00-07:00 0.45 \n", + "2016-04-07 03:00:00-07:00 1.98 \n", + "2016-04-07 06:00:00-07:00 4.93 \n", + "2016-04-07 09:00:00-07:00 3.84 \n", + "2016-04-07 12:00:00-07:00 1.66 \n", + "2016-04-07 15:00:00-07:00 0.22 \n", + "2016-04-07 18:00:00-07:00 -2.78 \n", + "2016-04-07 21:00:00-07:00 0.34 \n", + "2016-04-08 00:00:00-07:00 -1.35 \n", + "2016-04-08 03:00:00-07:00 -0.13 \n", + "2016-04-08 06:00:00-07:00 1.65 \n", + "2016-04-08 09:00:00-07:00 1.50 \n", + "2016-04-08 12:00:00-07:00 1.60 \n", + "2016-04-08 15:00:00-07:00 1.74 \n", + "2016-04-08 18:00:00-07:00 3.85 \n", + "2016-04-08 21:00:00-07:00 3.60 \n", + "2016-04-09 00:00:00-07:00 3.04 \n", + "2016-04-09 03:00:00-07:00 1.59 \n", + "2016-04-09 06:00:00-07:00 1.08 \n", + "2016-04-09 09:00:00-07:00 1.80 \n", + "2016-04-09 12:00:00-07:00 2.21 \n", + "2016-04-09 15:00:00-07:00 5.23 \n", + "2016-04-09 18:00:00-07:00 8.53 \n", + "2016-04-09 21:00:00-07:00 8.91 \n", + "2016-04-10 00:00:00-07:00 7.03 \n", + "2016-04-10 03:00:00-07:00 2.11 \n", + "2016-04-10 06:00:00-07:00 4.17 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "data = fm.process_data(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# retrieve data\n", + "data = fm.get_processed_data(latitude, longitude, start, end)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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temperaturewind_speedghidnidhitotal_cloudslow_cloudsmid_cloudshigh_clouds
2016-04-03 09:00:00-07:0011.2500004.719343569.712283829.66830992.6837440.00.00.00.0
2016-04-03 12:00:00-07:009.8500065.627148980.540706989.349943100.7489990.00.00.00.0
2016-04-03 15:00:00-07:0018.7500004.740433749.436252913.97999797.0132800.00.00.00.0
2016-04-03 18:00:00-07:0036.3500063.53600186.669267223.87968552.4368250.00.00.00.0
2016-04-03 21:00:00-07:0044.6499940.6281720.0000000.0000000.0000000.00.00.00.0
2016-04-04 00:00:00-07:0031.9500121.8313380.0000000.0000000.0000000.00.00.00.0
2016-04-04 03:00:00-07:0016.8500062.4389550.0000000.0000000.0000000.00.00.00.0
2016-04-04 06:00:00-07:0013.5500181.3774250.0000000.0000000.0000000.00.00.00.0
2016-04-04 09:00:00-07:0011.5500181.832403574.824061832.51650792.8328740.00.00.00.0
2016-04-04 12:00:00-07:009.8500062.033249984.610605990.448411100.8026160.00.00.00.0
2016-04-04 15:00:00-07:0022.7500002.328132752.580636915.21299997.0753580.00.00.00.0
2016-04-04 18:00:00-07:0039.4500122.31036888.648335229.69320352.9810620.00.00.00.0
2016-04-04 21:00:00-07:0042.8500062.2571890.0000000.0000000.0000000.00.00.00.0
2016-04-05 00:00:00-07:0033.2500003.1803930.0000000.0000000.0000008.00.00.08.0
2016-04-05 03:00:00-07:0018.4500122.7167070.0000000.0000000.00000060.00.00.060.0
2016-04-05 06:00:00-07:0015.1499941.6011250.0000000.0000000.00000067.00.00.067.0
2016-04-05 09:00:00-07:0012.5500181.571146298.314480145.252044213.64706464.00.00.064.0
2016-04-05 12:00:00-07:0010.3500060.264008615.713428396.547447260.66682656.00.00.056.0
2016-04-05 15:00:00-07:0022.3500060.965660316.13425642.614842285.51120489.00.00.089.0
2016-04-05 18:00:00-07:0037.6499941.64778664.6524160.00000364.65241695.00.00.095.0
2016-04-05 21:00:00-07:0040.4500123.9795980.0000000.0000000.00000096.00.00.096.0
2016-04-06 00:00:00-07:0030.9500125.7733260.0000000.0000000.00000090.00.00.090.0
2016-04-06 03:00:00-07:0018.4500122.0873430.0000000.0000000.00000099.00.00.099.0
2016-04-06 06:00:00-07:0015.9500121.5617940.0000000.0000000.00000099.00.00.099.0
2016-04-06 09:00:00-07:0013.7500000.926553240.6429330.000000240.642933100.00.00.0100.0
2016-04-06 12:00:00-07:0011.0500181.434015427.38210493.809306343.11299988.00.00.088.0
2016-04-06 15:00:00-07:0023.7500002.153369359.755645127.022025268.18153976.00.00.076.0
2016-04-06 18:00:00-07:0039.0500184.98578065.6206800.00552365.61979783.00.00.083.0
2016-04-06 21:00:00-07:0043.3500064.3716470.0000000.0000000.00000086.00.00.086.0
2016-04-07 00:00:00-07:0034.1499944.2638130.0000000.0000000.00000093.00.00.093.0
2016-04-07 03:00:00-07:0023.0500186.1759940.0000000.0000000.00000097.00.01.097.0
2016-04-07 06:00:00-07:0021.6499945.9325290.0000000.0000000.00000099.00.09.099.0
2016-04-07 09:00:00-07:0018.6499943.859754242.2166500.000000242.216650100.00.078.0100.0
2016-04-07 12:00:00-07:0017.8500061.686891369.5836460.000000369.583646100.00.088.0100.0
2016-04-07 15:00:00-07:0020.3500060.220227296.5939640.000000296.593964100.00.070.0100.0
2016-04-07 18:00:00-07:0032.2500005.74711266.5841630.00007466.58415192.00.076.088.0
2016-04-07 21:00:00-07:0025.9500123.4667130.0000000.0000000.000000100.01.099.0100.0
2016-04-08 00:00:00-07:0027.1499943.8448020.0000000.0000000.00000081.00.078.080.0
2016-04-08 03:00:00-07:0016.9500125.0116860.0000000.0000000.00000015.00.02.014.0
2016-04-08 06:00:00-07:0016.4500124.3177080.0000000.0000000.00000054.00.028.053.0
2016-04-08 09:00:00-07:0015.2500003.264966244.2601641.182951243.55700598.00.084.098.0
2016-04-08 12:00:00-07:0013.1499943.001933383.79132720.607123365.16148197.00.058.097.0
2016-04-08 15:00:00-07:0021.3500062.510717311.94276628.402913291.33706092.00.03.092.0
2016-04-08 18:00:00-07:0034.1499944.54209268.1041294.85267667.30488049.00.01.048.0
2016-04-08 21:00:00-07:0031.7500005.8569960.0000000.0000000.00000042.042.00.00.0
2016-04-09 00:00:00-07:0026.8500066.0234210.0000000.0000000.00000059.039.06.021.0
2016-04-09 03:00:00-07:0016.4500123.1075720.0000000.0000000.00000028.00.00.027.0
2016-04-09 06:00:00-07:0013.3500061.1108560.0000000.0000000.00000014.00.00.014.0
2016-04-09 09:00:00-07:0011.5500181.824829599.428545845.79667093.5254490.00.00.00.0
2016-04-09 12:00:00-07:0010.4500122.8189711003.859445995.556452101.0516660.00.00.00.0
2016-04-09 15:00:00-07:0020.8500065.266621767.573173920.99815997.3661740.00.00.00.0
2016-04-09 18:00:00-07:0031.8500069.55253498.694727258.22647955.5615740.00.00.00.0
2016-04-09 21:00:00-07:0033.35000611.7500510.0000000.0000000.0000000.00.00.00.0
2016-04-10 00:00:00-07:0025.25000011.3651130.0000000.0000000.0000000.00.00.00.0
2016-04-10 03:00:00-07:0014.8500065.2799340.0000000.0000000.0000000.00.00.00.0
2016-04-10 06:00:00-07:0012.3500064.9461600.0000000.0000000.0000005.00.00.06.0
\n", + "
" + ], + "text/plain": [ + " temperature wind_speed ghi dni \\\n", + "2016-04-03 09:00:00-07:00 11.250000 4.719343 569.712283 829.668309 \n", + "2016-04-03 12:00:00-07:00 9.850006 5.627148 980.540706 989.349943 \n", + "2016-04-03 15:00:00-07:00 18.750000 4.740433 749.436252 913.979997 \n", + "2016-04-03 18:00:00-07:00 36.350006 3.536001 86.669267 223.879685 \n", + "2016-04-03 21:00:00-07:00 44.649994 0.628172 0.000000 0.000000 \n", + "2016-04-04 00:00:00-07:00 31.950012 1.831338 0.000000 0.000000 \n", + "2016-04-04 03:00:00-07:00 16.850006 2.438955 0.000000 0.000000 \n", + "2016-04-04 06:00:00-07:00 13.550018 1.377425 0.000000 0.000000 \n", + "2016-04-04 09:00:00-07:00 11.550018 1.832403 574.824061 832.516507 \n", + "2016-04-04 12:00:00-07:00 9.850006 2.033249 984.610605 990.448411 \n", + "2016-04-04 15:00:00-07:00 22.750000 2.328132 752.580636 915.212999 \n", + "2016-04-04 18:00:00-07:00 39.450012 2.310368 88.648335 229.693203 \n", + "2016-04-04 21:00:00-07:00 42.850006 2.257189 0.000000 0.000000 \n", + "2016-04-05 00:00:00-07:00 33.250000 3.180393 0.000000 0.000000 \n", + "2016-04-05 03:00:00-07:00 18.450012 2.716707 0.000000 0.000000 \n", + "2016-04-05 06:00:00-07:00 15.149994 1.601125 0.000000 0.000000 \n", + "2016-04-05 09:00:00-07:00 12.550018 1.571146 298.314480 145.252044 \n", + "2016-04-05 12:00:00-07:00 10.350006 0.264008 615.713428 396.547447 \n", + "2016-04-05 15:00:00-07:00 22.350006 0.965660 316.134256 42.614842 \n", + "2016-04-05 18:00:00-07:00 37.649994 1.647786 64.652416 0.000003 \n", + "2016-04-05 21:00:00-07:00 40.450012 3.979598 0.000000 0.000000 \n", + "2016-04-06 00:00:00-07:00 30.950012 5.773326 0.000000 0.000000 \n", + "2016-04-06 03:00:00-07:00 18.450012 2.087343 0.000000 0.000000 \n", + "2016-04-06 06:00:00-07:00 15.950012 1.561794 0.000000 0.000000 \n", + "2016-04-06 09:00:00-07:00 13.750000 0.926553 240.642933 0.000000 \n", + "2016-04-06 12:00:00-07:00 11.050018 1.434015 427.382104 93.809306 \n", + "2016-04-06 15:00:00-07:00 23.750000 2.153369 359.755645 127.022025 \n", + "2016-04-06 18:00:00-07:00 39.050018 4.985780 65.620680 0.005523 \n", + "2016-04-06 21:00:00-07:00 43.350006 4.371647 0.000000 0.000000 \n", + "2016-04-07 00:00:00-07:00 34.149994 4.263813 0.000000 0.000000 \n", + "2016-04-07 03:00:00-07:00 23.050018 6.175994 0.000000 0.000000 \n", + "2016-04-07 06:00:00-07:00 21.649994 5.932529 0.000000 0.000000 \n", + "2016-04-07 09:00:00-07:00 18.649994 3.859754 242.216650 0.000000 \n", + "2016-04-07 12:00:00-07:00 17.850006 1.686891 369.583646 0.000000 \n", + "2016-04-07 15:00:00-07:00 20.350006 0.220227 296.593964 0.000000 \n", + "2016-04-07 18:00:00-07:00 32.250000 5.747112 66.584163 0.000074 \n", + "2016-04-07 21:00:00-07:00 25.950012 3.466713 0.000000 0.000000 \n", + "2016-04-08 00:00:00-07:00 27.149994 3.844802 0.000000 0.000000 \n", + "2016-04-08 03:00:00-07:00 16.950012 5.011686 0.000000 0.000000 \n", + "2016-04-08 06:00:00-07:00 16.450012 4.317708 0.000000 0.000000 \n", + "2016-04-08 09:00:00-07:00 15.250000 3.264966 244.260164 1.182951 \n", + "2016-04-08 12:00:00-07:00 13.149994 3.001933 383.791327 20.607123 \n", + "2016-04-08 15:00:00-07:00 21.350006 2.510717 311.942766 28.402913 \n", + "2016-04-08 18:00:00-07:00 34.149994 4.542092 68.104129 4.852676 \n", + "2016-04-08 21:00:00-07:00 31.750000 5.856996 0.000000 0.000000 \n", + "2016-04-09 00:00:00-07:00 26.850006 6.023421 0.000000 0.000000 \n", + "2016-04-09 03:00:00-07:00 16.450012 3.107572 0.000000 0.000000 \n", + "2016-04-09 06:00:00-07:00 13.350006 1.110856 0.000000 0.000000 \n", + "2016-04-09 09:00:00-07:00 11.550018 1.824829 599.428545 845.796670 \n", + "2016-04-09 12:00:00-07:00 10.450012 2.818971 1003.859445 995.556452 \n", + "2016-04-09 15:00:00-07:00 20.850006 5.266621 767.573173 920.998159 \n", + "2016-04-09 18:00:00-07:00 31.850006 9.552534 98.694727 258.226479 \n", + "2016-04-09 21:00:00-07:00 33.350006 11.750051 0.000000 0.000000 \n", + "2016-04-10 00:00:00-07:00 25.250000 11.365113 0.000000 0.000000 \n", + "2016-04-10 03:00:00-07:00 14.850006 5.279934 0.000000 0.000000 \n", + "2016-04-10 06:00:00-07:00 12.350006 4.946160 0.000000 0.000000 \n", + "\n", + " dhi total_clouds low_clouds mid_clouds \\\n", + "2016-04-03 09:00:00-07:00 92.683744 0.0 0.0 0.0 \n", + "2016-04-03 12:00:00-07:00 100.748999 0.0 0.0 0.0 \n", + "2016-04-03 15:00:00-07:00 97.013280 0.0 0.0 0.0 \n", + "2016-04-03 18:00:00-07:00 52.436825 0.0 0.0 0.0 \n", + "2016-04-03 21:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 00:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 03:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 06:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 09:00:00-07:00 92.832874 0.0 0.0 0.0 \n", + "2016-04-04 12:00:00-07:00 100.802616 0.0 0.0 0.0 \n", + "2016-04-04 15:00:00-07:00 97.075358 0.0 0.0 0.0 \n", + "2016-04-04 18:00:00-07:00 52.981062 0.0 0.0 0.0 \n", + "2016-04-04 21:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-05 00:00:00-07:00 0.000000 8.0 0.0 0.0 \n", + "2016-04-05 03:00:00-07:00 0.000000 60.0 0.0 0.0 \n", + "2016-04-05 06:00:00-07:00 0.000000 67.0 0.0 0.0 \n", + "2016-04-05 09:00:00-07:00 213.647064 64.0 0.0 0.0 \n", + "2016-04-05 12:00:00-07:00 260.666826 56.0 0.0 0.0 \n", + "2016-04-05 15:00:00-07:00 285.511204 89.0 0.0 0.0 \n", + "2016-04-05 18:00:00-07:00 64.652416 95.0 0.0 0.0 \n", + "2016-04-05 21:00:00-07:00 0.000000 96.0 0.0 0.0 \n", + "2016-04-06 00:00:00-07:00 0.000000 90.0 0.0 0.0 \n", + "2016-04-06 03:00:00-07:00 0.000000 99.0 0.0 0.0 \n", + "2016-04-06 06:00:00-07:00 0.000000 99.0 0.0 0.0 \n", + "2016-04-06 09:00:00-07:00 240.642933 100.0 0.0 0.0 \n", + "2016-04-06 12:00:00-07:00 343.112999 88.0 0.0 0.0 \n", + "2016-04-06 15:00:00-07:00 268.181539 76.0 0.0 0.0 \n", + "2016-04-06 18:00:00-07:00 65.619797 83.0 0.0 0.0 \n", + "2016-04-06 21:00:00-07:00 0.000000 86.0 0.0 0.0 \n", + "2016-04-07 00:00:00-07:00 0.000000 93.0 0.0 0.0 \n", + "2016-04-07 03:00:00-07:00 0.000000 97.0 0.0 1.0 \n", + "2016-04-07 06:00:00-07:00 0.000000 99.0 0.0 9.0 \n", + "2016-04-07 09:00:00-07:00 242.216650 100.0 0.0 78.0 \n", + "2016-04-07 12:00:00-07:00 369.583646 100.0 0.0 88.0 \n", + "2016-04-07 15:00:00-07:00 296.593964 100.0 0.0 70.0 \n", + "2016-04-07 18:00:00-07:00 66.584151 92.0 0.0 76.0 \n", + "2016-04-07 21:00:00-07:00 0.000000 100.0 1.0 99.0 \n", + "2016-04-08 00:00:00-07:00 0.000000 81.0 0.0 78.0 \n", + "2016-04-08 03:00:00-07:00 0.000000 15.0 0.0 2.0 \n", + "2016-04-08 06:00:00-07:00 0.000000 54.0 0.0 28.0 \n", + "2016-04-08 09:00:00-07:00 243.557005 98.0 0.0 84.0 \n", + "2016-04-08 12:00:00-07:00 365.161481 97.0 0.0 58.0 \n", + "2016-04-08 15:00:00-07:00 291.337060 92.0 0.0 3.0 \n", + "2016-04-08 18:00:00-07:00 67.304880 49.0 0.0 1.0 \n", + "2016-04-08 21:00:00-07:00 0.000000 42.0 42.0 0.0 \n", + "2016-04-09 00:00:00-07:00 0.000000 59.0 39.0 6.0 \n", + "2016-04-09 03:00:00-07:00 0.000000 28.0 0.0 0.0 \n", + "2016-04-09 06:00:00-07:00 0.000000 14.0 0.0 0.0 \n", + "2016-04-09 09:00:00-07:00 93.525449 0.0 0.0 0.0 \n", + "2016-04-09 12:00:00-07:00 101.051666 0.0 0.0 0.0 \n", + "2016-04-09 15:00:00-07:00 97.366174 0.0 0.0 0.0 \n", + "2016-04-09 18:00:00-07:00 55.561574 0.0 0.0 0.0 \n", + "2016-04-09 21:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-10 00:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-10 03:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-10 06:00:00-07:00 0.000000 5.0 0.0 0.0 \n", + "\n", + " high_clouds \n", + "2016-04-03 09:00:00-07:00 0.0 \n", + "2016-04-03 12:00:00-07:00 0.0 \n", + "2016-04-03 15:00:00-07:00 0.0 \n", + "2016-04-03 18:00:00-07:00 0.0 \n", + "2016-04-03 21:00:00-07:00 0.0 \n", + "2016-04-04 00:00:00-07:00 0.0 \n", + "2016-04-04 03:00:00-07:00 0.0 \n", + "2016-04-04 06:00:00-07:00 0.0 \n", + "2016-04-04 09:00:00-07:00 0.0 \n", + "2016-04-04 12:00:00-07:00 0.0 \n", + "2016-04-04 15:00:00-07:00 0.0 \n", + "2016-04-04 18:00:00-07:00 0.0 \n", + "2016-04-04 21:00:00-07:00 0.0 \n", + "2016-04-05 00:00:00-07:00 8.0 \n", + "2016-04-05 03:00:00-07:00 60.0 \n", + 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09:00:00-07:00 98.0 \n", + "2016-04-08 12:00:00-07:00 97.0 \n", + "2016-04-08 15:00:00-07:00 92.0 \n", + "2016-04-08 18:00:00-07:00 48.0 \n", + "2016-04-08 21:00:00-07:00 0.0 \n", + "2016-04-09 00:00:00-07:00 21.0 \n", + "2016-04-09 03:00:00-07:00 27.0 \n", + "2016-04-09 06:00:00-07:00 14.0 \n", + "2016-04-09 09:00:00-07:00 0.0 \n", + "2016-04-09 12:00:00-07:00 0.0 \n", + "2016-04-09 15:00:00-07:00 0.0 \n", + "2016-04-09 18:00:00-07:00 0.0 \n", + "2016-04-09 21:00:00-07:00 0.0 \n", + "2016-04-10 00:00:00-07:00 0.0 \n", + "2016-04-10 03:00:00-07:00 0.0 \n", + "2016-04-10 06:00:00-07:00 6.0 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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kod7phTlr0Ic/ungEsWQB/7abyjS0YjyawZ27XkcsWcDVf7gWQa8do120f4iC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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data['temperature'].plot()\n", + "plt.ylabel('temperature (%s)' % fm.units['temperature'])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "cloud_vars = ['total_clouds', 'low_clouds', 'mid_clouds', 'high_clouds']" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ZM7nrrrv4wQ9+QGtra4+etzQGEEJ02c6vKmgpbcSjKNwRoCp0ZzNz5lhe/8tm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\nEEIIIYQQ7d7/B13YFC3tn3RFAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for varname in cloud_vars:\n", + " data[varname].plot()\n", + "plt.ylabel('Cloud cover' + ' %')\n", + "plt.xlabel('Forecast Time ('+str(data.index.tz)+')')\n", + "plt.title('GFS 0.5 deg')\n", + "plt.legend(bbox_to_anchor=(1.18,1.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "total_cloud_cover = data['total_clouds']" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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VpaQ+8RhJy5eSPugWlLzcat8m4eOPsP3yM/4mTU+apERdQgLFl18F1O6qdOKSxaVJ9NAR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "total_cloud_cover.plot(color='r', linewidth=2)\n", + "plt.ylabel('Total cloud cover' + ' (%s)' % fm.units['total_clouds'])\n", + "plt.xlabel('Forecast Time ('+str(data.index.tz)+')')\n", + "plt.title('GFS 0.5 deg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GFS (0.25 deg)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# GFS model at 0.25 degree resolution\n", + "fm = GFS(resolution='quarter')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# retrieve data\n", + "data = fm.get_processed_data(latitude, longitude, start, end)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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paIDL1XUDo+IKB1VVLsyomM0qUfHhlJTUYK+SIa9CdJT09HTGjx/Prbfeiq7r\nTJgwgauuugqA3r1706dPHwD69+9Pjx5Nf5799Kc/5Re/+AV//vOfiYiI4Pe//z379+8H4JZbbuHx\nxx9nwYIFOJ1O7r//fhISEli0aBG//OUv6d27Nz179gRg6NChTJs2jblz55KUlBR43NGjR/PYY48R\nHh6OyWTiV7/61Xk9d0X356guMEVFlaFeQtAlJUV3y+fdmchr0DCny8Nfn/sGM3DRzMFM7OCmCxfi\n6/CXv2xGK3UQ3S+W224dF+rltJpX03jxuW+weDQiU2P4yUOXX3CvQWeW+9vf4Mg6SsJ119Nj9twW\n3efVlzfjLnOQPLInc2cN6+AVhsaWzALeWr2fkajE94ggtl8cOTtOoUdZ+a/7Lw318oAL8/2oqwnG\na5CUFN2hxxedg2SMhBDnbf1XxzADbpPC+NEpoV5OpzR0RAqZ3+RQcrKi+Rt3Qp99fhiLx5hZNGd2\n42UTovVqso7iyDLKQkrX/pv471+NKSqq2fuZzCpuunbGqHbjhZhYG/Hx4eQA3i78nIW40Ljdbv7j\nP/6jXpvv/v378+STT4ZoVW0jgZEQ4rwd2ZuPGegzKDGwWVPUNfniVPZ+k4PFq3PwyBmGDrpwyukq\nKh3k+F7jAWN6ERVpDfWSupTSNZ8DoJjN6E4HpWs+p8ecHzR7P7OvEYHb7e3Q9YVS7cYL0bHhJPeI\nBEB3a6FblBCiDovFwttvvx3qZbQL+QYjhDgvR7KLMTu9eIHpVwwM9XI6rTCrGUu80Z1u+7YLqzvd\nP1bux6yDx6Iyc8agUC+nS3EXFVG1cweYTKTcfS8ApevW4q1svizIbDEZx3B1zcDI5fZyorAKG8ZZ\n6Jg4GynJRibNpOtomgRHQoj2JYGREOK8fPOVMYTNEm8jLtYW4tV0bsNHGWWGJXkXTjlddm4p9lPG\neqd8fxAmyQi2q9J1a0DXibl4MtETJhIxchS600GJL4vUFH9g5O2i2ZOc/Eq8mk6M1XieMXE2IiKs\neAEVhTMlNaFdoBCiy5FPOCFEm9U43FTlGxPoJ13aL8Sr6fwmTUzFA1i8OgcOF4V6OS3y+eoDqCgo\nsTbGj+4V6uV0Kd5qO+Vffw1A/IyrAUi8YTYAZV+sxVPZdABt9QVGHk/XzBgdzTPa+ob7gvHoWKPb\npeZrU15YVBWahQkhuiwJjIQQbbZ+Q7bRdMGsMHpEz1Avp9OzWs2E+crpduw4GeLVNG/T1hMoVS68\nwPU3DQ+mWBnbAAAgAElEQVT1crqc8q82oDsdRAwbTlhaXwDCBwwgctRodKeT0n81nTWyhvkyRp4L\nsrlss7J8gRG+UsEY36Bk1RcQnimWIa9CiPYlgZEQos2yMgsBSBucJE0XWihjSBIAJac7d3tfj0dj\nu282VXx6HH16xYR4RV2L7vFQtm4tAPEzZta5LvGGmwBf1qii8axRmNXon6R5ul4pna7rZOWVYwF0\nTSfMZsYaZjxfS7jx3/IyGfIqRLA98MAD9S577733eOGFF1p1nBdeeIHly5ef11oefvhhtm3bdl7H\nOJd8kxFCtMnBI2dqNV0Y0OztheHiialo6JhdXopLOu8Z748/O4jFq+NWYPYNki1qb5Xbt+IpLcHa\nqzcRI+q2P7f1H0Dk6DHoLhel//pno8cIs/kCI2/nCIyO5pWz/1hJuxyrqNxBRbWbOF/wF1NraLQt\nwgJAZYUERkIE25/+9KdQL6FDSbtuIUSbbPzaaLoQlhhOTLQ0XWip6KgwNJsZs8PLlu0nuXbG4FAv\nqZ6yshpOZhZiBoZMTCUiQtpztydd1yld8y8A4r9/NUoD2dbEG2Zj3/MdZevXEX/1NZhj6mfsbL7A\nSNdCX0qn6zrPr9hDVY2b//fDS0iqFci0RdZJo4wuNT4cCuyBMjqAqGgb1VRSY3ed12MIIepbuXIl\n69evx+FwcObMGRYtWsS6des4cuQIP/vZz3jiiSf45ptv2L59O08//TRxcXGoqsrYsWMbPWZJSQmP\nPvooFb4M+DPPPFPn+meeeYYdO3agKAqzZs1i0aJFLF26lOuuu46pU6fy9ddf89lnn/Hb3/6Wd955\nhw8//JCkpCRKSowTMTk5OSxduhSz2Yyu6zz77LP07Nm28n4JjIQQrVZd7aKm0I4JmHRpeqiXc8FJ\nTo2l5GgJuVnFoV5Kg/6xch9mwGM18X3JBra7mkMHceYexxQdTfQllzR4G1t6OpFjxmL/bjeln39G\n0i3z690m3GZkTjpDYFTj9FBZ7Qbg2/353DCl/3kd7+gpIzBKDLdQDkTX6ngZG2ejEHDVyJBX0XVl\n/uopSnfsbNdjxk8Yz/BfPNbs7ex2O6+99hqfffYZb775JsuXL2fr1q28+eabgds8+eSTvPjii/Tt\n25df/vKXTR7vpZdeYvr06cybN4/du3ezd+/ewHVffvkleXl5vP/++3g8HhYuXMikSZMaPE5xcTFv\nvfUWn376KQBz584FYOPGjYwZM4af/vSnbNu2jcrKyjYHRlJKJ4RotS82ZGMC3GZVmi60wbhxvQFw\nlzvxdrJZLIezzuAoqEJH5/KrB8vesQ5Q+m8jWxR3xXRUS+PZuMBeoy+/wFNeVu/6cN9eG6UTBEal\nlc7Avzfty0fXz29N/sYL4crZGUZ+CQkRAHhdEhgJ0RGGDzfKp6OjoxkwwDg5FhMTg9N59u+8uLiY\nvn2NpjHjx49v8njHjh0LZJTGjh3LrFmzAtdlZWUxYcIEAMxmM6NHj+bo0aN17u9/P8nNzWXw4MGY\nzWbMZjOjRo0C4OabbyYqKorFixfz7rvvYjKZ2vzcJWMkhGi1YweMMqt+Q5NCvZQL0sD+CXyugEWH\n7/YVdKo22Gs+PYQJBVNCuAS9HcCVfxr7d7tRzGZiv3dlk7e19Usncuw47Lt3UfL5P0med2ud68PD\njYwR5xmEtIfagVFhaQ3ZpyrI6BPbpmM5XB5OFtpRFQV8M5pq7zHqmRQJgOIN/fMWoqO0JLPTURTf\nCYmmpKSkkJ2dzYABA9i7dy+xsY3/vQ8cOJA9e/YwZMgQtm3bxoYNG7DZbIHrVqxYwR133IHb7WbX\nrl3MmTOHLVu2UFRkjLXIzMwEoF+/fhw5cgSXy4XJZCIzM5Mbb7yRtWvXMnHiRO6//34+/fRTXnnl\nFZ5++uk2PXcJjIQQrZJ5qAizS8MLXPk9KbNqC1VViUiMwH2mmv17T3eawOirjTmYqt14gdmzR4R6\nOV1S6b/XABBz6ZQG9w2dK/GGm7Dv3kX5l1+QcPU1mOPiAtfZfF3aVMCraSEdvltSKzACI2vU1sDo\n2OlKNF2nX89o7L4GC7VL6ZKTItHRMengcnmwWuWrjBDB4g+afvnLX/Kzn/2M6OhoIiMjmwyM7rnn\nHpYtW8bq1atRVZWnnnqKVatWAXD55ZezefNm5s+fj9vt5tprr2XYsGHcfPPNLFu2jI8//pj09HQA\nEhISuPvuu5k3bx4JCQlERhonSUaNGsWSJUt46aWX0DSNZcuWtfn5ybuJEKJVNn2TA0BYjwiio8JC\nu5gLWMbgHhw8k0tpfucYUulyedi96TgWICkjgZSkqFAvqcvxVlZSsekbAOKuurpF97H17UfUuAlU\n7dpByeefkTx/QeA6q9UoF1EBp0sjwha6wKi00kk4MLRPLLvyytl6oID50wdhMbd+Tf4yugG9o6na\nnQ9AdMzZwMhiNuFVFMw6FBbZSW1jACaEqG/27NmBf0+bNo1p06YBMHToUF599dXAdaNHj+bDDz9s\n0TETEhJ4+eWX61x2//33B/69ZMmSevcZOXIkq1evrnf53LlzA3uLanv33XdbtJbmSGAkhGgxe7UL\nR5HRdOGSKf1CvZwL2kUTUsnclIvZ7aWwqIrkEAciH31ywGjPrSrcdKO05+4IZRvWo7vdRI4aTVjv\n3i2+X+INN1G1awflG9aTMPPaQNbIbKkVGLm9RNhC95FeUlHDYBQsp6vo2yOS3DN29mQVM2FI68tt\n/YFR3/gIMnWIjA7DdE6ApZtU8GgUnJHASIjO4kc/+hHl5eWBn3VdJyYmhhdffDGEq2odCYyEEC22\nbn2W0XTBojJymOw/OR9RkVb0cDNqjYetO04ya+bQkK2luKSagsPFmIARk1IDg0NF+9HcLsq+aHig\na3PC0tKImjCRqh3bKfnnpyTfuhA4GxiZUHC6PEDoMrilFU7CUdA1nbFpceSesbNp3+lWB0a6rpN1\nymjpm+hrE1+78YKfKcwEHo2S4s47C0yI7ub5558P9RLOm7QbEkK0WM5BYyNkujRdaBc9U40z3Sey\nS0O6jpX/2IcJ8NrMXDHt/Nosi4ZVbtmCt6KCsLQ0wocOa/X9E6+/EYDyDetxlxr/v6iqgr/9QHWN\nu72W2iaV5WeHrfaOCkNRYE9WMZXVrZs1VFBaQ1WNm5hIK4q/8UJs/cDI6ms8UV4uQ16FEO1HAiMh\nRIvsO1CAxW00XZh+RUaol9MljB/fBwBPhROPJ/htu6urXbzyyhbcZ6rR0bnymiHSnrsD6LoeaNEd\n//2ZLer4dK6wVCNrpHs8lP7zk7PHVo1j1ThCGxg5qs4GQPZyByP6J+DVdLYeKGzVcfxldBm9Y6j0\nN15oYFhseKQRGFWd0/RBCCHOh3wCCiFa5NuNxwGwJUUSGdH47BXRchn9E3CrRk3z7n35QX3sw1ln\nePXFb/EU16Ch02dkCsPbsB9ENK86cz+uvJOYYuOIvrjhwYUtkXjDTaAolH+1Abdv4rviD4yqQzfT\nx+ny4nV7Az8XF1Zx6cgUwBj22hr+wGhgaiyVZTVAwxmjmGjjMkd1aANCIUTXIoGREKJZlVVOnGeM\nWv5Lp6aHdjFdTGSi0W40M4iB0aefH2LNB/sCzRYuu2E4N85qfXmXaJnSNZ8DED/9KhRz2/dvhfVJ\nJWrCRegeDyX/NCa/KyZfYOQMXYBQWuXEytksWEmRnTEZPbBZTWSfquB0sb3FxzqaZ+wvyugdS0WZ\nkTFqaI9RXLyRRXKHOFMmhOhaJDASQjTL33TBYzVJVqGdDfLt1yoLQttue7WLv/xlM7m7T2MCiAnj\nzvsmMWq4NNLoKM68k1Tv34ditRJ72ffO+3iJN9wIikLF1xtwlxSjmoyPcacjdBmj0goHtXPIXq+O\no9LJxCHJQMuzRjVOD3lFVZhUhfSUaCrKGy+l65EYAYDuDn4JqhBd2cqVK3nuuefqXPaTn/wEj6fx\n95ipU6ee12NeeeWVuFyt249Ym8vl4sormx6Y3VISGAkhmpV76AwA/YdLUNTeJo7rjQZYPBr5BZUd\n9jgHj5zhtRe/RSt1oAG9R/bkh/dOCpQkiY5RusbYWxQzZRqmqPNvyR7Wuw/RF11sZI0++zQQGDmc\n3mbu2XFKKp2BwMjim610pnY53b4CNF1v5N5nZZ+uQAf69oxC0XUc1W5MJoXIqPqluz2Tjd+l6m3+\nuEKI8/Pss89iPo9sd3Pasu+yNl3Xz/sYftKTVQjRpO/25WPxGE0XrrxsQKiX0+VERFjRI8xQ7WHb\njjyuv7b923Z//NlBcvfkYwHcJoWrrh/G8KHJ7f44oi5PeRmVW74FRSH+qhntdtyEWTdSuW0r5V9v\nwDxmAB7A5QxhxqhWYJSaHs+xw2coLrRz8eXJJMaEUVzh4MiJMob0jW/yOIHGC33OltFFxdoa/MIT\nH2dDA0xAld1FVKTsexSivezatYvFixdTWlrK/Pnzefnll/n888/Jz8/n0UcfxWKx0Lt3b/Ly8njr\nrbdwuVw88sgjnDp1ivj4eP70pz9hMpkaPPb69esDc42GDx/Ok08+ie47cZKXl8eyZcvQNCMT/POf\n/5whQ4YwdepUvvnGGI798MMPc+uttzJixAgeeeQRKisrSUtLCxz/nXfe4aOPPkJVVUaNGsVjjz3W\nqucugZEQoklbNhlNFyKSI4mQpgsdIiUtjqJDZzhxrKRdj1tZ5eSdv+9CL3OgAkqcjbtuG0d0VOjm\n3XQnZevXoXs8RI4bj7Vn+5UrhvXuTfRFk6jcuhlbTTkOInG5QpgxqlVK1y8j0RcYVaEqCpNHpPDp\nt8fZuC+/BYGRsb9oYJ/YQBldTANldACqquJVQNWhoLCKqP4J7fZ8hOgM3n11C0db2dWxOQOHJbPg\nP5tvAGO1WnnttdfIy8vjnnvuCZyc+N3vfsd9993HtGnT+OCDD8jLywOgurqan/zkJ/Tq1YtFixaR\nmZnJqFGj6h3X6/Xy61//mhUrVhAfH89rr71Gfn5+4PjPPPMMd955J1dccQUHDx5k2bJlrFixos4x\n/Ld97733GDx4MD/+8Y/Zs2cPW7ZsAWDVqlU88cQTjBw5kvfeew9N01rVbVVK6YQQjSqvcOAqMTpD\nTZH5Nh1m/ASjbbdW6cLtaZ8vuJkHC3n9pc3oZQ68QOroFO69d7IERUGiOZ2UfbkegIRWDnRtCVuG\n0TLfohnNB9whDIzKyh2YUFDNKr37GrO5iguNPXP+crrtBwtxuhtfo6brtVp1x1Lpb7zQQEc6P8U3\n4LawqOXNHYQQzRs+fDgASUlJ1NTUBC7Pyspi3LhxAEyYMCFweWxsLL169Qrcx+FoeL5YaWkpcXFx\nxMcbJ0kWL14cuB9AdnY2EydOBGDo0KEUFBTUO4Y/m5STk8Po0aMBGD16dKDU7+mnn+add95h0aJF\nnDp1KpCNaqmgZ4w8Hg9LliwhLy8Ps9nMr3/9a0wmE48++iiqqjJo0CCeeOKJYC9LCNGAdV/6mi6E\nmRgyqEeol9NlDegbj1tVsGg6O787zaQJqed1vI8+OcDJfQWB0rkZN41gqLx+QVXx7Ua0qirC0vtj\nGzio3Y+v2oyAwawbwYa7iaCjo1WUO0nEmC0UExeOxWrCXuWiptpFr8RI+veK4djpCnYfOcOkRhp9\n5BdXU+30EB8dRkJMGAfKjS9j0Q10pPMzh5nA5aW0tLojnpYQIdWSzE5HaWy/zuDBg9m5cyeXXXYZ\nu3fvbvb250pMTKSiooKKigpiYmL4zW9+ww033BC4PiMjg23btnHllVdy4MABevQwPrc8Hg81NTWY\nTCaOHj0KwMCBA9m1axdXXnklmZmZgeYQ77//Pk8++SRWq5XFixeza9euQLDVEkEPjDZs2ICmabz3\n3nts2rSJP/7xj7jdbh5++GEmTpzIE088wdq1a7nqqquCvTQhxDnyskowAwOGy36UjhbVIwJnoZ2D\nmQXnFRht+CaHU/sKUAFTQji33zZO5k6FQPlXGwCIn3F1u20Kri0QGPkyRp4QBkY1dmPIalSMsR8o\nISmSgrwKigvtpKZbuXRkCsdOV7BpX36jgVHtwa6Kopxt1d1Exigs3IKz0kVFRcNnp4UQ7cP/HvbI\nI4+wbNkyXn/9daKiorBYLI3etrHjPPHEE9xzzz2YTCaGDx8eyPoA/OxnP+Pxxx/nb3/7Gx6Ph6ef\nfhqAO+64g1tuuYW0tDT69DEqLObPn8/PfvYzFi5cSP/+/bFajc+5wYMHs2DBAiIjI0lJSalz/JYI\nemCUnp6O1+tF13UqKysxm8189913gWjusssuY9OmTRIYCdEJ6L7ynKHSorvDDR6WzN7CY5QXtL1t\nt8vl4btNx7EAMf1iWXjruPZboGgxzVGD80QumExEjR3fIY+h2oy9N2bNDSp4PKFpW+32aHicHkAl\nzrcfKDE5yhcYVZGaHs/Fw5J5b90R9h0rprzKSWwD5ZxZp842XgCobGaPEUBkdBjOQjvVlW1v8yuE\nqGv27NmBf1utVr744ovAz7t37+bpp58mLS2NDz74IJA18jdGAKODXVOmTZvGtGnT6ly2bt06APr0\n6cPf/va3eve57777uO++++pd/j//8z/1Lrv55pu5+eabm1xDU4IeGEVGRnLy5ElmzpxJWVkZL7/8\nMtu3b69zfWVlx7WsFUK0jKZpmHyluT2Tzr/NsGjaxHF92L3hGBaPTt7pCvr0imn1MVZ9fACLZgxt\n/cHc+htfRXA4jh0DXScsrS+qtWOydWczRk5QwRuiwKis1nDXqBgj4ElMMoYW+/cZRUdYGZ2RyK4j\nZ9iSWcCMi/vWO05gsGufWHRdp6LMKKVraLirX3RMGCWAs0aGvAoRDL169eLHP/4x4eHhmEwmnnrq\nqQZvt2fPHn7/+98Hskf+dtrXXnst8+fPD+aSWy3ogdEbb7zBtGnTeOihhygoKGDRokW43Wff1Ox2\nOzExrf9CIIRoXxUVTlTAC9IKNwhsNjNKhAWq3WzfkUefWa17Hyw8Y6fwSDEmYNQlfQmzStPRUKnJ\nMmrgwzMGdthj+AMjk8cFZtBCFBjVbtXtD4x6+GYMFReebYpw6cgUdh05w6Z9+fUCo2qHm1Nn7JhN\nCv16RuOoceNxa1jDTITZ6pfq+CUkRHAc8IRwhpMQ3cnEiRPrdYlryOjRo3n77beDsKL2F/RPztjY\n2EDniOjoaDweD8OHD2fr1q1cfPHFfPXVV0yePLnZ48THR2A2N9wjvStLSooO9RK6ve7yGuT7Oj3p\nqtIpn3NnXNP56j8kieO7TnE6t6zVz+/117djAvQICz+4aWSr2pO2VVd8DdpD0YkcAJLHjuyw35FD\nS+Q4YHI7wGackQ3F65F5ojwQGPVJjScpKZqYaKP8rbS4moSESEwmlenxEbz5+SFyC6uwe3TSa2VE\ndx40WhIPTI2jd69YTh4vBSAhMbLJ5zQwowe71mWBRwv5/4uhfnwhr4FoH0EPjO644w6WLVvGwoUL\n8Xg8PPLII4wYMYKf//znuN1uMjIymDmz+dam3bELTVJSNEVFUmYYSt3pNcj2z9SxqJ3uOXfV12HE\niGSO7zqFp8JJXl4p1hZmffYfLKSmwPh9XHnNEIqLO759cVd9Dc6XrmmUHzgEgDu5T4f9jrzVRpZE\ndRt7cTS3FpLXI/dUOf6cjlc7u4aYOBsVZQ6OHCwg0ZdBmjg0mS935fHp11nccsXZbNqOzNMA9E2O\noqioktycYgDCo6xNPidbmHFyVNV0CgrKg3IyoCHytxB6wXgNJPDqHoIeGEVERDS4WepCTbkJ0VWV\n+U4+mK3dLzMbKv1S43CbFCxenR27T3PJxWnN3kfTNL74/BBmFKxJkdKWO8TcBflo1XZMcXGYExI7\n7HECpXQuYy+OrrVuVkd7Ka6oqVdKB0YDhooyB8WFVYHA6NKRKXy5K49v9+fzg8szUFVj/4G/I93A\neo0XGt9fBEaJrxcwAaVlDhITItrviQkhuiUZ8CqEaFBlhdGC1xreeI2/aH/Rvo3rBw/UH2zXkC++\nOobZ4cUD3DRnRAeuTLRETVYWYOwv6og23X6K2YxiNqN6jL9TQhQY1R7uag07e641sM+o1vDVjN4x\nJMeHU17lIvO4kZHWdJ3s02cbLwC1WnU33pHOTzMZv+P8wrZ3cxRCCD8JjIQQDaq2Gy1wwyMlMAqm\nIcOMmVGVhc2XwzkcHjK3ngCg99AeJMbLGfNQc2QbjRdsAzI6/LEUmw2Tbgw1JDRxERW+7E74OQ1a\nEpPrdqYDY4bJpSNSANi0Lx+AU2fs1Di9JMaEER9tZJz8HemaGu7qp1qMjHZxcfcrrxdCtD8JjIQQ\nDfK3wI2Oaf7LiWg/E8b1xgtYvDonfSVGjVm5ej8WDdwmhRtmDQvOAkWTao52fEc6P9Vmw6QZgZGK\njhaCrFF1lXECpXYZHRAonztzTiZn8kgjMNp5uIgap+fsYFdftghaXkoHYLYZWaqy0pq2LF8IIeqQ\nwEgI0SC30/jCFdfE5HnR/sKsZhRflm7bzrxGb5dfUElxttG9a/zUdCzdsEtnZ+OtrsZ1+hSK2UxY\nv34d/nhqWO3ACJzu4Lat9moabt8JlLhzBrFGx9qwhpmosbsD2WeA5LhwBqXG4nJr7DxcxFF/YNTb\nCIw0TafKV8Yb3YL3Hn9Gu6LScf5PSAjR7UlgJIRokO425qIkyIbmoOuTHg/AqZzSRm/z0ar9mAAt\nwsLUSzr+S7honuNYtjHYtW8/VEvHz/5Sa5XShSIwKq9yYfENdz03iFEUhYQk/zyjulmjS0eeLafL\n8g12HZhqBEb2SieaphMRZW3RSI7IKCNTVWOXIa9CiPMngZEQokGK1yjLSUqUwCjYJk7oA4Bud+Nw\neOpd/92+fLylNWjozLxeSug6C4dvsKstCGV0ULeUzgQ4XPX/X+lIpVW1hrtGh9W7vodvn9G55XQX\nDU3GbFI5eLyU/JJqLGaVNF/pnX9/UUwLM9Wxvtu5qiUwEkKcPwmMhBD1OBwezICOTqIERkGX2jsW\nt0nBBOzYXbecTtM0vlpzBAWFiJRoMvonhGaRop6aLP/+oo5vvABGYKSiAToKCjUNBNEdqbTC2WCr\nbj//PqNzM0YRNgtjB/UI9ItIT4nGbDK+jgQ60sU135EOIMH3/uR1BTdbJoTomiQwEkLUU3jG6Ijm\nVRRMIRqa2N3F9DS+VB46UFTn8jVfZGF2Ge25Z88eGYKViYbomoYj22jVbRsQpIxRmD+rYoQYjpog\nB0aVTWeMzgZG9Tss+svp4Oz8IjjbeKEl+4sAevYwslKKV2vR7YUQoinyjUcIUU9RsfFFRjfJW0So\nDB1utO2uOnP2S6W92sXhHUYGKW1EsjTG6ERcp0+j1dRgTkjAkhCcLJ5/yKuRNYJqR3DLyUoqHIHA\nKLKBwCjBF7SUFVfj9dQNXEb2TyA6wmicULsjXUW5r5SuBR3pAJKTItHRMek6bo9kjYQQ50e+9Qgh\n6vG3vjVZpdNZqIwf3SvQtjsn12jCsHLVfiw6uM0Ks64ZGtoFijoC+4uClC2C+oFRTZAzRiVlNagN\nDHf1s1hNxCaEo2k6pcV1s0Zmk8qiGUO4bExvRmckBi73l9K1NGNktZrxKgoKCoVFzc/+EkKIpkhg\nJISop7zC+HJisdX/siOCw2o1o0YZ5+N37DzFyVPllOUarY0v/t4AzGZ5++5Mgr2/CGoHRkamxBnk\nPUaVjQx3ra1HYJ5R/aBl4tBk7rxmaGB/EUBlK/cYAegmozNegQRGQojzJJ+sQoh67JXGHBGbr9RF\nhEaqr213fm4Zn3yUiQnQo6xMnpgW2oWJes52pBsUtMf0B0Zm3cgYOZ3BDYwaG+5aW2MNGBricXup\ntrtQVaXB0rzGmKzGCZySkuoW30cIIRoigZEQoh6Hr/Wtf0aICI2JE1ONf1S70MudaMB1NwwP6ZpE\nfd6qKlz5p1EsFmx9+wbtcVWbkVXxzzJyBrFdt6bruHzDXWObyO4k+lp2tyQw8megomLCUFWlxWux\nhhuBUbmv1bcQQrSVBEZCiHpcvr0KLZ0lIjpG75Ro3GYF1TdEM6pPNP36xoV4VeJcjmPZAIT1S0cx\nB6/8VPFljPyBkSuIGaOqajdmX7/t2CYaJSTWGvKq63qjt4PWt+r285fyVfky3UII0VYSGAkh6vG6\njT0L8fGt+4Ii2l+sr223R4E50p67U6rJOgIEd38RgBpmZHTN/sDIHbyubKWVTvyFtg216vaLignD\nGmbGUeMJlN41prUd6fyiY4zbO+wy5FUIcX4kMBJC1OdrrZuUIMNdQ2369IF4w82Mu7w/0VLa2Ck5\nsnzzi4K4vwhq7THSjIDA4wreLJ+SSkeTw139FEWhh6+c7kwz5XSVrexI5xfnyzC5g9x8QgjR9Uhg\nJISow6tpmHwlL8m+LzQidFJ7x3L/g1OZOrlfqJciGqBrGjXZRild0DNGvj1GZq+RiXEHOWPU1Ayj\n2lragKGivG2ldImJxu21ID5/IUTXJIGREKKOklJjNokXCLdJVzohmuLKy0N3OjD36IE5Nrj7v/wZ\nI5MvMDp3iGpHqj3cNSq66QzP2cCo6XbaFb7mCa3NGCX7jq96m97DJIQQzZHASAhRR9EZo+WtZmp5\nVyghuqvA/qIgDnb1C5TSeYxMi8cTvIxJSakDFQWTRcXSzCDoHj2bzxjpuh7oStfaPUY94sPR0DEB\n9uqm9zEJIURTJDASQtThnwWiyABRIZoV2F80MHSBkcltdGPTgpgx8gcxYRGND3f1i0+MQFGgrKQa\nTyPlbk6HB5fTi8Vqwhbeuky1qqp4FeNETn4L2oILIURj5JuPEKKOMl9nKHNY8NoOC3Ghqsk2BruG\nImOkmEyoVismX/MFzRO8UjJ7lRGMNdV4wc9sMRGXEIGuQ2lxw0NY/YFWdKwNRWl9tlqxGF9nis40\nXa4nhBBNkcBICFFHVYXxhSeslWdthehuvJWVuAsKUKxWwlJTQ7IGU7gtMMdI04KTMdJ1HadvCHRc\nC/futLYAACAASURBVBsl+PcZnSloOKPj31/U1tlpJqtxIqekRIa8CiHaTgIjIUQd1XajRj8yqvkS\nGSG6s5osI1tkS+8f1MGutak2GybN16ZaC07GqNrpweR7rKaGu9aW6Otw2dg+o7YOd/ULizBO5Pgz\nT0II0RYSGAkh6nDWGGeCmxraKIQAR7Z/flHwy+j8TOHhgYyRHqTAqLTCWWuGUUsDo6YbMPhbdUe3\nsvGCn/9Ejr2ZIbJCCNEUCYyEEHV4nMbm6Lg2fkERorvwZ4zCQx0Y+fYYKUHqvVBSa4ZRS0+g9PCX\n0hXa0fX6AVzleZbSRfsCNGeNBEZCiLaTwEgIUYfuNr5dJSREhHglQnReuteL45gx2NU2ILiDXWsz\nhYej+krpFHS0IGSNSisd+HcgtqT5AkBElBVbuAWX0xPYx1ibv5SurRmjeN/7lccR3CGvDQV5QogL\nlwRGQog6VN8Xq+SkyBCvRIjOy3nyBLrLhSW5J+aYmJCtw2i+YAQDKuBspB12e6o93DWyhRkjRVEa\n3Wek6zqVFb49RrFt22PUI9G4nx7EWU5uj8bPX93CL1/5NmiPKYToWBIYCSEC7NUuTICGTnwbS1qE\n6A4c/sYLGaHLFkHdUjoT4ApCYFRaWnN2uKul6eGutQX2GRXVbaltr3SieXXCIyzNDottTIrv2CYt\neN35cvIrOF1cza7DRUHJ1AkhOp4ERkKIgALfDBCvoqCq8vYgRGNqfINdQzG/qLbazReClTEqb8Vw\n19oaa8Bwvo0XAGKibXgxfgdlFcHpTHc0rxwATdOprJa9TUJ0BfLNRwgRUOIfvmiWtwYhmuLI7jwZ\nI1X3gq6jouBwdnxgVF1pBAEt3V/k18NXSnfm3MCo7PzK6Pw0kzEYNr8gOENes/IqAv8uk254QnQJ\n8u1HCBHgH45oamM5ixDdgae8HHdREUqYjbA+oRns6mcKD0cBFIzysWpfu/2O5PANd23pDCO/+MRI\nVFWhvKQGt+tsABfoSHeenTAVi/GVprik4wMjXdfJ8mWMAMrt9RtKCCEuPBIYCSECKiuNM7dWW2iG\nVQpxIQhki/r3RzGF9iSCyWYEE6o/MHJ0bGDkcHlQvMZjxce3LsNjMqvEJRrd40rOnA1e2qOUDsAS\nZrxvlfpO8HSk4nIH5fazWSLJGAnRNUhgJIQI8A9HDI9s3d4BIbqTwP6iEM4v8jOF+wIjX2c6Rwdn\njEorWz/ctbaGOtNVtlMpnc33vlVZ2fHZm6O1skUA5VWSMRKiK5DASAgR4PCdAY2Mat3eASG6k87S\nkQ6MUjog0LLb4fB06OOVtmG4a22JgUGvZwOjivL2KaWLjDJWVh2E7I1/f1GsLxgrs0vGSIiuQAIj\nIUSAy2l8qYqJlcBIiIboHg+OnGNA6DvSQe3AyPjbdTiDGBi1svkCQI9AZzpfB0zP/2fvzuMkq+u7\n0X/OUlWntu7qvWeGAYZhABcWZRjgaghxiSxijDE+idv1EfUab5RAzEsjj6LB+0KDhscbxeDL+8KI\nPNGYEJc8ombcUJRFWRQQhJmB2Xt6q6717Of+cc6pqu7p7qquOqeqTs3n/Q9MT0/Vb7qnq873fDcb\n5aIOQWh9J9Jahr2Mk96FPqtnDrsZoxefMQEAWGIpHdFAYGBERDW21xA95m2RJ6LltAP74RgGYtPT\nkDKZXh/nuMBIDzkwmi+oiHn/305m2c8YLcyWli12zQwpkKTOLklGvf4lSw93Mp+mWzgwU4IgAOft\nGAfAUjqiQcHAiIhqBMtdUjjOwIhoVf2yv8hXC4y8Ja96yOO6FxqWu8obWO7qS6XjSKZj0DULxSW1\nNqo7G8BC6clxt39JMMNd8Prs0QJsx8HWiQymvAEUeQZGRAOBgRERAQBM04bkuIHRxES6x6ch6k/1\n/UX9FRjJlhcYhbzgtbDo9gNtdLlro/GGRa+FgEZ1A8CUN9hBchyYIQZH/uCF7ScNY9jLmi2VdTje\n6ycRRRcDIyICAMwtViBAgAkgEee4bqLVVJ9xA6Pk6f0SGLkBhWy5PS5GyGVkZW/iWyf9QKMT/gCG\nMoreqO6hXGcT6QD3dcsEIEDAsbnwdhn5gxdO3zyMRExCSpFhWg7KIQ++IKLwMTAiIgDA7Kx7IeF4\n2+OJaDkzvwhzYR5iMon4ps29Pg6A+h4jyXIDFiPkjJHmDTbIjbSf4RlvGNkdZCkdADiy+/p1bLbU\n5DPbfHzHwR5v8ML2LUMAgJGse3aW0xFFHwMjIgIALHolMmIbfQNEJ4KqP6Z722kQxP54+xQkCUI8\nDsl2sxVWiCVkhmnBMfzlru33IY41lNIVAxrV7RPj7uvX/HwlkMdb6Vi+imLFQDYVw4SX5Rr19jlx\nMh1R9LFehogAAHmv1l9O8GWBBputqjj4DzdDiMUweuVVSD3v+RCE5plS1Ru80C/9RT4xodSm0llG\neIHRYkmvTaTLtjGq25cbS0GUBBTyKuSYG2AOBZQxiisxWBUTS16JXtD2eP1Fp28Zrv2bGfG+FswY\nEUUfr4CICABQ8t7UEym+LNBgqzz5W6h73SDn0FNPQtl+OsZe81qknv+CdQMkP2OU7LfASFEg6eFn\njBYLasMOo/YDGUkSMTqWxtyxEkzDhiyLSKbbH+bQKJmOobRQRakYTpDi9xdt3zJc+1gtY8Qlr0SR\n1x+1AETUc9WS2zuQDugChahf+UGRsu00iJkM1D3P4NAtn8KBmz6O8mO/XnW6mG0Y0J571v1zp53W\nzeM2JSoKJMf9+bWtEAOjhuWunS5jHZusT77M5pSWMnat8AO2akhBip8x2r55qPYxPzDKhxSMEVH3\n8NYwEQEANNWAgM7uBBNFgZ/5Gb3yKqTOOgv5H/4AC9//LtS9e3Dof/4DlNNOw9hVr0XqhWfXLti1\n/c/BMU3EN2+GlOqvcfaiokBa9AOj8EZGLzRmjDoOjDIAZgAEV0YHALlcEkcBGCFMiKtqJg7MliCJ\nAk7dVA+MRvzAiBkjoshjYEREAABLsyADGAlgbC5Rv3IsC+q+vQAA5bTtEJUkRq94NXIvewXyP/oh\nFr93N9S9e3HoM/+AxKnbMPaaP0L67HPr/UV9sti1kZsxcnsEHTu8jNH8QhUCBEgxCZLcWcGJP4AB\nALLDwb3m+Mup7RDGlj97pADHAbZOZZBoGFIz6vUYLbHHiCjyGBgREQDA8XoTxsYYGNHg0g4egKPr\niE1OQR6q3/UXFQWjl1+B3MtejvyPf4jF794N7dl9OPz//k8kTt0GeOV1/bK/qJGoKLWpdLDDyxgV\n8lWIABKpWNPPbaaxlC6oiXRAfcmrEELm7JnDx/cXAfVx3ZxKRxR9DIyICAAgehdUk+OZJp9JFF2q\nP3J7+/ZVf19MJDD6qsuRu/RlWPrJj7Dw3e9Ae3Zf7ff7MWMkKPWpdAgvYYRSUccQOu8vAoBkKo50\nJo5ySQ80MBobS8GGAxkCKhUdqVRwPZO1/qItQ8s+XusxKmtwHCewfiki6j4OXyAiFIoqJLjXVENZ\nDl+gwVX1SuKSTQIcMZHAyB9ehm033YyJN/w55JERKKdtR3x6uhvH3BAxoUD0MkYCHNirDI8IglZx\nMyLDAQUyO14whXQmjqkVGZhOSKIIywtMjs6VA3tcx3GWjepulFJkxGURumFDDaGEj4i6hxkjIsLs\nnLsM0RIAsU8WVxKFoVnGaCU3QHoVRv7wVWEeqyONpXQiAN2woMSDfXs3LRuWbgEQMDLa/nLXRhf/\nwXZc/AetfR82QpBFwLAxN1vGaSePBPKYRxcqKKsmhjNxjK0YUCMIAnKZBI7lq8iXNCS5C44osvjT\nS0SYnXfvrAoxBkU0uMylJRhzsxASChJbTur1cQIjNpTSSQA0w4YScOK3UNZrE+mCnCIXBikhA4aO\nhcVqYI/p7y86fXN9satlW7j98f+FrWObMJwZwbF8FUslHZvG+mtqIRG1jldBRIR83t0SLwV8l5mo\nn6h7vWzRtm0QJKnJZ0eHmzFyx3WLADQj+HKuhYYdRp2O6g5bIukOhyjkAwyMDvv9RfUyut/M/xYP\nz/4G33v6xxjOuF+TPCfTEUUaAyMiQnHJDYziSQZGNLhq/UXb+2+AQifcjJEbDIkANC34HT6Ny10z\nQ/0dGKUy7knLAU6Je2aV/qKfHboPAKBZOobSbqCd52Q6okhr+SroRz/6EW6++Wbouo53vOMd+LM/\n+7O2n/QLX/gCfvjDH8IwDLzxjW/EBRdcgA9+8IMQRRE7duzADTfc0PZjE9HGlb3FhMkAxvAS9auN\n9hdFhagkIcCB4FiAIKEawnLThSUV/qtDEFPpwpQdTiAPQK0YgTxeRTVxeLYMSRRwyrQ7tXOuuoAn\nF56ufU4q7Q68WCozY0QUZWtmjBYWFpb9+mtf+xq++c1v4u6778add97Z9hM+8MADePjhh/HVr34V\nd9xxB44cOYKbbroJ1113Hb7yla/Atm3s3r277ccnoo3TvAuITLa/eweI2uWYJlRv7HaziXRRIyru\nz63oZY0q1WACgkbzC2UIECDGJEhSfxeb+EuqzYAyZ/uOFOAAOGU6i5jsZobuPXw/HNSn/ylJLzBi\nxogo0tbMGN144404/fTT8fa3vx3JZBLT09O48cYbEYvFMDY21vYT/uxnP8MZZ5yB97znPSiXy/ib\nv/kbfP3rX8fOnTsBAJdccgl+/vOf4xWveEXbz0FEG2NoJiT0f1M1Ubu0A/vhGAZi09OQMoO1q8sP\njCTHggWgqgYfGC15fYhBLHcN28SEO/zAMYJZ6lTbX7TZLaOzbAu/OPIgACAuxaFbOuKKG5Syx4go\n2tYMjG655Rbcf//9uPbaa3HppZfi+uuvx3333QfDMPCBD3yg7SdcXFzE4cOHcdttt+HAgQP4i7/4\nC9h2/cUrnU6jWCy2/fhEtHG2bkECMBbQGF6iftPq/qIoqmeM3AyJGkIpXamoIYP+L6MDgKkJN/AV\nbQe2bXe8gqDWX3SSGxg9Ovc4inoJ0+kpjCSG8duF3yGWcAOjpTIzRkRRtm6P0YUXXogLL7wQ//mf\n/4n3vOc9eMMb3oBXvvKVHT1hLpfD9u3bIcsytm3bhkQigZmZmdrvl8tlDA0NrfMIrpGRFGR5cKYK\ntWpiItvrI5zwBvF7INhuGciO08cj8/eLyjkHWZS+BwsHnwUATJz3gkiduxXjm8fwLADZm0wnSWLg\nf0fdK8+bnMr0/ddvfDwDC+7ocikWw3gHN3xs28G+I+6o7l1nb8Z4LokHHv8lAODyM34fT83tARaA\n4RH3cqpQ1vv+6zOo+HWnIKwZGO3evRu33nor4vE4/vqv/xq33nor7rzzTrz73e/GO9/5Tpx//vlt\nPeH555+PO+64A29729swMzODarWKiy66CA888AB27dqFe+65BxdddFHTx1lcrLT1/FE2MZHF7Cyz\nab00iN8Dw7QgOW61vCwhEn+/Qfw+RE3Uvgf5J54EAJiTJ0Xq3M1MTGSxWHazFf7I7sWFaqB/R9tx\nYFRNAALS6Xgkvn62KECyHfz2yRmctWO87cc5NFdGWTUxkk3AMUw8/tw+/GbmScTEGJ6feQGePrYf\nAJAvFSBLAsqqiYOH80jETrwbt73UjdcjBl4nhjUDo8985jP48pe/jEqlgmuuuQb/9m//hre97W14\n3etehy984QttB0aXXnopfvnLX+L1r389HMfBRz/6UWzZsgX/43/8DxiGge3bt+Oyyy5r+y9ERBtz\nbNZtqjaBWmMx0SAx84swF+YhJpOIb97S6+METky45W2y5QZGmh5sKV2xrNcm0g3notGHKMREQLMw\nN1cGOgiMav1F3pjunx9+AABw/uS5SMWSSMnuoIeqqWI4rWC+oGGppGFyhGXJRFG0ZmCUTqdx1113\nQdO0ZcMWhoaG8P73v7+jJ13tz99xxx0dPSYRtWdu3s2+OrLQ45MQhaPqj+nedhqEDvtN+pEgihAS\nCUhej5GhB7vgdbEUneWuvpgiw9EsLHa45LVxf5Fhm7WhCy/dciEA1AKjilnBcGYY8wUN+ZLOwIgo\notZ8h7j11lsRi8UwMjKCT3/60908ExF10cKCGxiJLP2gAaU+4+8vGrzBCz5RUSDZIQVGhcblrtHI\nGPnT8/zl1e2qZ4yG8OjsYygZZWzJbMKpQycDAFIxPzCqYjjtfpU4gIEoutbMGI2OjuKtb31rN89C\nRD1QKLjjZWNKy/ueiSKlutebSDdgi10biYoC0SulM4xgA6P5huWuqUx83c/tF5lMAipKqHQQpJRV\nA0fmK5AlEadMZfHtR+8DALx084UQBDfDnpLdzFDVqCKXcbNpHNlNFF2DV1NARBtSLLp3VJUI7Cch\n2ijbMKA99ywAQDltgAOjhFIrpTODDowWKu5y13j/L3f1DXlLXvVq+/1Wew+70+hOnc5iXp3D0/m9\niIsxXDD9otrn1Evpqsh5QSOXvBJFVzRe4YgoNGrZvcucSkfjTjDRRmj7n4Njmohv3gwple71cULT\nWEpnmcEsNvUteX06iWR0bp6MjroBi6m1Hxg9c7DeX/Szw/cDAHZOnYekFwwBK0rpvIzREjNGRJHV\nNDB6+9vf3o1zEFGP6N4yyKHhZJPPJIoe1R+8MICLXRuJSj1jFHRgVPLKbdPZ6Nw8mRzzguAOvhZ7\nDruB0SmbU7j/yK8AAC/dsnydiB8kVYx6xoildETR1TQwUlUVR44c6cZZiKgHTG+0by4iY3iJNsKf\nSDfI/UXA8oyRbQUbGKkVtzQsSjdPpiYzAADZcWC2ERzZtlMrpasqB1E2K9ia2YyTsyct+7z6uO4q\nhtNejxGHLxBFVtNu68XFRbzsZS/D2NgYEokEHMeBIAj4wQ9+0I3zEVHYDPeiYXyM42VpsDiOUx/V\nvX1Hj08TrsaMkW05gT2u4zgwVbdnaXQsOoGRosgwAcgQMDdfxvTUxpZzHp4rQ9UtjA0peGjezRa9\nZMtFtaELteeRExAgQLU0ZNPuZE/2GBFFV9PA6Itf/GI3zkFEPWDbNiTvGmpyfHD7L+jEZC4swMrn\nIaZSiE9P9/o4oRIT9YyRE2DGqKyakBwHgICRiO3mcSQBsBzMzG48MPL3F5201cHvlvYhIcVxwdR5\nx32eKIhIxZMo6xXIMRuCAJSqBkzLhhyRQRVEVNf0p3bLli146KGH8K//+q8YHR3Fgw8+iC1bBm9z\nONGJqFDQIAKwAGQz0VjcSNSqen/R9oFc7NpIaMgYwQ4uY7RYjN5yV58YdzM4894S643w9xdZI88C\nAHZOvQiKvHq5cSbmBoyqXcVQmpPpiKKs6TvFpz71KfzkJz/B97//fViWhX//93/HJz7xiW6cjYhC\nNjNbBgDYotDkM4mip7rX7y8a7MELgLfHyHYnTCK4uAiLRbUWGKUjFhjFvd1sS20seX3mcAEQLBw0\nnwIAvHTLhWt+bjruBkbuyG6/z4gDGIiiqGlg9LOf/Qw333wzEokEMpkMbr/9dtxzzz3dOBsRhWx+\n0R3DK8iDfTedTkzqHnexq3JCBEbJesbIcWA7wURHjctdozSVDgAUL3uT98aNt6pY0TGzUEFiYgaa\nreKU7Nbjhi40qgVGRhU5ZoyIIq3p1ZDolR/4DYe6rtc+RkTRll90S0ykhNTjkxAFy9Z1qPufAwQB\nyrbTen2c0DVOpZMAGEYwfUZzc/XlrlF77/f7JvcfLuBfdj8Ns8Xeqz3eNDpl82EA62eLgOUZI+4y\nIoq2pq9yl112Gf7qr/4KS0tL+NKXvoQ3v/nNePWrX92NsxFRyIpF9807SosbiVqhPfcsYFmIb94C\nKRmdaWrtagyMRACaYQXyuP5y13gEXyNOP2UEABAD8F+/PICbvvIrzLaQPdpzaAlCsgg9PgdFUnD+\nKkMXGvk9Ro27jBaZMSKKpKZT6d71rnfhpz/9KTZv3owjR47gve99L/7gD/6gG2cjopBVvDfvZDp6\nFz1E66nvLxr8MjoAEBOJWildkIFRqaghgeiV0QGA4gVzp01lsVjVse9IER+9/UG8/YqzcP6Zk2v+\nuT2HliBPHgAA7Jp+ERLS+n93ZoyIBkfTjNF73vMelMtlXHvttfjbv/1bBkVEA0Srus3a2SyXu9Jg\nOZH6iwCvxyiEjJFajt5yV58fGAm2g4++/QK8aMc4qpqJz/3HY7jz+7+DYR7/NbJsG/uO5iGN+WV0\nFzV9Hj8wqpoNPUZc8koUSU0Doze84Q3YvXs3XvnKV+L666/H/fff341zEVEXGJp7IZXLMTCiweEu\ndn0awAmUMVIUiLAhODZECKiqRiCPa3qvEaOjUQyM3KIYtWogrcTwl687G3/+8h2QRAE/eOgg/p87\nfoWZxeWjvA8eK8McOghBNrFt6GRsyWxq+jzphlI6P2OUZ8aIKJKaltJdeumluPTSS6GqKn784x/j\nk5/8JBYXF/GjH/2oG+cjohA5XoP2yEj0LnqI1mLOzcEqFCBmMohNTfX6OF0hKu7NDdExYQlxVKud\nB0ZVzYRouctdR0ejtdwVqGeM1IoBx3EgCAJeecFWnH7SMD7/jcewf6aEj93+IN52+VnY9Tz338me\nw/Uyupe0kC0CVo7r5lQ6oihracTMM888g9tuuw2f+cxnkMvlcM0114R9LiLqAsFyR/r605uIBkFt\nf9Fp22sTVQedHxj55XTVaueldMuWuw5Fa4cRAMgxCbIswrYdGHr967Ft0xA++t93YeeZE1B1C//0\nzcfx5e8+Cd2w8NiRfRAzS4ghjvMnz2npeTINgdFQOg4BQKGiww5w0S4RdUfTjNFVV10FSZLwmte8\nBv/8z/+Mycm1GxaJKDpU1YQMwIGDsbHo3Q0mWovqDV44UfqLAEBIJABBgOQtea1qnWcslgVGEVvu\n6kskYzCLGtSqgXiifsmTUmT8xWtfiB89fAhf/cHT+PEjh/HMoQIWh38DJIGzR89FvMnQBV+tx8io\nQJZEZFIxFCsGChW9tvCViKKhaWD0qU99CmeeeSZKpRJsO5i9CETUe8fmygAASxAgRWw/CdF6qs+c\nWBPpAHfXYONkOk3tPGM0l68gBjfjloroBX4yGUO5qEFTzeN+TxAEvOzFJ2H75mF8/puP4eB8HsrJ\nByAAeNX2l7b8HI2ldAAwnE6gWDGQL2kMjIgipunVUDKZxOtf/3q8/OUvx8tf/nK89rWvxb59+7px\nNiIK0dyC23TsSAyKaHDYmgbt4AF3seup23p9nK4SGnYZrRYIbNTCvPsa4S53jWZJYqJhAMNaTpnO\n4oa3XYAXvECEIFlImKM4Kdt86IKvtsfIC4z8PqM8+4yIIqfpFdENN9yAd7zjHbj//vvx4IMP4l3v\nehc+8pGPdONsRBSixYX6RQ/RoFCf3QfYNhInba313ZwoxIRSzxjpnQdG+bwKIJrLXX21AQxNhlEk\nEzIuOX8MAHDG1OYNPUcq5g6vqZoqbMfGcG0AAyfTEUVN08BocXERl112We3XV1xxBfL5fKiHIqLw\nLRW8i54EAyMaHLX+otNPnDI6n9iQMdL1zkvpit5rRCoTveWuvlYDIwAom+7NopyS3dBziKKIpOwG\n4VVTrZXPcTIdUfQ0DYzi8Tgef/zx2q8fe+wxJJMc7UsUdeWiezdTSUf3oodopeoefyLdCRoYeRkj\nI4DASC27wcTQcHQzb/XAqHkGray7fZeZ2MandCZl97qoYlRrgVGeS16JIqfp8IUPfehDeO9734tc\nLgfHcbC0tIRbbrmlG2cjohCpFfeiJx3hu8FEjRzHgbpnD4ATayKdT1QUiF7GyDQ6D4wM1V/uGt2p\nlX6PkdZCxqhkuBmjdgKjlJzEAhZRNasYTrOUjiiqmgZG5513Hr73ve/h2WefhW3b2LJlCzKZTDfO\nRkQh0lUTIoChoejeDSZqZBw7BqtUhJTNIjYx0evjdF1jxsg0OpsiqxsWBMsGIER6nP9GSulKRglA\nfZjCRqT8jJFZRS7jluJx+AJR9DQtpfvOd76D173uddixYweSySSuvPJK7N69uxtnI6IQWV6pzUiE\n7wYTNVL31vcXnSiLXRs19hiZZmeBUb5U32GUHYhSutYzRul4GxmjWD0wqg1fKDNjRBQ1TQOjz3/+\n87j99tsBACeffDLuuusu/OM//mPoByOikHkXTuOj7BmkwXAi7i9q1BgYWWZnpXSFihH55a7ABnuM\njPZ7jGoZI6NSG9e9VNJhO86GH4uIeqdpYGQYBsbHx2u/Hhsbg8MfdKJIs20bkvdzPDm+8YsAon7U\nmDE6EYlKEpLjZkZss7P36aWCihgEOACSER7QorSwx8hX6mT4QkPGKCZLSCVkWLaDUgvPS0T9o2mP\n0fnnn4/rrrsOV111FQDg7rvvxnnnnRf6wYgoPIt5FSIEWABSqehe9BD5bLUK7eBBQJKgnHJqr4/T\nE2KinjGyrc5K6eYX3WWlQkyM7HJXoPVSOsdxahmjdFsZI2/Jq+F+3YYzcVQ0E0slHUN8jSWKjKaB\n0Q033IA77rgDX/va1yDLMnbu3Ik3vvGN3TgbEYVkZta9ALCl6F7wEDVS9+0DHAeJrSdDTES39KsT\ngpKoDV9wOgyMCkvuDiM54gug4wkZguCOL7csG5K0eqGMamkwHQtxKY64tPGFto3DFwAgl0ngyHwF\nSyUNWyc5sIooKpoGRvF4HFdffTWuvvrqbpyHiLpgYdFtMhbkptW0RJFQ31+0vccn6Z3GHiPH7qyU\nrlR0J6rFlY0HCf1EEAQkkjGoFQNa1UAqs3rQ3El/EbB8+AKAWp8RJ9MRRQuviohOQPm8++YtJ5re\nGyGKBHXPid1fBHh7jLyMETpsBa5W3At6JRXtwAhobQBDqdPAyMsYVWuldG4Axsl0RNHCwIjoBFQq\nuG/W/vJDoihzbBvVve5i1+T2EzljlKxljATH6WhQkr8QNR3hwQu+VgYwdDJ4AQCStVI6NxufSzNj\nRBRFa14VHT58eN0/uHnz5sAPQ0TdUSm7b9apTPQveoiMmaOwy2VIwznIY+PN/8CAalzwKgLQUQpE\nugAAIABJREFUTRuJWHs9QrpmIgEgG+FR3T5FaT6AoezvMOq4lM7tzfIzRvkSM0ZEUbJmYPTmN78Z\ngiBA0zTMz89j69atEEUR+/fvx9atW/G9732vm+ckogD5d4OjvJ+EyKce2A8AULZtOyEXu/oap9KJ\nADTDajswsg13eMNwLrrLXX21Ujp17cCoaJQAAJl4ewuvV5bSNe4yIqLoWDMw+uEPfwgAuPbaa/Gm\nN70JO3fuBAD8+te/xhe/+MXunI6IQmFqFmQAuRyXu1L0WYUCACA2Otrjk/SWO3zBvfiXAOi6BbRx\nnW9aNmDZAASMDMBrRMILjLR1eoz8jFGnPUYVswrbsZkxIoqopj1Ge/bsqQVFAHDOOedg3759oR6K\niMLleHeDR0fauztK1E/8wEjKDvX4JL21spROM6y2HqdUNeCPXEixx6glkighIcXhwIFmaRj2vm5L\nZb2jXi8i6q6mndfT09P4zGc+gyuuuAK2beNb3/oWTj311C4cjYjCInqjfKcm2rsIIOonZtELjIZO\n7MBIiMchwgYcB6IgQNXaC4wKZb12cZAciMColR6jzgIjwF3yqlk6KkYVY8kkEnEJmm6hqplIRXzs\nOdGJomnG6Oabb0ahUMB1112H97///TBNEzfddFM3zkZEIahUdEgAbDgYGYD+ASKrWATAjJEgCJAa\nskaVdXpq1rNU1moZo+RAjeteJ2PkBUbtDl8AVtllxMl0RJHTNGM0PDyMD3/4w904CxF1wbE59wLA\nEgSIIif2U/T5pXTyCR4YAd4uI9uEJcZQbTMwyuc1CBAAUYAkRf81YiOBUSbeScbIC4wadhnNLFax\nVNKweZzZeaIoaBoYnXXWWcdN+ZmYmMA999wT2qGIKDxz826TMeToX/AQAYBVK6XL9vgkvScm3IyR\nAUBT1x42sJ5FbwG0GBuM14hErccovAWvwPIBDEB9Ml2+zIwRUVQ0DYyefPLJ2v8bhoHdu3fjkUce\nCfVQRBSexUX3TVuKtzfGl6jfmAWW0vkERYFkuQFAdZ1AYD2lojtJTU4MxgLoZhkj27FRMaoQINSC\nm3YkY8uXvA6nOZmOKGo2dDsoFovh8ssvx3333RfWeYgoZIWCu4AwrgzGRQ+d2GxNg6OpEGQZYjL6\no6U71TiZTtXaC4zKZfdC3h9zHXVKbVy3seqEuIpRhQMHKTkJSWz/htHKUjruMiKKnqZXRt/4xjdq\n/+84Dp5++mnEYoPxYkl0Iip7b9LKADRVE1klP1uUPaGXu/pERYFUcDMjht5eYFQtu+O6UwPyGiFJ\nImJxCYZuQddMJFZMiCt5y13TbS539dWWvJruzaccdxkRRU7TwOj+++9f9uuRkRHccsstoR2IiMKl\nevXumSwn0lH0cYfRco0ZI01vb1y3rpqIAUhnEwGerLeUZAyGbkGtrhYYdbbc1ZdcMZVumBkjoshp\nGhjddNNNMAwD+/btg2VZ2LFjB2SZJThEzZTKOu6591m89OKTMdRHQYiumZAADA0NzkUPnbhqO4yy\nHLwAAKKShGS7gZHR5h4j0wuohgfoNUJJyiguuX1GwyPLSy6DGNUNNJbSeT1GfsaIwxeIIqNphPPY\nY4/hfe97H3K5HGzbxtzcHD73uc/h3HPP7cb5iCLrO999CvNPz2PP4zP4v/7vixGP98cNBVu3IQEY\nGe2sbISoH1j+4IUTfLmrT0wkINluKZdhbDwwchwHtmEBEDCcG5yerfUGMJR1NzDKBhUYmSt7jFhK\nRxQVTYcvfPzjH8ctt9yCu+66C9/4xjfw2c9+FjfeeGM3zkYUaQVv+pusWbjjKw/3+DR1gmUDAMYZ\nGNEA8Je7coeRS1QUiF4pndlGYFTVrNod06EBKqVLNAxgWCmwjFHMfU31A6NUQoYsiVB1C2qb/V5E\n1F1NA6NKpbIsO3TeeedB03j3g6gZvWGHiH6sjG/f/eQ6n90dpmlD8qYyTXLhIA2A2g4jBkYAlvcY\nmaa94T9fqOi1wCiZjgd4st5SFD9jdHyAEsRyV6Bh+II3lU4QBE6mI4qYpoHR8PAwdu/eXfv17t27\nkcvlQj0U0SAwvVG5Ys7tL9r/6FE88psjvTwS5hYrECDABKBwXDcNAJPLXZcRFaXWY2S1ExiVdfij\nCZIDMpUOcHuMgNVL6YLLGC0vpQPqAxg4mY4oGpoGRn/3d3+H2267DRdeeCF27dqFf/qnf8LHPvax\nbpyNKNq8i5JXXnEm5PEURAA//c5TODpT7NmR5ubcCwBH4lhjGgycSrdc4/AFu43AKF/UIEOAg3pf\nziCo9RipawdGmVhn5cXJhh4jf1+SP7J7iQMYiCKh6S3jbdu24etf/zoqlQps20Ymkwnkiefn5/En\nf/InuP322yFJEj74wQ9CFEXs2LEDN9xwQyDPQdQrtu2XrAmYHk/jrW99Mb7w2V9A1i18/c5H8K6/\nvBiJHgxjWFhw72SKsfaXGBL1k1qPEYcvAFheSmdbbQRGea8MTBYHai+U32OkVlYbvuCP6+7s+iYm\nyoiJMRi2Ac3SoMgKcml/lxEDI6IoWPPK7C1vecu6L4pf/vKX235S0zRxww03QFHcEqObbroJ1113\nHXbu3IkbbrgBu3fvxite8Yq2H5+o1xbzKkQIsACkUm4pxZ++6Tx8/Uu/gqxb+PKXH8I737Gr6+da\nWnKnVckJBkY0GNhjtFxjKZ1jORv+84WCW/IlxwfrNWK9qXT1jFHnfZcpOYkl3UDFrEKRlYZdRiyl\nI4qCNQOj9773vaE96Sc/+Un8+Z//OW677TY4joMnnngCO3fuBABccskl+PnPf87AiCJtZtZ9o7Ub\nStamp7L4vSvOxL3/+ymYcxX8x7efwB9f9fyunqtYdAOjxAD1DtCJy3EcmAXuMWrUmDFy7I0HRsWi\newEfG7AeRL/HSFt1+EIJAJCJdz6pMxVLYkkvoGq6r7X1HiNmjIiiYM0eo127duHMM8/E6aefjl27\ndmHXLvfutv/rdt11110YGxvDS17ykloNrm3X0/3pdBrFYu96MIiCsLDglmYI8vIfsfPO3oSTz50G\nABx+fAa/fPhQV89VLbt3S1MDNG2KTlx2tQJYljuiOs5/0wAgKApE2/05bycwqni9MIPUXwSs3WNk\nWAY0S4coiFCkzhdxr1zyWu8xYsaIKArWDIyeeOIJXHnllXjsscdqH7v33nvxR3/0R3jyyfbHDt91\n112499578Za3vAVPPfUUPvCBD2BxcbH2++VyGUOsFaeI8+v0Y4nj77pedflZiE+mIULAL77/NA4d\nKXTtXP4Oj+xQ5xcARL1WW+7KMroaMVHPGAmOU7sB2Sr/NSI9YDdP1iqlK5t+f1E6kJ6qlZPphtMc\n100UJWvmyj/5yU/i05/+NC688MLax6699lrs3LkTn/jEJ/ClL32prSf8yle+Uvv/t771rfjYxz6G\nv//7v8eDDz6ICy64APfccw8uuuiipo8zMpKCLA9WDXQrJiZYLtJrrXwPNM1drJgeSqz6+ddeewlu\n+rv/glw1cde/PIIPfviVtV6kMNm6BQnASScNR/7fUtTPPwh6/T0ozB4EACijIz0/Sy81/t2toXit\nx0gEMDySRmIDw1ZM3X3tmpjMDNTX1HEciJIA07AxkktB9r4m5cUlAEAuOdTR39f/syOZIWAOkBQH\nExNZxBQvMCrrA/X17Ef8+lIQ1gyMCoXCsqDI93u/93v41Kc+FeghPvCBD+DDH/4wDMPA9u3bcdll\nlzX9M4uLlUDPEAUTE1nMzrLMsJda/R7kvVK6uCKv+fl/+qYX4av/34OI6Tb+4R9+gne+cxdEsekE\n/fpz5Kt49LEZlDdQouF4Fz1KQor0vyX+LPReP3wPivuPAgDsZKrnZ+mVld8Hx3EgwS1Plxzg0OE8\nshu46eLvX0tE/DViNYoSQ6Ws48CBRWSybonbgYVj7u8JStt/38bvgWS6mamZxUXMzhZhOw4kUUCp\nauDwkTxiJ+AN3W7oxusRA68Tw5qBkWmasG37uAs127ZhGMdPdWlH42S7O+64I5DHJOoHWtWAgPVL\n1ibH07j01c/DT7/9W2BRxV3ffAKv/+MXrvn5+XwVDz16BM/uXUBpvgLZtCBgY6UfMgAHDibHgxm7\nT9RL9Yl0vGDxCYKAmDdRTgSgGRZa/eoYpg3BdtcMDA8PXrltIimjUtahVY1aYFRb7hrvfCIdACRX\nlNKJgoChdByLRQ1LJR3juWQgz0NE4VgzMLrgggvw2c9+Fu973/uWffzWW2/FC1+49sUbEQGWZkEG\nMNLkTfCcF0zh4KElPPfQYRx7aha/eOAALt61FQAwv1jBI48ewXN7F1BaqEA27VogFANgA7DiImRl\nY03SU5uHkBvAix468dR2GLHHaJmYtyNNAqAZre8yKlb02kXBIA5oWa3PKMhR3UDj8IVq7WO5jBsY\n5csMjIj63ZqB0XXXXYd3vetd+Pa3v42zzz67NlZ7dHQUn//857t5RqLIcUy3ZG1srPmb4BV/eAZu\nP1KAeqSEX/5wD5547CjKawZCEoYn0zjt9DGcd/YmZAbw4oWoVbVR3RzYs4w/alsAoOnHj6deS6Gi\nw7/NkuxCz2O3rRYYlXU/MOp8VDfQEBiZ9XL/4XQCQJG7jIgiYM3AKJPJ4M4778R9992H3/72txBF\nEW9605tq+4aIaHW2bUP0btJOTbRWsvaWN70Y//S5n0OumtCPleuBUEJCbjKN7aeP49yzp5EewIsV\nonZxuevqJEWBaFuwRQnVVfb2rKVQNmoXBckB3HVWD4zqX5OS4U+lC6a8eOVUOsDNGAHcZUQUBetu\ncBMEARdffDEuvvjibp2HKPKKJR0SAAtANtNaICPLIt74f56Pu/79N0goMk7fMYFzXzjVlUl1RFFV\nK6VjxmgZMZGEqBqwIaGqtt4TvFRQIUEABNT6lAaJv+R1WcbICDZjlPQyRtWGUrph7jIiiozBWm1N\n1AdmZt0t6raIDU2ZG80l8Y6r21+eTHSi4fCF1QlKAlLFhCkBVbX1jNGit39NjEmB7PTpN4lVSumK\nAQ9fqJfSqbWPDfsZoyIzRkT9rvWrNiJqydy8e3EhcCwrUai44HV1olJf8qpuIGNULLoZDTkxmK9d\nirJKj1HQwxdix/cY5dJuxijPjBFR32NgRBSwvLdja1AvLoj6gWNZsMolQBAgZTh+vpGoKLUlr9oG\nMkYlbzhAYoOTLqPC7zHSGqfS6UFPpXNL8qpGFY7jAAByWW/JK3uMiPoeAyOigBUL7sVFPDmYFxdE\n/cAqlQDHgZTOQJB4E6KRmKhnjDYyla5acQOG5IBOu1RSy4cvOI5TyxilAwqM4lIMsijDdCwYtvv1\nHPYyRpxKR9T/GBgRBaxSdu8KplocvEBEG1frLxpif9FKjRkjXWt9j5HuBQzpAX3tWjl8QbU0mI6F\nuBRHXAruRla9z8gtqx5KxyAAKFYMWHbr3w8i6j4GRkQB07y7rllvszoRBc+fSMf+ouOJSrKWMTKM\n1jNGppddGhoazNeulXuMgu4v8q1c8iqJIrLpOBy4I9GJqH8xMCIKmKG5Fxc5bjgnCk1tuSsDo+M0\nZowM3Wrpz9iOA8dwsxm54cF87Up4i2811YRtOygFPKrbt+ouo7S/y4jldET9jIERUcAc0724GB0N\n9s2WiOr8UjqZpXTHERUFotffYhitlW5VVBN+p1YmO5ildKIoIp5wgyNdMxsGLwQ7vKOeMapPpvN3\nGTEwIupvDIyIAiZa7iSiqYlgyzOIqM5ixmhNjeO6TaO1jFGhrMPvskkO8GLpxj6jshe4BDV4wZeU\nj88Y+buMOJmOqL8xMCIKULmiQwJgw8FITun1cYgGllkbvsDAaCUxUS+ls83WMkbFil7b+J5MDe5E\nzcY+o6LhLuPOxLtQSseMEVEkyM0/hYhaNXPMfaO1BAGiyPsORGHh8IW1NWaMLKu1wChf0k6QjFE9\nMCoLbsYorOELVaMxMPIyRmVmjIj6GQMjogDNzbtvtILMoIgoTH4pnZxlj9FKjcMXbNNp6c8sLWkQ\nIACSAGmAX7/qgZGJkhzsDiPfynHdQOMuIwZGRP1scF/9iHpgMa8CAKQE7zkQham2x4gZo+M0Zozs\nFvfm5Je8167YYC/LTXg9RlrVqI3rzgbdY+RNuVteSsepdERRwKs3ogAVltw3wrjCHy2iMNVK6TiV\n7jhCQ8bIsVrLGJW8C/ZBf+3yM0bVqoGSFHLGyFhl+AJL6Yj6GjNGRAEqe2USyfTgNi8T9Zqt67BV\nFZAkiEmOxV9JjMUgCm6mSGgxY1TxXrsSycF+7fIDI61q1PcYxbtbSmc7rQWrRNR9DIyIAqRW3N0h\nmSwn0hGFxc8WyUNDEAShx6fpT7GY+/Yu2DacFi7E1ar72pVOD+7gBWD58IX6gteAA6NVptLFZBFp\nRYbtOCh67xNE1H8YGBEFyFTd8pVcbjA3xxP1A/YXNSd7vUISALOFyXSG99qVGUqEeayeawyM/FI3\nP8MTlPpUusqyj+eyftaIfUZE/YqBEVGAbG+Z4ugoAyOisJi15a7sL1pLzA+MHEAzmgdG/iLY4aHB\nznb7C14rFR0OHKTlFCQx2IETqVWGLwBALu0PYGCfEVG/YmBEFCDBa3SemAi2NIOI6iwud20q5k3G\nFAVA0611P1czLIi2+9o11MWMkWNZsNVq808MUGPGCADSAS93BYC4GIMoiDBsE4ZVL5sbzjBjRNTv\nGBgRBUTTTcgAHDiYGGNDOFFYrILXY8RSujXFEm4AIECAZqwfGBUrem25a6pLPUaO4+Dw5z+LPde+\nD8bCfFeeE6gPl9BVE3CC7y8CAEEQVh/A4I/s5mQ6or7FwIgoIDPH3EZeSxAgifzRIgoLe4yaiynu\nRXgrgVGhbNR2dyS7FBiVH3kY5UcehmMYUPfs6cpzAm6JoSSLsC1AsKXAR3X7/AEM1cZdRmlmjIj6\nHa/eiAIyO+8GRo7EHyuiMJm1Ujr2GK1FVhIQHBuCIEBV15+CVmjIGCVT4Y/rtg0Ds//61dqv9aNH\nQn/ORn6fkWzGQskYAUBKPr7PqLbLiD1GRH2LV3BEAVlYcN8Apfhgb44n6jWrwIxRM1JSgegtea16\nE+fWki9qkOGOPVe6sMco/4P/gjF7DPBGreszR0N/zkaK4v4dJTMeYmB0/JLXnNdjlGfGiKhvMTAi\nCkhhSQUAxAZ8czxRrzXuMaLViUoSktNaYLS05F68C7IY+l4oc2kJC//5LQDA6BWvBgAYMzOhPudK\nfp+RZMYCX+7qW22XUS7DqXRE/Y6BEVFAyt5dwGR6sDfHE/Uae4yaExUFkpcxUqvrB0aFgvvaJSfC\nz3bP/ce/w1ZVpM85F7mXvQKAmzFqZQltUJRkPWMUWo/RKhmj2lS6stbVvy8RtY6BEVFAVG/SUCY7\n2HtAiHrJcRzuMWqBoCi1jJGqrd9jVPYyGAkl3Js66v7nULj3p4AkYeINfwZpaAhiMgm7UoFVKob6\n3I3qPUZxZGLhTBCtT6WrL3lNxCQkExJMy0Gxuv73hIh6g4ERUUB0zb0IGRoe7M3xRL1kVyuAZUFI\nKBDj3ZmgFkViIgHJdi++NW39qXRV76ZOmNlux3Ew+y93Ao6DkZe9AvHpTRAEAbGpaQCAcbR75XRK\nYyldSBmj5CqldABw0kQGAPD0gXwoz0tEnWFgRBQQ21uiODbKHUZEYantMOJEunWJDRkj/6bNWjSv\nBymdDu+mTulXD6L69O8gZbIYveo1tY/HvcBIn+neZLrlgVEmlOdYrZQOAM7ZPgYA+M3e7u1uIqLW\nMTAiCohguTXj41zuShQaf/AC+4vWJyrJWo+Rrq+fMTJDznbbuo7Zr38NADD2x6+DlKpnaeLTXmB0\ntHuT6fzASDbjyMTDLqVbHhhpexdxNgT85pk59hkR9SEGRkQBMEwLkuPAgYPJ8XBKM4iocYcRA6P1\nNGaMjHUWvNq2A9u0AQDDQ+H0Ry5+/7sw5+cRP2krhn/v95f9Xmxqyj1jFyfTyQl38p5sxqFI4fyd\na1PpGjJGmmpg5sASFAhA2cCBY6VQnpuI2sfAiCgAs3MVCBBgQUA8znHdRGGpT6RjKd16RKW+x8hc\nJzAqVY3actd0JviMkbG4iIXv/CcAYPLP3ghBXH7ZUS+l617GyIm7X4+YpYQ2njzpLXitNmSMZg7X\nB0yMQGA5HVEfYmBEFIDZuTIAwJHD3QFCdKLzl7vKLKVbV2PGyDTsNT+vUNHh38oJY/jC3F1fh6Pr\nyLz4fKTOet5xv+8HRsaxGTj22ucMkim5wyZkM7zhHauV0s0cWqr9/zCA3zwzF9rzE1F7GBgRBWBh\nwR3JKsbC3wNCdCKzWErXEjFR32NkmusERmW9ljFKpoINFKp796D4i59DkGWM/+l/W/2cigIpl4Nj\nmjDnu5NBMSR3b5NohpfdX23B69FDhdr/yxAwc6iIisqx3UT9hIERUQCWllQAQExhGR1RmMwChy+0\nonHBq22uXUq3LGOUCi5j5Ng2Zr96JwAg98pXIT4xuebndrucThUrcOBAMCXYIWWpFCkBURChWzos\n24LjODh2xA2Mznih21c1DODxZxdDeX4iag8DI6IAlIruHUglwAsLIjqenzGSmTFalyDLkAQ3IHLW\nCYyWChokCIAAxOLBZbyL998Hde9eSMPDGLvy1et+brcDo7JZgSW7mRq1uv4o83YJgoCk7A52qJhV\nLMyVoWsWMkMJvPDFWwAAOQCPPjMbyvMTUXsYGBEFoFpx32TDaF4mojoOX2idJHo9j+sFRl62W4pL\ngQ0isDUNc3d9HQAw/rrXQ1SS635+fTJdlwIjvQxLdvuMtGp4pWz1XUYVzHhldNNbhjC5KYtEKoYE\nBDzzzAJsju0m6hsMjIgCoHtvrkMhjbslIpfFUrqWyTHvLd5aOzAqetnuWCK4MuCFu/83zMVFJE7d\nhqGLX9L08+sZo+6M7C4ZjRmjMAMjdzJdxazW+oumNg9DEARsP2McACCrJg7McGw3Ub9gYEQUAMtb\noDg6uv6dUSJqn2NZsMolQBAgZTK9Pk7fk2X3LV6w185IlEtuYJRIBlMGbMzPYfF7dwMAJv/b8eO5\nVxOf3gSgi6V0RhmmlzEKMzBqLKXzJ9JNbXED+lN3uIFRDsCvObabqG8wMCIKgjf1aXwsnC3qRARY\npRLgOJDSGQgSJ0A2E/eyQKLjwFmjXMvvsUmlg5lIN/dv/wrHMJDddSGSO3a09Gdi4+OAKMKcn4et\n64GcYz1Foxx6jxFQn0xXKJaRX6hCkkWMT7kB/UmnjECURaQh4PGn2GdE1C8YGBF1yLJtyN5Fx9Qk\n72IThaU+qpv9Ra2Ixf3ACDCt1QMjQ3UDg2y288Co8runUHzwAQjxOMb/5A0t/zlBlhGbmAAcB8bs\nsY7P0UzZqPcYhVtK5wZG80fdkd0T01lIknvZJckitm4bAQAszRRRCvEcRNQ6BkZEHZpfqEKAAAtA\nUuFUOqKwWEX2F22E3zckANCM4/uMHMeplQEPDW+8P9JxHOgzM1i65yc48sXbcPjWfwQAjLzqcsTG\nxjb0WLU+o6Phl9OV9DLMbvQYxdwKgsIxNwib3rL83+3pZ7kjzHMQ8Pi+hdDOQUSt49IVog7NzpUB\nALYUzEQnIlqdWeBEuo2IKXGgBIiCAN2wgBV9RJphQXQcAAKy2eYTNR3HgTEzg8pTT6L6uydReepJ\nWPn8ss9JnHIqRi+7YuNnnZoG8Gjok+kcx0HZKCMrDwPoTsaoOmsDEDG1eXlgdMr2UUAAsg7w6O9m\nceHzp0I7CxG1hoERUYcWFioAACHGBCxRmOqjupkxakVM8cvjhFUzRoVyfbnraj1GbiB01A2EnnoS\nlaeegrW0PBCSMlkkzzwTyTPPQurMsxDftLmlgQsrxae7M5lOszSYjgUh7pYWamH2GMlJwBGgL7hf\nj5UZo4QSw/imLOYOF3Fg7zxsx4EY0Mh0ImoPAyOiDuXz7h6QIMfdEtHx/FI6LndtTdwr7RUEcfXA\nqGLAzyElU8sDI9vQsf/jfwf90MFlH5ey2VoQlDzjLMQ3bw5k/1G3lryWDDfDH1fc4R2hTqWLJaFU\nMoAlIjusILXKnrsznz+FucNFKLqN544WsW0T/20T9RKv5Ig6VCy4gVEixf4iojBxuevGxJJe35Ag\nQlWPz4wUGzJGyRWvX9qzz0E/dBBCIoHMOeciecZZSJ55FuKbNgW2CHbZWb3AyAi5x8gPjJSk+zdX\n1XBL6VIld8DCymyRb9uOcdy7+xkMA3j06TkGRkQ9xsCIqEOVsvvGmg5o3C0Rra7eY8SLx1ZISQWi\nbcAWY6isEhjly1pDYLT89Us98BwAILtzF6b/+9VhHxVyLgchHodVKsIqlULbU1XS3cAomXT/vuEO\nX0giWcoBAKa3DK/6OdlhBamcgkpexVNPHgMuOS208xBRc2yKIOqQ7r2xDuW43JUoTCyl2xhRSUKy\n3YBIWyUwKiypECFAkARI8vLLAW2/GxglTj45/IMCEAShXk53LLw+o7Lh9oSmU+7rtVY119zx1KmU\nnKpljKbWyBgBwBnedDptoYpCJfw9TkS0NgZGRB2ydPeCI5fb+LhbImqdxYzRhgiKAslxX5+qq5SM\nLRU0AIAcP754RNu/HwCgnHxKiCdcrlZOF2KfkV9Kl02mIMdE2LYDQz++/yoIgi4hoaVhixbGJtNr\nft6O500AAIYBPLZnPpSzEFFrGBgRdcgxbADAxNjab3xE1DkueN0YUVFqGSNVOz5jVC652Yl4cnlg\n5JgmtEMHAUFAYuvW8A/qqU+mCz8wysTSULzx5WGV0y3NuF/fajoPB2tnpcYmM5AVGXEI+M3j4U7l\nI6L1db3HyDRNfOhDH8KhQ4dgGAbe/e534/TTT8cHP/hBiKKIHTt24IYbbuj2sYjaYts2JO/9bnKC\ngRFRWGxdh62qgCRBTKZ6fZxIEBsyRquV0lUrOpJYZfDC4UOAZSE2NQVR6V6JcH3Ja3iBT+F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a0FRquU0QFAwusx0jZYSuc4Do4eWgKw+mLXRqmYGxhVzcqGnqPRC54/CRNAzAH2PZdv+3GIaG0M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\nNCdyT5N6T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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for varname in cloud_vars:\n", + " data[varname].plot(ls='-', linewidth=2)\n", + "plt.ylabel('Cloud cover' + ' %')\n", + "plt.xlabel('Forecast Time ('+str(data.index.tz)+')')\n", + "plt.title('GFS 0.25 deg')\n", + "plt.legend(bbox_to_anchor=(1.18,1.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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temperaturewind_speedghidnidhitotal_cloudslow_cloudsmid_cloudshigh_clouds
2016-04-03 09:00:00-07:0012.0500185.031501569.712283829.66830992.6837440.00.00.00.0
2016-04-03 12:00:00-07:0010.8500065.904075980.540706989.349943100.7489990.00.00.00.0
2016-04-03 15:00:00-07:0019.2500004.729503749.436252913.97999797.0132800.00.00.00.0
2016-04-03 18:00:00-07:0035.9500123.04726186.669267223.87968552.4368250.00.00.00.0
2016-04-03 21:00:00-07:0040.1499941.5160470.0000000.0000000.0000000.00.00.00.0
2016-04-04 00:00:00-07:0031.4500121.0783780.0000000.0000000.0000000.00.00.00.0
2016-04-04 03:00:00-07:0015.8500062.2459070.0000000.0000000.0000000.00.00.00.0
2016-04-04 06:00:00-07:0013.1499941.2041590.0000000.0000000.0000000.00.00.00.0
2016-04-04 09:00:00-07:0011.3500062.067124574.824061832.51650792.8328740.00.00.00.0
2016-04-04 12:00:00-07:009.7500002.182842984.610605990.448411100.8026160.00.00.00.0
2016-04-04 15:00:00-07:0022.7500002.766839752.580636915.21299997.0753580.00.00.00.0
2016-04-04 18:00:00-07:0038.1499943.05188488.648335229.69320352.9810620.00.00.00.0
2016-04-04 21:00:00-07:0041.6499942.4691090.0000000.0000000.0000000.00.00.00.0
2016-04-05 00:00:00-07:0033.1499942.8708530.0000000.0000000.0000002.00.00.02.0
2016-04-05 03:00:00-07:0017.8500062.9071290.0000000.0000000.00000043.00.00.043.0
2016-04-05 06:00:00-07:0014.3500061.2402020.0000000.0000000.00000038.00.00.038.0
2016-04-05 09:00:00-07:0012.0500181.297575385.253540358.322310176.38746739.00.00.039.0
2016-04-05 12:00:00-07:0010.1499940.720278725.077835571.044365213.79636639.00.00.039.0
2016-04-05 15:00:00-07:0022.7500001.301153340.52237691.098199275.05915281.00.00.081.0
2016-04-05 18:00:00-07:0038.1499941.07186864.6524260.00009564.65241191.00.00.091.0
2016-04-05 21:00:00-07:0039.4500124.2174040.0000000.0000000.00000098.00.00.098.0
2016-04-06 00:00:00-07:0031.0500185.0416760.0000000.0000000.00000094.00.00.094.0
2016-04-06 03:00:00-07:0017.9500121.4598970.0000000.0000000.00000099.00.00.099.0
2016-04-06 06:00:00-07:0015.7500001.6422240.0000000.0000000.00000099.00.00.099.0
2016-04-06 09:00:00-07:0013.9500121.746568240.6429330.000000240.642933100.00.00.0100.0
2016-04-06 12:00:00-07:0011.2500001.498166427.38210493.809306343.11299988.00.00.088.0
2016-04-06 15:00:00-07:0023.7500003.153569367.274193141.920516264.95930574.00.00.074.0
2016-04-06 18:00:00-07:0039.3500063.29539165.6212710.01080065.61954381.00.00.081.0
2016-04-06 21:00:00-07:0043.1499943.2237400.0000000.0000000.00000084.00.00.084.0
2016-04-07 00:00:00-07:0034.6499943.2156020.0000000.0000000.00000091.00.00.091.0
2016-04-07 03:00:00-07:0023.0500186.3000080.0000000.0000000.00000097.00.00.097.0
2016-04-07 06:00:00-07:0022.5500187.9839400.0000000.0000000.00000098.00.00.098.0
2016-04-07 09:00:00-07:0018.5500183.587102242.2166500.000000242.216650100.00.047.0100.0
2016-04-07 12:00:00-07:0017.2500001.925201369.5836460.000000369.583646100.00.065.0100.0
2016-04-07 15:00:00-07:0022.5500181.248439296.5939640.000000296.593964100.00.063.0100.0
2016-04-07 18:00:00-07:0022.7500005.58735266.5841540.00000066.584154100.00.081.0100.0
2016-04-07 21:00:00-07:0025.1499944.0190800.0000000.0000000.00000095.00.090.093.0
2016-04-08 00:00:00-07:0024.4500124.8811170.0000000.0000000.00000097.00.082.096.0
2016-04-08 03:00:00-07:0016.6499945.7991810.0000000.0000000.0000004.00.02.02.0
2016-04-08 06:00:00-07:0016.5500186.0927500.0000000.0000000.00000050.00.023.049.0
2016-04-08 09:00:00-07:0014.3500064.480357255.17669427.419078238.87849287.00.024.085.0
2016-04-08 12:00:00-07:0012.9500123.985536500.684197205.320386315.06453776.01.031.063.0
2016-04-08 15:00:00-07:0020.6499943.274263307.22505719.113069293.35893594.01.04.094.0
2016-04-08 18:00:00-07:0033.7500004.37059567.8130172.32768067.42964255.01.02.055.0
2016-04-08 21:00:00-07:0029.2500004.8592700.0000000.0000000.00000052.052.00.01.0
2016-04-09 00:00:00-07:0027.0500185.2581740.0000000.0000000.00000070.062.03.010.0
2016-04-09 03:00:00-07:0016.0500183.2559180.0000000.0000000.00000029.01.00.028.0
2016-04-09 06:00:00-07:0012.7500000.4816640.0000000.0000000.00000015.00.00.015.0
2016-04-09 09:00:00-07:0010.9500120.781025599.428545845.79667093.5254490.00.00.00.0
2016-04-09 12:00:00-07:009.7500002.3479781003.859445995.556452101.0516660.00.00.00.0
2016-04-09 15:00:00-07:0021.2500003.984972767.573173920.99815997.3661740.00.00.00.0
2016-04-09 18:00:00-07:0031.9500127.94730898.694727258.22647955.5615740.00.00.00.0
2016-04-09 21:00:00-07:0033.14999411.0143550.0000000.0000000.0000000.00.00.00.0
2016-04-10 00:00:00-07:0025.35000610.8638210.0000000.0000000.0000000.00.00.00.0
2016-04-10 03:00:00-07:0014.7500004.4807370.0000000.0000000.0000000.00.00.00.0
2016-04-10 06:00:00-07:0012.2500003.2540590.0000000.0000000.0000000.00.00.00.0
\n", + "
" + ], + "text/plain": [ + " temperature wind_speed ghi dni \\\n", + "2016-04-03 09:00:00-07:00 12.050018 5.031501 569.712283 829.668309 \n", + "2016-04-03 12:00:00-07:00 10.850006 5.904075 980.540706 989.349943 \n", + "2016-04-03 15:00:00-07:00 19.250000 4.729503 749.436252 913.979997 \n", + "2016-04-03 18:00:00-07:00 35.950012 3.047261 86.669267 223.879685 \n", + "2016-04-03 21:00:00-07:00 40.149994 1.516047 0.000000 0.000000 \n", + "2016-04-04 00:00:00-07:00 31.450012 1.078378 0.000000 0.000000 \n", + "2016-04-04 03:00:00-07:00 15.850006 2.245907 0.000000 0.000000 \n", + "2016-04-04 06:00:00-07:00 13.149994 1.204159 0.000000 0.000000 \n", + "2016-04-04 09:00:00-07:00 11.350006 2.067124 574.824061 832.516507 \n", + "2016-04-04 12:00:00-07:00 9.750000 2.182842 984.610605 990.448411 \n", + "2016-04-04 15:00:00-07:00 22.750000 2.766839 752.580636 915.212999 \n", + "2016-04-04 18:00:00-07:00 38.149994 3.051884 88.648335 229.693203 \n", + "2016-04-04 21:00:00-07:00 41.649994 2.469109 0.000000 0.000000 \n", + "2016-04-05 00:00:00-07:00 33.149994 2.870853 0.000000 0.000000 \n", + "2016-04-05 03:00:00-07:00 17.850006 2.907129 0.000000 0.000000 \n", + "2016-04-05 06:00:00-07:00 14.350006 1.240202 0.000000 0.000000 \n", + "2016-04-05 09:00:00-07:00 12.050018 1.297575 385.253540 358.322310 \n", + "2016-04-05 12:00:00-07:00 10.149994 0.720278 725.077835 571.044365 \n", + "2016-04-05 15:00:00-07:00 22.750000 1.301153 340.522376 91.098199 \n", + "2016-04-05 18:00:00-07:00 38.149994 1.071868 64.652426 0.000095 \n", + "2016-04-05 21:00:00-07:00 39.450012 4.217404 0.000000 0.000000 \n", + "2016-04-06 00:00:00-07:00 31.050018 5.041676 0.000000 0.000000 \n", + "2016-04-06 03:00:00-07:00 17.950012 1.459897 0.000000 0.000000 \n", + "2016-04-06 06:00:00-07:00 15.750000 1.642224 0.000000 0.000000 \n", + "2016-04-06 09:00:00-07:00 13.950012 1.746568 240.642933 0.000000 \n", + "2016-04-06 12:00:00-07:00 11.250000 1.498166 427.382104 93.809306 \n", + "2016-04-06 15:00:00-07:00 23.750000 3.153569 367.274193 141.920516 \n", + "2016-04-06 18:00:00-07:00 39.350006 3.295391 65.621271 0.010800 \n", + "2016-04-06 21:00:00-07:00 43.149994 3.223740 0.000000 0.000000 \n", + "2016-04-07 00:00:00-07:00 34.649994 3.215602 0.000000 0.000000 \n", + "2016-04-07 03:00:00-07:00 23.050018 6.300008 0.000000 0.000000 \n", + "2016-04-07 06:00:00-07:00 22.550018 7.983940 0.000000 0.000000 \n", + "2016-04-07 09:00:00-07:00 18.550018 3.587102 242.216650 0.000000 \n", + "2016-04-07 12:00:00-07:00 17.250000 1.925201 369.583646 0.000000 \n", + "2016-04-07 15:00:00-07:00 22.550018 1.248439 296.593964 0.000000 \n", + "2016-04-07 18:00:00-07:00 22.750000 5.587352 66.584154 0.000000 \n", + "2016-04-07 21:00:00-07:00 25.149994 4.019080 0.000000 0.000000 \n", + "2016-04-08 00:00:00-07:00 24.450012 4.881117 0.000000 0.000000 \n", + "2016-04-08 03:00:00-07:00 16.649994 5.799181 0.000000 0.000000 \n", + "2016-04-08 06:00:00-07:00 16.550018 6.092750 0.000000 0.000000 \n", + "2016-04-08 09:00:00-07:00 14.350006 4.480357 255.176694 27.419078 \n", + "2016-04-08 12:00:00-07:00 12.950012 3.985536 500.684197 205.320386 \n", + "2016-04-08 15:00:00-07:00 20.649994 3.274263 307.225057 19.113069 \n", + "2016-04-08 18:00:00-07:00 33.750000 4.370595 67.813017 2.327680 \n", + "2016-04-08 21:00:00-07:00 29.250000 4.859270 0.000000 0.000000 \n", + "2016-04-09 00:00:00-07:00 27.050018 5.258174 0.000000 0.000000 \n", + "2016-04-09 03:00:00-07:00 16.050018 3.255918 0.000000 0.000000 \n", + "2016-04-09 06:00:00-07:00 12.750000 0.481664 0.000000 0.000000 \n", + "2016-04-09 09:00:00-07:00 10.950012 0.781025 599.428545 845.796670 \n", + "2016-04-09 12:00:00-07:00 9.750000 2.347978 1003.859445 995.556452 \n", + "2016-04-09 15:00:00-07:00 21.250000 3.984972 767.573173 920.998159 \n", + "2016-04-09 18:00:00-07:00 31.950012 7.947308 98.694727 258.226479 \n", + "2016-04-09 21:00:00-07:00 33.149994 11.014355 0.000000 0.000000 \n", + "2016-04-10 00:00:00-07:00 25.350006 10.863821 0.000000 0.000000 \n", + "2016-04-10 03:00:00-07:00 14.750000 4.480737 0.000000 0.000000 \n", + "2016-04-10 06:00:00-07:00 12.250000 3.254059 0.000000 0.000000 \n", + "\n", + " dhi total_clouds low_clouds mid_clouds \\\n", + "2016-04-03 09:00:00-07:00 92.683744 0.0 0.0 0.0 \n", + "2016-04-03 12:00:00-07:00 100.748999 0.0 0.0 0.0 \n", + "2016-04-03 15:00:00-07:00 97.013280 0.0 0.0 0.0 \n", + "2016-04-03 18:00:00-07:00 52.436825 0.0 0.0 0.0 \n", + "2016-04-03 21:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 00:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 03:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 06:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 09:00:00-07:00 92.832874 0.0 0.0 0.0 \n", + "2016-04-04 12:00:00-07:00 100.802616 0.0 0.0 0.0 \n", + "2016-04-04 15:00:00-07:00 97.075358 0.0 0.0 0.0 \n", + "2016-04-04 18:00:00-07:00 52.981062 0.0 0.0 0.0 \n", + "2016-04-04 21:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-05 00:00:00-07:00 0.000000 2.0 0.0 0.0 \n", + "2016-04-05 03:00:00-07:00 0.000000 43.0 0.0 0.0 \n", + "2016-04-05 06:00:00-07:00 0.000000 38.0 0.0 0.0 \n", + "2016-04-05 09:00:00-07:00 176.387467 39.0 0.0 0.0 \n", + "2016-04-05 12:00:00-07:00 213.796366 39.0 0.0 0.0 \n", + "2016-04-05 15:00:00-07:00 275.059152 81.0 0.0 0.0 \n", + "2016-04-05 18:00:00-07:00 64.652411 91.0 0.0 0.0 \n", + "2016-04-05 21:00:00-07:00 0.000000 98.0 0.0 0.0 \n", + "2016-04-06 00:00:00-07:00 0.000000 94.0 0.0 0.0 \n", + "2016-04-06 03:00:00-07:00 0.000000 99.0 0.0 0.0 \n", + "2016-04-06 06:00:00-07:00 0.000000 99.0 0.0 0.0 \n", + "2016-04-06 09:00:00-07:00 240.642933 100.0 0.0 0.0 \n", + "2016-04-06 12:00:00-07:00 343.112999 88.0 0.0 0.0 \n", + "2016-04-06 15:00:00-07:00 264.959305 74.0 0.0 0.0 \n", + "2016-04-06 18:00:00-07:00 65.619543 81.0 0.0 0.0 \n", + "2016-04-06 21:00:00-07:00 0.000000 84.0 0.0 0.0 \n", + "2016-04-07 00:00:00-07:00 0.000000 91.0 0.0 0.0 \n", + "2016-04-07 03:00:00-07:00 0.000000 97.0 0.0 0.0 \n", + "2016-04-07 06:00:00-07:00 0.000000 98.0 0.0 0.0 \n", + "2016-04-07 09:00:00-07:00 242.216650 100.0 0.0 47.0 \n", + "2016-04-07 12:00:00-07:00 369.583646 100.0 0.0 65.0 \n", + "2016-04-07 15:00:00-07:00 296.593964 100.0 0.0 63.0 \n", + "2016-04-07 18:00:00-07:00 66.584154 100.0 0.0 81.0 \n", + "2016-04-07 21:00:00-07:00 0.000000 95.0 0.0 90.0 \n", + "2016-04-08 00:00:00-07:00 0.000000 97.0 0.0 82.0 \n", + "2016-04-08 03:00:00-07:00 0.000000 4.0 0.0 2.0 \n", + "2016-04-08 06:00:00-07:00 0.000000 50.0 0.0 23.0 \n", + "2016-04-08 09:00:00-07:00 238.878492 87.0 0.0 24.0 \n", + "2016-04-08 12:00:00-07:00 315.064537 76.0 1.0 31.0 \n", + "2016-04-08 15:00:00-07:00 293.358935 94.0 1.0 4.0 \n", + "2016-04-08 18:00:00-07:00 67.429642 55.0 1.0 2.0 \n", + "2016-04-08 21:00:00-07:00 0.000000 52.0 52.0 0.0 \n", + "2016-04-09 00:00:00-07:00 0.000000 70.0 62.0 3.0 \n", + "2016-04-09 03:00:00-07:00 0.000000 29.0 1.0 0.0 \n", + "2016-04-09 06:00:00-07:00 0.000000 15.0 0.0 0.0 \n", + "2016-04-09 09:00:00-07:00 93.525449 0.0 0.0 0.0 \n", + "2016-04-09 12:00:00-07:00 101.051666 0.0 0.0 0.0 \n", + "2016-04-09 15:00:00-07:00 97.366174 0.0 0.0 0.0 \n", + "2016-04-09 18:00:00-07:00 55.561574 0.0 0.0 0.0 \n", + "2016-04-09 21:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-10 00:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-10 03:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-10 06:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "\n", + " high_clouds \n", + "2016-04-03 09:00:00-07:00 0.0 \n", + "2016-04-03 12:00:00-07:00 0.0 \n", + "2016-04-03 15:00:00-07:00 0.0 \n", + "2016-04-03 18:00:00-07:00 0.0 \n", + "2016-04-03 21:00:00-07:00 0.0 \n", + "2016-04-04 00:00:00-07:00 0.0 \n", + "2016-04-04 03:00:00-07:00 0.0 \n", + "2016-04-04 06:00:00-07:00 0.0 \n", + "2016-04-04 09:00:00-07:00 0.0 \n", + "2016-04-04 12:00:00-07:00 0.0 \n", + "2016-04-04 15:00:00-07:00 0.0 \n", + "2016-04-04 18:00:00-07:00 0.0 \n", + "2016-04-04 21:00:00-07:00 0.0 \n", + "2016-04-05 00:00:00-07:00 2.0 \n", + "2016-04-05 03:00:00-07:00 43.0 \n", + "2016-04-05 06:00:00-07:00 38.0 \n", + "2016-04-05 09:00:00-07:00 39.0 \n", + "2016-04-05 12:00:00-07:00 39.0 \n", + "2016-04-05 15:00:00-07:00 81.0 \n", + "2016-04-05 18:00:00-07:00 91.0 \n", + "2016-04-05 21:00:00-07:00 98.0 \n", + "2016-04-06 00:00:00-07:00 94.0 \n", + "2016-04-06 03:00:00-07:00 99.0 \n", + "2016-04-06 06:00:00-07:00 99.0 \n", + "2016-04-06 09:00:00-07:00 100.0 \n", + "2016-04-06 12:00:00-07:00 88.0 \n", + "2016-04-06 15:00:00-07:00 74.0 \n", + "2016-04-06 18:00:00-07:00 81.0 \n", + "2016-04-06 21:00:00-07:00 84.0 \n", + "2016-04-07 00:00:00-07:00 91.0 \n", + "2016-04-07 03:00:00-07:00 97.0 \n", + "2016-04-07 06:00:00-07:00 98.0 \n", + "2016-04-07 09:00:00-07:00 100.0 \n", + "2016-04-07 12:00:00-07:00 100.0 \n", + "2016-04-07 15:00:00-07:00 100.0 \n", + "2016-04-07 18:00:00-07:00 100.0 \n", + "2016-04-07 21:00:00-07:00 93.0 \n", + "2016-04-08 00:00:00-07:00 96.0 \n", + "2016-04-08 03:00:00-07:00 2.0 \n", + "2016-04-08 06:00:00-07:00 49.0 \n", + "2016-04-08 09:00:00-07:00 85.0 \n", + "2016-04-08 12:00:00-07:00 63.0 \n", + "2016-04-08 15:00:00-07:00 94.0 \n", + "2016-04-08 18:00:00-07:00 55.0 \n", + "2016-04-08 21:00:00-07:00 1.0 \n", + "2016-04-09 00:00:00-07:00 10.0 \n", + "2016-04-09 03:00:00-07:00 28.0 \n", + "2016-04-09 06:00:00-07:00 15.0 \n", + "2016-04-09 09:00:00-07:00 0.0 \n", + "2016-04-09 12:00:00-07:00 0.0 \n", + "2016-04-09 15:00:00-07:00 0.0 \n", + "2016-04-09 18:00:00-07:00 0.0 \n", + "2016-04-09 21:00:00-07:00 0.0 \n", + "2016-04-10 00:00:00-07:00 0.0 \n", + "2016-04-10 03:00:00-07:00 0.0 \n", + "2016-04-10 06:00:00-07:00 0.0 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## NAM" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fm = NAM()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# retrieve data\n", + "data = fm.get_processed_data(latitude, longitude, start, end)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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8DMP6N7366qvZZ599OOKII3jttdcAuOSSS/jud7/Lfvvtx2WXXUY0GmX33XfP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BBC7EOBESGELIKZPDDye4Hw2//vW/HGGxUwmL5svADMdcrbGzgDJIJQAbz4Lydx4P99yduFtaFU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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for varname in cloud_vars:\n", + " data[varname].plot(ls='-', linewidth=2)\n", + "plt.ylabel('Cloud cover' + ' %')\n", + "plt.xlabel('Forecast Time ('+str(data.index.tz)+')')\n", + "plt.title('NAM')\n", + "plt.legend(bbox_to_anchor=(1.18,1.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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mZhgyo2ple0ki0pGNA2wLZbAjaoDNYMWpzo9noABY3haA5DL/P0sedKT5MEuJ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\n3oJSisWLF3P69GkGDhzI9u3bWb9+PY0bN2bChAkEBATQrVs3du3aRW5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temperaturewind_speedghidnidhitotal_cloudslow_cloudsmid_cloudshigh_clouds
2016-04-03 09:00:00-07:007.2490233.827341569.712283829.66830992.6837440.00.00.00.0
2016-04-03 12:00:00-07:003.6803282.305870980.540706989.349943100.7489990.00.00.00.0
2016-04-03 15:00:00-07:0015.8527533.704706749.436252913.97999797.0132800.00.00.00.0
2016-04-03 18:00:00-07:0034.1723022.07484186.669267223.87968552.4368250.00.00.00.0
2016-04-03 21:00:00-07:0037.6000371.1484340.0000000.0000000.0000000.00.00.00.0
2016-04-04 00:00:00-07:0030.4402771.0595630.0000000.0000000.0000000.00.00.00.0
2016-04-04 03:00:00-07:0015.2794491.5079300.0000000.0000000.0000000.00.00.00.0
2016-04-04 06:00:00-07:0010.9327091.8083500.0000000.0000000.0000000.00.00.00.0
2016-04-04 09:00:00-07:008.4081732.475026574.824061832.51650792.8328740.00.00.00.0
2016-04-04 12:00:00-07:006.2982482.828518984.610605990.448411100.8026160.00.00.00.0
2016-04-04 15:00:00-07:0018.2974553.101655752.580636915.21299997.0753580.00.00.00.0
2016-04-04 18:00:00-07:0037.5059201.78529988.648335229.69320352.9810620.00.00.00.0
2016-04-04 21:00:00-07:0040.1199041.8045220.0000000.0000000.0000000.00.00.00.0
2016-04-05 00:00:00-07:0032.6221011.8625860.0000000.0000000.0000000.00.00.00.0
2016-04-05 03:00:00-07:0017.3773803.6458590.0000000.0000000.000000100.00.00.0100.0
2016-04-05 06:00:00-07:0013.5032041.8929870.0000000.0000000.0000006.00.00.06.0
2016-04-05 09:00:00-07:0012.8854982.041240241.8042236.756694237.86574694.00.078.092.0
2016-04-05 12:00:00-07:007.6499942.070144947.134436925.347866118.6292516.00.00.06.0
2016-04-05 15:00:00-07:0020.0068971.689175755.676166916.42010597.1360990.00.00.00.0
2016-04-05 18:00:00-07:0037.0630190.93580190.638153235.47348953.5156710.00.00.00.0
2016-04-05 21:00:00-07:0039.0474242.6643090.0000000.0000000.00000048.00.00.048.0
2016-04-06 00:00:00-07:0031.1661683.6710850.0000000.0000000.0000008.00.00.08.0
2016-04-06 03:00:00-07:0017.3678593.1973800.0000000.0000000.00000064.00.00.064.0
2016-04-06 06:00:00-07:0013.1454161.9940720.0000000.0000000.00000078.00.00.078.0
2016-04-06 09:00:00-07:0011.0156562.358184353.823204275.542977192.13710348.00.00.048.0
2016-04-06 12:00:00-07:008.7776182.788645593.572659358.102413271.88847560.00.00.060.0
2016-04-06 15:00:00-07:0020.7987372.361150492.829089390.715694211.15006346.00.00.046.0
2016-04-06 18:00:00-07:0038.5467223.45511775.06227784.30027761.57339816.00.00.016.0
2016-04-06 21:00:00-07:0042.1813663.6386430.0000000.0000000.00000098.00.00.098.0
2016-04-07 00:00:00-07:0035.6238102.8179840.0000000.0000000.0000008.00.00.08.0
2016-04-07 03:00:00-07:0020.0541081.4950720.0000000.0000000.00000094.00.00.094.0
2016-04-07 06:00:00-07:0017.3672794.6319910.0000000.0000000.00000092.00.00.092.0
\n", + "
" + ], + "text/plain": [ + " temperature wind_speed ghi dni \\\n", + "2016-04-03 09:00:00-07:00 7.249023 3.827341 569.712283 829.668309 \n", + "2016-04-03 12:00:00-07:00 3.680328 2.305870 980.540706 989.349943 \n", + "2016-04-03 15:00:00-07:00 15.852753 3.704706 749.436252 913.979997 \n", + "2016-04-03 18:00:00-07:00 34.172302 2.074841 86.669267 223.879685 \n", + "2016-04-03 21:00:00-07:00 37.600037 1.148434 0.000000 0.000000 \n", + "2016-04-04 00:00:00-07:00 30.440277 1.059563 0.000000 0.000000 \n", + "2016-04-04 03:00:00-07:00 15.279449 1.507930 0.000000 0.000000 \n", + "2016-04-04 06:00:00-07:00 10.932709 1.808350 0.000000 0.000000 \n", + "2016-04-04 09:00:00-07:00 8.408173 2.475026 574.824061 832.516507 \n", + "2016-04-04 12:00:00-07:00 6.298248 2.828518 984.610605 990.448411 \n", + "2016-04-04 15:00:00-07:00 18.297455 3.101655 752.580636 915.212999 \n", + "2016-04-04 18:00:00-07:00 37.505920 1.785299 88.648335 229.693203 \n", + "2016-04-04 21:00:00-07:00 40.119904 1.804522 0.000000 0.000000 \n", + "2016-04-05 00:00:00-07:00 32.622101 1.862586 0.000000 0.000000 \n", + "2016-04-05 03:00:00-07:00 17.377380 3.645859 0.000000 0.000000 \n", + "2016-04-05 06:00:00-07:00 13.503204 1.892987 0.000000 0.000000 \n", + "2016-04-05 09:00:00-07:00 12.885498 2.041240 241.804223 6.756694 \n", + "2016-04-05 12:00:00-07:00 7.649994 2.070144 947.134436 925.347866 \n", + "2016-04-05 15:00:00-07:00 20.006897 1.689175 755.676166 916.420105 \n", + "2016-04-05 18:00:00-07:00 37.063019 0.935801 90.638153 235.473489 \n", + "2016-04-05 21:00:00-07:00 39.047424 2.664309 0.000000 0.000000 \n", + "2016-04-06 00:00:00-07:00 31.166168 3.671085 0.000000 0.000000 \n", + "2016-04-06 03:00:00-07:00 17.367859 3.197380 0.000000 0.000000 \n", + "2016-04-06 06:00:00-07:00 13.145416 1.994072 0.000000 0.000000 \n", + "2016-04-06 09:00:00-07:00 11.015656 2.358184 353.823204 275.542977 \n", + "2016-04-06 12:00:00-07:00 8.777618 2.788645 593.572659 358.102413 \n", + "2016-04-06 15:00:00-07:00 20.798737 2.361150 492.829089 390.715694 \n", + "2016-04-06 18:00:00-07:00 38.546722 3.455117 75.062277 84.300277 \n", + "2016-04-06 21:00:00-07:00 42.181366 3.638643 0.000000 0.000000 \n", + "2016-04-07 00:00:00-07:00 35.623810 2.817984 0.000000 0.000000 \n", + "2016-04-07 03:00:00-07:00 20.054108 1.495072 0.000000 0.000000 \n", + "2016-04-07 06:00:00-07:00 17.367279 4.631991 0.000000 0.000000 \n", + "\n", + " dhi total_clouds low_clouds mid_clouds \\\n", + "2016-04-03 09:00:00-07:00 92.683744 0.0 0.0 0.0 \n", + "2016-04-03 12:00:00-07:00 100.748999 0.0 0.0 0.0 \n", + "2016-04-03 15:00:00-07:00 97.013280 0.0 0.0 0.0 \n", + "2016-04-03 18:00:00-07:00 52.436825 0.0 0.0 0.0 \n", + "2016-04-03 21:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 00:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 03:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 06:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 09:00:00-07:00 92.832874 0.0 0.0 0.0 \n", + "2016-04-04 12:00:00-07:00 100.802616 0.0 0.0 0.0 \n", + "2016-04-04 15:00:00-07:00 97.075358 0.0 0.0 0.0 \n", + "2016-04-04 18:00:00-07:00 52.981062 0.0 0.0 0.0 \n", + "2016-04-04 21:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-05 00:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-05 03:00:00-07:00 0.000000 100.0 0.0 0.0 \n", + "2016-04-05 06:00:00-07:00 0.000000 6.0 0.0 0.0 \n", + "2016-04-05 09:00:00-07:00 237.865746 94.0 0.0 78.0 \n", + "2016-04-05 12:00:00-07:00 118.629251 6.0 0.0 0.0 \n", + "2016-04-05 15:00:00-07:00 97.136099 0.0 0.0 0.0 \n", + "2016-04-05 18:00:00-07:00 53.515671 0.0 0.0 0.0 \n", + "2016-04-05 21:00:00-07:00 0.000000 48.0 0.0 0.0 \n", + "2016-04-06 00:00:00-07:00 0.000000 8.0 0.0 0.0 \n", + "2016-04-06 03:00:00-07:00 0.000000 64.0 0.0 0.0 \n", + "2016-04-06 06:00:00-07:00 0.000000 78.0 0.0 0.0 \n", + "2016-04-06 09:00:00-07:00 192.137103 48.0 0.0 0.0 \n", + "2016-04-06 12:00:00-07:00 271.888475 60.0 0.0 0.0 \n", + "2016-04-06 15:00:00-07:00 211.150063 46.0 0.0 0.0 \n", + "2016-04-06 18:00:00-07:00 61.573398 16.0 0.0 0.0 \n", + "2016-04-06 21:00:00-07:00 0.000000 98.0 0.0 0.0 \n", + "2016-04-07 00:00:00-07:00 0.000000 8.0 0.0 0.0 \n", + "2016-04-07 03:00:00-07:00 0.000000 94.0 0.0 0.0 \n", + "2016-04-07 06:00:00-07:00 0.000000 92.0 0.0 0.0 \n", + "\n", + " high_clouds \n", + "2016-04-03 09:00:00-07:00 0.0 \n", + "2016-04-03 12:00:00-07:00 0.0 \n", + "2016-04-03 15:00:00-07:00 0.0 \n", + "2016-04-03 18:00:00-07:00 0.0 \n", + "2016-04-03 21:00:00-07:00 0.0 \n", + "2016-04-04 00:00:00-07:00 0.0 \n", + "2016-04-04 03:00:00-07:00 0.0 \n", + "2016-04-04 06:00:00-07:00 0.0 \n", + "2016-04-04 09:00:00-07:00 0.0 \n", + "2016-04-04 12:00:00-07:00 0.0 \n", + "2016-04-04 15:00:00-07:00 0.0 \n", + "2016-04-04 18:00:00-07:00 0.0 \n", + "2016-04-04 21:00:00-07:00 0.0 \n", + "2016-04-05 00:00:00-07:00 0.0 \n", + "2016-04-05 03:00:00-07:00 100.0 \n", + "2016-04-05 06:00:00-07:00 6.0 \n", + "2016-04-05 09:00:00-07:00 92.0 \n", + "2016-04-05 12:00:00-07:00 6.0 \n", + "2016-04-05 15:00:00-07:00 0.0 \n", + "2016-04-05 18:00:00-07:00 0.0 \n", + "2016-04-05 21:00:00-07:00 48.0 \n", + "2016-04-06 00:00:00-07:00 8.0 \n", + "2016-04-06 03:00:00-07:00 64.0 \n", + "2016-04-06 06:00:00-07:00 78.0 \n", + "2016-04-06 09:00:00-07:00 48.0 \n", + "2016-04-06 12:00:00-07:00 60.0 \n", + "2016-04-06 15:00:00-07:00 46.0 \n", + "2016-04-06 18:00:00-07:00 16.0 \n", + "2016-04-06 21:00:00-07:00 98.0 \n", + "2016-04-07 00:00:00-07:00 8.0 \n", + "2016-04-07 03:00:00-07:00 94.0 \n", + "2016-04-07 06:00:00-07:00 92.0 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## NDFD" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fm = NDFD()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# retrieve data\n", + "data = fm.get_processed_data(latitude, longitude, start, end)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "total_cloud_cover = data['total_clouds']\n", + "temp = data['temperature']\n", + "wind = data['wind_speed']" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0, 100)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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b7WbNmumhhx7SNddcI5/Pp3fffVdt2vADDAAQWo61a2T4fHK3biPFxlpdDhBa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\nsOMBAABUNmETpHNzc5WYmFj62OFwyOf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3Nzeeeuopzp8/3+Zv/eE1QK7a8R/tLw/VqqSk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\nYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "total_cloud_cover.plot(color='r', linewidth=2)\n", + "plt.ylabel('Total cloud cover' + ' (%s)' % fm.units['total_clouds'])\n", + "plt.xlabel('Forecast Time ('+str(data.index.tz)+')')\n", + "plt.title('NDFD')\n", + "plt.ylim(0,100)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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m2v0laU6ykDNq7uRxoOtpuWemjp3TNm0EwOzZy+VIDnTAVsAM+MHaMKPZ/aQ5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\nqlVVqBXl2FlZ/vgeOJimER0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gj5NmNd4EGISTZhSF6NhGUzR8pOGk2eNvFo8geoI/J2gk\nZjQHZIX2AVTV0yUaIT8uNTmEyPhgzGtOTM7waBOgw+rdB7NnL9SyMk8/RWmpxNMXjzUBOiKN65qT\n9FS3ye/+ffv2MXv2bJ588kkee+wxHnvsMX71q18l5eZ+ljhp9uiM5saM4U7S7N+65sRJcwBqmgEi\nJ/tzyYkaH7No+jhpdk5FdJ8tOQnijObGvNwM6PfSDEf0lHHYqhp7E1BV5XY4LVNTg77kC2xF8Wyy\n1lhgSzQMI/G96tXvC3PwEKz2JWjbt6F9uS4p19Sb+g0vv/xyUm4UNL46aR7u/7FziZNmH89obixx\n0jxvbixx88P4tkgEddfO2MQYH592mr37YLVpg7ZrJ+rWLd6ds36QINc0g7ebAf0+OcNhF7XBGHkc\noUWfE543h8iZZ7sdUrOFvvgcJRolOnQ4dmGR2+E0KXLaGeT85U+EZ35Mze13uR1O0uirVqDU1GD0\n6o3dvr3b4RyaqhKZMJHsv71OeMZH1Pbr3+pLNpk0X3nllYlxc4395S9/afXN/cwvjYAARv+BsS7S\nrzagVJT74gfNwZTSYJ00mwMGYrVti7ZjO+rGr7B693E7pCapO7aj2HasptZrTR/NoapEjx9F1sf/\nIbToM+p98D0MDdMzzABOz4BGzYBLvgDL8k4ZRF0d+rKl2Iriu6UmhxKZMJHQos8JzZrhz6TZJ6UZ\njshJ42J//36xCGX/Puy27dwOKSl0j85nPlhk4ulk/+11QjOnU3vd9a2+XpM/la699lquueYarrnm\nGq644gq6devGsGHeGi2Sdj6Z0ZwQDmMMGgyAvnyZy8G0jOqMnAvISTOqmjhtDs+b63IwR8fvM5ob\nO6BEww/q6lD378fWdeySErejSQmrU2fMTp1RK8rRvvJOg6y+bClKNIo5YGAglsr4fV5zoglwtD+S\nZvLziY4+KTbFwee15I05S028XiKTaH79dA7U17f6ek0mzWPHjk38M378eO6//37mzvXHX/Kpom7f\nhmJZsUfU4bDb4RwVY1h8yYlPSzSck2Y7ICfNANGT4vOafVLXnChJ8um4ucb8tk47Uc/cqbN3TmBT\nwBgZr2teON/lSBoEpTTDET1+FHZuHvqa1f5buhGJEPo8nqyN8UnSTMN2uiDVNYc+j/3s9Pr3hdWx\nE8agISi1tUn5udLkT99du3Yd8M+cOXMojZ/6ZSot3gzll1pI8H8zYOKkOQjTM+L8thkwcdLs08Um\njRnHHY+tKLGJMnV1bofTJC3IkzMaiZx+JgDZL7/gciQNgpY0Ew4Tcd6w++zkU1+2BKW2FqNvP189\ncTmgGdCyXI6m9dSdO9A2f42VX4A5YKDb4TQpMUUjCUtmmkyaL7zwQi666KLE/z7++OPcdVdwitlb\nwk9NgA6/NwMmTpoDMKfZYQ4chFVQiLZ9my9OfNR40uznGc0Ou7AIs/8AlGgUfbn3RzEGfXKGo+47\nF2DlFxBaOB9t5Qq3wwHbbqjdDErSTKNH1j4r0Uiszh7j7fnMBzMHDoqVHu3Z7Y2v61bSP2s0glHT\nXI6mac5JfzLmNTeZNL///vvMnDmTWbNmMXPmTN566y0GDvT+O4tU0jZ/DYDZ3UdJ84BBsWaEDetR\nKivcDqfZnOkZQTppjs2mjZfNeHBiwMEST1gCUJ4BjZeceL9EQ90Re1Nldg72STP5+dRfeDEAOc/9\n2eVgYiMWtV07sdq0wexzrNvhJE1iXvPsmb4auxiaHysNjY4Z63IkzaQoDafNM/xfouG3py/R0WOx\ns7MJrViGsnt3q6512KR59+7d7Nq1i0svvTTx77t27WL79u1ceeWVR3VxwzD4yU9+wmWXXcaFF17I\n9OnT2bx5M5deeilTpkzh/vvvb1XwbknMaO7m/RnNCeEwxkCfNgNaFkq8PMNu08blYJKrYcyW92bT\nHiwxo7mrf2c0N2Y4dc0+aAZUd2wDwOrsg8bjVqq94moAst58zfU3+Ink4PhRgaolNwcMxOzYKbaV\ndM1qt8M5OpZFaEGsJtXrmwAPJUjzmv3SBJiQnZ34mgnPbl1J0mF/Cjz22GNceOGFbNy4kQsuuIAL\nL7yQCy+8kClTpnDSSUdXgP/uu+9SXFzMSy+9xLPPPssDDzzAww8/zK233sqLL76IZVl89JH/voD8\nWJ4BYAxz6pr9VaKhVFbEGi/zC3zTeHm0oiO9u9DhALbdUNPcLRhJs3NKovugGVCLnzRbQT9pJpbQ\nRcaejFpdRdYbr7kaizNdxetjtZpNUYiOPxVofRKRLtrqVajlZZjduvtyI2l0/KmxxTIL56NUVbod\nTsvV1ydyCD+NYHRWaodbWaJx2KT5kUceYdasWdxwww3MmjUr8c/06dO55557jurikydP5uabbwbA\nNE00TWPVqlWccELshR4/fjzz5s1r1R/ADX6a0dxYoq55ib+aAZ1tgEGqZ3Y0zKZd7OnHpErpfpSa\nGkbjZVEAACAASURBVKyCQl/O+T4Us28/rMKiWE359m1uh3NEmVLT7Ki74ioAcv7vz65+X/jtMXRz\nROJJs1+aAROlGaN9VpoRZ7cpxjh+FIphEPpkttvhtJi+bAlKJIIxYCB2kX+e/DrNgKFWrtQ+qkbA\nF154gWeeeYann36a3//+99x5551HdfGcnBxyc3Opqqri5ptv5pZbbsFuFGxeXh6VlT57x2UYsRnN\niuK7ebXGyFgpQOiLz12OpHkCWc8cZ3XugtmhI2p5GdpG78ymPVjD5Ax/fc0fkao2vGnx+Gmz0yhq\nBnx6hqP+nG9htS9BX70KfYFL4+dqa9GXL8Nu9HUSJA3za+dCJOJyNE1LzGf2YWmGIwglGqF4E6Bv\nSjPizAEDMTt1Rtu9C23VyhZfp8mNgDfeeCOdOnVixYoVTJw4kblz5zJo0KCjvsGOHTu44YYbmDJl\nCueccw6PPvpo4r9VV1dTWHjkYfHFxbnouoe6MzdtAtOErl0p6ereZp+SkoLmf9D4MZCbi7ZpIyXU\ngV9G9lixkWChDu1b9udOgaTGMWY0vPsubdevgtEjk3fdZKrYC4Deq6cnPgdJi2HCOJg5naKVS2Da\n5cm5ZrJZFsST5nbD+kN2tssBJfnr/3CuvQZ+/nOKX3kOzpuU+vsdbM5SMAwYNoz2vbx3wt/qz0FJ\nAQwejLJyJSUbVsL48ckJLBVsGxbEkuaCcyZR4NefQd/5FvzyIXJmfUxO+3w4xLZlz1sWKyXMmTie\nHBc/Dy16/c8+C557jrafz4VTW/bEosmked++fbz00kv88pe/ZPLkyfzwhz9k2rRpR3XxvXv3ctVV\nV3HvvfcyZswYAAYOHMhnn33GqFGjmD17duLXD6e0tOao7pUuoSWraANEu3ajbI87p+QlJQXsaeG9\ni0YcR/jTOZR/OIPIWZOTHFlqZG3aRiFQl19IpUuveWOtef0PJXfQMPLefZeaWXOonvStpF03mXJW\nrSMfqO3QmSqXPwfJfP1DA4fFvp8/meva93NTlF27aG8YWG3bsq8yCpVRV+NJ9tf/4ajfvZS2Dz8M\nb77JvrsfTPtc3pz/zIx9zY84wfWv+YMl63OQd/J4cleupPqd96kZ6NE37ID21Xra7tqF1b6EfcWd\nwa8/g7r3o13btqibNrF//mLMY/smP7hUsm3azpmLBuwfMBzTZzlQ1phxFD73HJH3/0n596874vUP\np8nyDOckuGfPnqxdu5bCwkIMwziqAJ955hkqKip46qmnuPzyy5k6dSo/+tGPeOKJJ7j44osxDIOz\nzz77qK7lFX5tAnQYiTFb3p8Y4AjiNsDGGpoBvTt2Tt0an9HswwacIzGOi/VX6MuWePYRtbbT2Qbo\nvdPOVLK6dScy6WyUaJTsV9K/7KShnnlU2u+dLokSjVkz3Q2kCYnSjDEn+fN01qFpDYs2pv/H5WCa\nT92yGW33LqziYl+OYIyMn4itKITmfwo1LTuQbfKk+cQTT+SWW27h1ltv5ZprrmH16tXoepMfBsBd\nd911yEUoL7zgnW1PzeXHGc2NOeuDvV7D2VhiG2Db4NU0Aw2zmlcsg2gUQiGXI/omZ7GJ3+r4m2K3\nKcbo2w/9y3XoK5YlkmgvUbfHkmazS2YlzRBrCMz68ANy/u8v1F5/c/oWKdh2YqyWMSp4TYCOyNhT\nsHUdffEilPIyzzZ2hebFmwDH+md19uFEJp5B9ltvEp7+EbXX/tDtcJrlgMZYH755sdu3xxg2gtDS\nxYTmf0o0XmPeHE2eNF9zzTXcdNNNdOvWjUceeYQuXbrwu9/9rkUBB4EvZzQ3kljo8MWiWL2eDwT9\npNkubovRqzdKXR36mlVuh3NI2tb4E5aAzGhuLOrxpy+ZNjmjscjEMzC790TbsjmtJ3Pq5q9R9+zG\natsWs7f/TtSOWn4+0RNORLEsQnM+cTuawwrNj500R3y2CfBQnNFnoU/nQG2ty9E0T8MbSX81ATYW\ndU76Wzh6rsmk+eKLL6ZXr14ADBs2jKuvvppOnTq16GZBkFjw4NPyDLukBLNHT5Saat8MtU+cNAdw\neobDiJdo6F94c16zU54RlBnNjTklS7pXk+Z4E6CVIZMzDqCq1H4/1kOTncYNgQcsNfHhiVpzNKzU\n9uboOXXbVrTNX2MVFGIOGux2OK1md+xIdOhwlLq6xJsBv9B9OjmjsUR5zKwUJc0DBw7k/fffZ/Pm\nzYmtgLt27WrRzYKg4aTZv8lD1Eeb0ABU56Q5oOUZ0DAO0JObAevr0XbvwtY0rI7Be8Oc+H5Y5M1R\njFoGnzQD1F0yBTscJvzRv1G/3pSWezo/G40Azmc+WCSeNHt1XrOTWEZHj0lfeU6KRf04eq66Gn3l\ncmxNIxrfZOtH0VGjsXPz0FevShxINEeTSfOiRYt49NFHufzyyxNbAS+66KIWBet7hpGo7fTzY+po\nvEYv5JO6ZsWZ0xzQ8gyA6MhYLa0XmwGdxR9W5y5wlP0MfmL2H4CVX4C2ZTPqrp1uh/MNmVzTDLE6\nxPrzzkexbXJeeC4t99Q/j/1s9POJ2tEyRhyHVViE/tWGRKO7lzQ0Afq/NMORmNc8wz9Jc2jJFyim\niTF4KOTluR1Oy4XDRE4ZB8QXnTRTk0lz422Azj8zZ85s9o2CQN2xHcU0YwsGsrLcDqfFDKcZ0C8n\nzfsz4KR56DBsTUNbuxqqq90O5wBavCQpaE2ACZrWUB7zuffeSKoZOj2jsdorrwEg++Xnob4+tTer\nqYmdqKmqr0/UjpquEz05lkSEZ890N5ZDCC1wlpr4vwnQET3hRKz8AvR1az35RuVQgtQYmyjRmPlx\nsz+2yaS5oqKC++67j2nTplFWVsY999zjvy1+SdJQmuHPemaHMXgodnY2+ob1KPv3uR1OkxpOmoOb\nNJOTgzFwcKwhZ/lSt6M5QOLpSsDGzTUWHeXdkiV1R7ymuXMG1jTHGaNOxBg0BHXvXrLefyel9wot\nXYxiGBiDhkB+fkrv5RUNJRotq/NMFWXvXvS1a7BzcjCGe3eOdLOFQkTHTQAgPKP5iZsb9ACtlI/G\nmzHDs2bElkc1Q5NJ8z333EO/fv3Ys2cPubm5FBYWctttt7UsUp9TnXFzPk+aCYUSP4A8v1LbNFHK\nywGw23hzHFKyeLUZMHHSHOCk2fDoKEalsgK1qhI7Jyew02OOiqJQe8VVAOSkuCHQaXYyAjyf+WDR\nCacCEP5kVrOTiFQKLZgHxPsOwmGXo0kuX63UbjSCMQglS2afYzGP6Ya6b19s1GszNJk0b9myhcsu\nuwxN0wiHw9x2221s27atxcH6mebMqg1A8hD1+MQAh1JehmLbWEVtAtMEcjhebQZsqOMPaHkGjZoB\nly6Ozcr2COeU2ezUOfBTHJpS/70LsfILCC2Yh7ZqZcruEwrQidrRMns3SiJWLnc7nITQ/Ph85jHB\nKc1wRCbGR8/NnumpnzmHom1Yj1paitmxk++ftAOgKA2vfzNP+ptMmlVVpaqqCiX+A3vLli2oapMf\nFkhBekzdMJvW2yfNickZxcE/ZWvYDOitpFnb4pw0Bzdpttu2w+hzLEptLfqqFW6Hk5DJM5oPZucX\nUH9BrAk957lnU3QTOyOTZhSFyPhTAQjN9M4UjdD8+ElzAJNmq3sPjL79UKsqPVkW1pjeeJpMQN68\nR1o4r7nJ7PfGG2/k8ssvZ/v27dx0001cdNFF3HTTTS0K0u8aHlP7P3lwHj3qX3wOpulyNIenJGY0\nBz9pNvsPwCooRNv8taeaQ9Rt8dnkxwTghOEIvNggm0iaM3FG8yHUXnE1AFlvvIZSlfzeGnXTRtS9\ne7DatcPq1Tvp1/eyhpXa3kialcoK9OVLsXU98SQoaPxSohGk0gxH9JTx2KpKaOF8qKo66o9rMmme\nMGECzz77LA899BDnnXceb7/9NqeffnqrgvWrIIybc1gdO2F2645aVYm2bq3b4RxWw0lzgJsAHY27\n2D+Z5XIwcbbdUJbUtavLwaRW4unLZ95JmhMzmrsE+7U/WubAQUTGnIRaXUXWm68n/fp+XxPcGpFx\npwLxaRUe2FSnf7YAxbJi/Td+HnF2BJGJsaS5uSUC6Zb4vgjA5AyHXdwWY+RxKNEo4Xlzjvrjmkya\nq6qqePbZZ/nd737HM888w2uvvUZ9qkf+eFHj5CEAJ80A0ePjs4E9dLJ2sMRJc4Y0QUXiDTkhj2zn\nUvbuRamrwypqg11Q6HY4KdWw5MQ7zYAN5Rly0uyocxoC//os2HZSr52RpRlxdvv2RIcMQ6mvj52+\nuSxRmjE2OPOZ/3979x0eVZk9cPx7p6VTg0F6ld5L6AgqxbKAih1sq7urPxQVFKSIIujqWoHd1bUg\ngiBKUVApShUQlRKkF+lFKaGkTbvv74/JTAgkTMrM3GTmfJ6HR5OZuffNuZPJmXfOe95LOTt2RkVH\nY92yGe3PP40eTp60c2cx79yBstlwNW9p9HACytHdU6JRmDctfpPmYcOG4XK5mDhxIuPGjSM1NZXR\no0cXfZSllJZ6Bi0jA71M2bBJHly+mbX1Bo8kf96ZZj2MezRfzJk922NbtaJErGI3Hw3zHs0XcTdq\njIqNw3zwAFoJ2fXUu2OVO4J7NF/KftNf0BMTsezYhuXnwL52eft0R8JOgHnxlWh8v8TgkYBtnXcR\nYEeDRxJEMTG+NwVF6RkcCpYNv6Ip5UmYS/H+FHnxzvQXpq7Zb9J85MgRRo0aRePGjWnatCljx45l\n+/btRR5kaRWOH1E7OmWXAny/pMTWNXtnmiOl3Za7/jW4K1+N6dQpzDuM/z0zHckuSSrF28YXmMWC\no5PnD1jUwvkGD8bDkt0lwl2nrsEjKUGiosi6934gwAsC09OxbN9a6rcJLg77zX8BIObTqcbujpmZ\niWXTBpSm4WzfwbhxhEBJr2sO509fXK3boCeUwbJ3T4HXEflNmqtXr86mTZt8X+/Zs4caNcJ7QVBe\nfMlDGM24uZs0xV2rNqZTJ7H+tNbo4eQp0maa0TSc2avYbatXGDoUiKyZZgD7wLsAiJ71mcEjAe2P\nPzAfOogen4C7YSOjh1OiZA56AKVpRC2Yj3bqVECOGTbbBBeDq0077H1vRstIJ/a1iYaNw7ppA5rD\ngbtx07CfMHH0vAHInmkugZNX4bgI0MdqxdmlG1DwBbB+k+Zjx45xzz33cMstt9CvXz8GDBjAb7/9\nRq9evejdu3fxBlyKmHzJQxjNuGka9r8MACDq63kGDyZv3t0Aw/2F82K+1k8lYEtbU3bHmHBY/FoQ\n9j43oZcpizVlk+Ez/d7aalerNmHfo7yw9Bo1cVzfC83hIPqzTwNyzJy2WuHZqaGg0se+iLJYiJ4x\nzbDfAe8kTliXZmRz16vvWZR/5gyWLZuNHk5ubjeWDZ62tOGwfXZevK3nrAUs0fCbNE+aNInFixcz\nZcoUJk2axLfffstnn33GBx98wP/+97/ijbYUMXtnmsNkEaCX/S/9AYha+HWJfJdrOhNhM82QM9O8\ndg04HIaOxfu8D5fFr37FxGDvdysA0Z8bO9uc87FoW0PHUVJlPehpPxcz7eOAvHaF88fQheGuW5+s\n+x9C03XiXhpjyBis2fXMjjBeBOijaTm1tSWsRMO8ayemtAu4q9cI27aX3k1ObKuWF+h1xG/SfPXV\nV7N//35SUlJy/atRo0ZElWmYjnlrmsMreXA1a4G7Zi1MJ//0bVlakmhnzwKRNdOsV74aV4OGaBnp\nhm9zntOjOTJmmgGy7roHgKgvPweXy7Bx5NpQQFzG0eN63DVqYj50ANvyYiYbkbqpST7SnxmBnlCG\nqB+WFngGLmBcLl/bR2dy+G1qkpeSWtecU5oRvr8Teq3anjLVs2exbN7o9/5+k+a//e1vvP/++6xa\ntcr3b/Xq1QEZbGmSM+MWZslDCS/R8NU0R0Kf5ov4SjQM3mggZ0OfMHveX4GrbXtcdepi/vMPDFvR\n7nR6tvQGnK0ju1wgX2YzmYMfBCB66ofFO9T+fZhOn0ZPrIRes1YABle6qcREMp58BoD4caND+imk\n5bcUtIx0XHXroZKSQnZeIzm7dkNZLFg2/OIrSSwJwrqe+SKF2R3Qb9J88uRJZsyYweuvv+7799pr\nrxV7kKVNzsYm4TXTDDklGrYSWKKhnYmcbbQv5uyW3frJwLpm06GDmE6dQk8og35VZPzxAjxvJO+6\nF4Coz2caMgTL9q1omZm46tRFVaxoyBhKg6y7B6FsNmxLF2M6dLDIx7H8ErmbmuQn85G/465WHcv2\nrUTNDt3vgXWdt545MmaZAVRCGZztO6DpOtaSsrEVkfNpl+Pa7BKNQCTNycnJrF9fcvv4hoTTienE\ncZTJFJZ1Pa7mLXHXqIX5zz9KRFN7H6cTU9oFlMmEKlPW6NGElLNTZ5TZjGXjr2gXzhsyBu+uhM4u\n3SJuIVrWwLs83Rm+W2jIzI+vX3CYbh8cKKpSJew390NTiphPpxb5ONbseEtpxkViYkh/fiwAca+M\nh/T0kJzW+pO3P3PkJM1wUW1tCSnR0E6dwvL7PlRsLK7GTY0eTlA5u3bz/L399We08+eueN8CtZy7\n//77adKkCU2bNvX9N5KYjh9DU8qTMFutRg8n8DQN+y39ALAtKBn9aeHiHs3lwOT3qRpWVEIZXK3a\noLndvkUxoebdldDRrbsh5zeSXrUazq7XojkcRM2fG/LzS31twWU+4FkQGD1jWpEXznrjHa4dAorK\nfutAnC1aYT5xnNj/Tg7+CXU9p3NGJCwCvIjz4rrmAO90WRS+16BWbcIz77mISiiDq217z9/bH69c\nfuw3E/nkk09YunQpW7ZsISUlxfffSJKzsUn4lWZ45eqiUQJ2ogMwZc/wRVo9s5ehred0PWemObtU\nJNJk3Xk3YEwXDW+7OUma/XMld8DVqAmmUyeJ+ubrQj9eS7uAecc2lMWCs0WrIIywFDOZSB/3MgCx\nk94O+k6Z5l07MZ09i7tKVfTqkdNoAMDVpBl6paswHz+GeecOo4eT80YyQl6Dcuqar7yOxW/SfNVV\nV1GpUiXMZnOuf5HE16s2jNtuuVq29qxEP3E84FvTFpWvnjmCOmdczLelrQGLAc07tmM6dQr31VVw\n16sf8vOXBPYbb0GPT8C64RfMe/eE7LzayZOYD+xHxcbhbtQ4ZOcttTSNzAceBiD648LvEGjZtBFN\n1z2bmsTGBnp0pZ6zc1fsfW5Ey0gnLsgbnljXXVSaEWm15SZTiSrRsERA54yLFXQxYIFazt18882M\nGDGCMWPG+P5FkpyZ5jDuIKBp2G/2lGhEfR36j6Pz4ptpjqAezRdztmmHHp+AZddOTL/vC+m5vQsQ\nnd2ujbw/Xl5xcb5PYEI52+xtM+hs1RoslpCdtzSzD7wTPS4e209rC70hh+9j6AhJDooifex4lNlM\n9IxPgjoL6qtnjrDSDC9f67nvFxs7EKcTa3b7NWebyPi9cLVsjV6uHOYD+694P79Jc6dOnfjb3/5G\nmzZtaN68ue9fJAnHLbTzYh9wGwDRc78Au93g0YCWGtkzzdhsOHr3BSAqxLXmOfXM14b0vCWNr4vG\n7Jkh6yzj+1hUFgEWmIpPwD7wTgBiPilc+7lI6RBQHO56IdjwRCmsP3n2Coi0RYBejh7XoWJisK1Z\n7XteGsGy7TdP95669SKne4/ZjKMApYh+k+aBAwfSpUsXKleuzIABA+jSpQsDBw4MyBhLC98W2mHe\nq9bVvCXOps0xnTlD1LcLjB4OptTInmkGLuqhHcKk2eHA5v2YNMKTZmdyR9w1a2E+fixkraAssgiw\nSLwLAqNmz4K0tII9SCmpHy+g9GEj0eMTiPp+SVD6x5sO7Md84jh6xYq4r2kQ8OOXBqp8BTIffQyA\nuJfGGrYg0Nuf2RXm/Zkv5cwu0bgSv0nzokWLeOSRR3jxxRc5d+4ct912GwsXLgzIAEsLb3mGu0pV\ng0cSZJpG1qAHAIguRvumQDFF+kwznpkHPT4B628pmPb/HpJzWjf8gpaRgathI/SkyiE5Z4mlaWTd\n6dkhMHrWjOCfz+XCusn7sajMNBeGu3ETnMkdMaVdIHrO7AI9xvz7XkxnzuC+KiniFp4VlkpMJGNo\n8DY88XXNSI7AeuaLZAwZil6hAraf1mJbssiQMfjeuEdY0uwIRNL8/vvvM2vWLOLj46lYsSLz5s3j\nv//9b0AGWCoohemwd6Y5vMszAOy3DUTFxmL7cRXmfaFb/JQXb8u5SO2eAUB0NI5efQCIWvBVSE7p\nnUWK9NIMr6w7PF00or5b6LeHZ3GZd2xHy0jHXbMWqlKloJ4rHHkXBMZ8/EGBZum8m5q4ZFOTAsl8\n5B+4q1bDsu03or6YFdBj+z7d6hiZpRleqkxZMp4aDkDcyy+AyxXyMVh/icxPu/Rq1XF06nLF+/hN\nmjVNIz4+3vd1UlISWgS9uGjnz2FKT0PFxkXEjKcqU5asfrcCED19mqFj8c00R9hugJfylWiEqK7Z\ntwiw67UhOV9Jp9eoiaNzV7TMzKCXyfhKBWSWuUjsN/dDT0zEsn2rLyG+kkhNDors4g1PJr4EGRkB\nO7RvpjlC65kvlvnAX3HXqIll186Qt7w0HT+G+chh9IQyuBs0DOm5S4Jzc65cmuo3aa5Xrx4zZ87E\n5XKxe/duxo0bxzXXXBOwAZZ0vkWA1apFzEyEr0Tj8xlF3iwgELQI79Ps5ehxHXpcPNaUTZj8rOwt\nLu38OSybNqDMZpydInMFe158JRpB/gMmnRyKKSqKrHsGAxAz1X/7OdlEpvDst92Bs3nLgG54Yjp+\nDPOB/ejxCZ7Wf5EuKor0kZ4Fl7H/nBDQNyf++BbGtmkbcZuKAX53v/UbkbFjx3Lo0CEsFgvPPPMM\nNpuNF198MWDjK+nM3kWAYd4542KuNu2yNws4hW3RN4aNw3RGZpoBiInB0as3EPwSDevaNWhuN67W\nbVEJZYJ6rtLEfnM/VGwc1vXrgtr+z5I90yydHIouc/CDni3Qv56Hdvp0vvfTLpzHvHM7ymLB1aJl\nCEdYyl284cm7bwVkwxPvLLOrfbK0WcxmH3C7781JzAehK4m1/uztzxxZ9cwFlW/SPG/ePADi4uJ4\n7rnnmD9/PgsWLOD555/PVa4R7nJmmsO7c0Yumkbm4AcAiJk21bhhyEyzj/0Wb4nGvKCex7p6BSD1\nzJeJj8d+818AiJ49Myin0M6cxrJvLyomBlfjpkE5RyTQa9TEcd0NaA4H0Z99mu/9LBs3oCmFq1lz\niIkJ4QhLP2eXbth79/VsePL6K8U+npRm5MFkIn2MZ4Iy9t230M7k/wYwkKy/StJ8JfkmzdOmGVvP\nWlJEwhbaebHfdgcqOhrbquUh69pwKalpzuG47gbPTOfmTZgOHgjaeXz1zN0jc+vsK8nK7tkcPXtm\nULaa99Uzt2gFVmvAjx9Jsh70tJ+LmfZRvtdKSjOKJ33MS54NT6ZPxbxrZ7GO5U2aHR2kJOxizu49\ncFzbE9P5c8S+9a/gnzArC8uWFJSmecozxGUisGClcLw9msN9Y5NLqXLlfQvQYmYY8AYqKwstIwNl\nsaDiE0J//pImJga7t0Rj4ddBOYXpxHEsu3aiYuNwtpYXzEs5O3XBXb0G5iOHsa79MeDH95VmyCLA\nYnP0vMFzrQ4ewLrihzzv46vdlBm1InFf04CsQQ8Ue8MT7cxpLDu2o6KicLVqHcARhgfvbHPMx//D\ndOhgUM9lSdmM5nTibthYyvPykW/SvGfPHq677rrL/vXs2ZPrrrsulGM0lPnoUSD8NzbJS9a9ngU1\nUfPnhrzJuncLbVWufMQswPTHV6IxZzY4nQE/vjV7ltnRqTPYbAE/fqlnMpE18C4gOD2brb/IJhsB\nYzaTOfhBILv93KV0XTY1CYD04c97NjxZutj3+lFY1vU/AdkdY6KiAji68OBq1oKs2+5AcziIe2V8\nUM/l3dRESjPyl2/SXLNmTaZNm3bZv08//TSiSjdMRyNjC+28ONt3QE9MxHzoAOadO0J6bk12A7yM\n47ob0CtWxLp1CwmPPRLw/p2+0gypZ86Xr2fzwq8LvutcQbjdWDZtAMDVVmaaAyHrnsEoqxXb0sWY\njhzOdZt5315MZ8/irnx1xJXeBZKqVImMJ58GIG7c6CKVLeXUM3cM6NjCSfrIMSibjeg5s7H8lhK0\n8+SULMlrUH7yTZqtVitVq1bN919EcLkwHT+G0jT0q6sYPZrQM5ux35C9scbib0N6atkNMA+xsZyb\nPhs9oQzRX80l4bG/Bi5xVipnpln6M+dLr1MXZ3JHtIx0ohYGrpOJeddOTGkXcFevIbswBoiqVAn7\nLf3QdJ3oTz/OdZtvm2DZ1KTYMh99DHeVqli3binShifWn7I3NZF65nzpNWqS+eAjQPb22sGgVM7v\nRXuZac5Pvklz69ZSW2T64wSa241+VVLEfmzk6HMTQMhbz8lMc95cbdpx7vO56PEJRM+fS8LjgZlx\nNu/ZjfnEcfTESrgbNQ7ASMNXMHo255QKyAxPIGU9kL0g8NNPcvWct8giwMC5eMOTV8YXrqdwWppn\n4ZnZLNfCj4ynhqGXKYtt5XKsK5YF/PimQwcxnfwTvWJF3LXrBvz44SLfpHns2CC9mylFvO3mImH7\n7Pw4ul2Lio7GunEDpj9OhOy8MtOcP1fb9jmJ87w5xA8fWuxjWld5t87uHpkN7QvB/pf+qJgYbGtW\nB2xhTs6GApI0B5IzuSOuRo0xnTpJ1Lc5O31J54zAst9+J85mLTAfO0rs+/8u8OOsv/7s6QvfvAVE\nUCvbolAVKpLxxFMAxI1/IeAdfHz1zPLpyxXJX8crMPs6Z0TeIkCfuDhfz17bkkUhO61vpll6NOfJ\n1S6Zc7Pmomw2YmZMu+ImDgVhW7USAGc3aTXnjypTFnvfm4HA9WyWJC5INI3M+x8GIHrqh55vnT+H\neddOlNXqSdZE8V204UnMO2+i/flngR4mpRmFk/nIP3BfXQXrbylEzfsyoMeWRYAFI0nzFZizrY4J\nYAAAIABJREFUZ5EifaGIESUa0qPZP1f7ZJztOwBgXbemGAdyYV2zGpBNTQrK17P588+K3VlGO5uK\nZc9uT8utps0DMTxxEfvAO1GxcdjW/oh55w4sG371bGrSvAVERxs9vLDh7Node68+mNLTCrzhiXVd\n9iLAjpI0F0hMDBnPPg9kl8LY7QE7tOXX7JaXkjRfkSTNV2A6sB8Ad63aBo/EWN7FgLZVKyA9PSTn\nlN0AC8b7x8a6ruh9gy2bN2K6cB5XnboR2VqxKJxdu+O+uoqnD/D6dcU6lmXjrwC4mreUVn9BoBLK\n+FoFxnzyoczqB1H62PEF3/DEbsea/dx3JncIwejCQ9ad9+Bq0BDzoYPETM2jnWJRpKVh2fYbymLx\nbK4k8hX0pDklJYVBgwYBsGPHDrp168bgwYMZPHgw3333XbBPXyxmSZoBUElJONu0RbPbsa1cHpJz\nms54ZpplIeCVOTt3BcC2puhJs7SaKwKzGbu3/VwxFwRas2d4nFLPHDSZD3hKNKJmz/I9312SNAec\n+5oGZN33AJrbTdz4K6+LsmzaiGa342rUGCWTIwVnsZA+Ont77bdeRzt/rtiHtG7agKbruJo2g9jY\nYh8vnAU1af7ggw8YPXo0zuyNGLZu3cpDDz3k6/nct2/fYJ6+2CRpzmH3lmiEqPWcdvHmJiJfztZt\nUVFRWLZvRTtTtLpmX6s5qWcuFG8Xjaiv5hXrExjfzGc7SeKCxd2kKc52yZgunPd9MiC1m8GRPnwk\nelw8UUsWYV29Mt/72Xz1zJ1CNbSw4ejVB0eHTpjOnCF20tvFPl7Oa5D8TvgT1KS5Zs2aTJkyxff1\ntm3bWLFiBffddx+jRo0iozCtaUItK8vTo9lslo+sAUfvGwGIWroI3O6gn89b0yzlGX5ER/tmKK0/\nFaFMID0d6y/rUZqGs0vXAA8uvLnr1fckYmkXiPnw/aIdRNexbPRuaiJJczBlPvhX3/+7q1RFrxIh\n+w2EmLrqKjILsOGJdx2G1DMXgaaRPvYlAGLe/zem48eKdTjLxX3LxRUFNWm+4YYbMJvNvq9btGjB\ns88+y/Tp06levTqTJk0K5umLxXzoIJpSnoTZajV6OIZzN2iIu1ZtTKdO+RYMBJO3e4YsBPTP2akL\nANa1qwv9WOv6tWhOJ64WLWVWvwjSh48EIPbdN4s002/esxvT+XOeJC4SN1AKIfst/dErVgSknjnY\nMrwbnvyWQtSXn19+B5cLyy/Zs5sy01wkrrbtsd/cDy0zk9gCLrzMk67LTHNhqCA7cuSIuvPOO5VS\nSp0/f973/b1796oHHnjA7+OdTlfQxnZFCxYoBUrdcIMx5y+JnnrKE5Phw4N7Hl1XKirKc6709OCe\nKxwsX+6JVYsWhX/sM894HjtiRMCHFTFuuMETw6efLvxjP/jA89iBAwM/LnG5ceM88f7oI6NHEv4+\n+cQT62rVlMrIyH3br796bqtb15ixhYtdu5Qym5UymZTatq1ox9ixw3MtqlTx/O0VV2QJZYL+8MMP\nM2bMGJo1a8a6deto0qSJ38ekphpTwhGTso14ILNqDdJOXjBkDPmpVCmBkwaMydqjN+Xeegv94485\n/djTEBcXnBNlZFDJbkdFRXEqzQXpEv8rqtOYRJsNtmzh9O6DhVpUU27xUqzA2badcJakn+kKSlr8\nLSPGUn7pUtTkyZy550H0GjUL/Nj4FauJAdKatiKzBP1MV1LS4l8ofx+Kpet1ntZ+pfVnoJRcg979\nKNe0OdatW0ib8E8yn3zGd1PMt0s8f1/bdyxxf18LosTEv/zVxA96gJipH2J/ZjjnpxV+G/PoxctI\nAOxt2nP+VFrgxxgEwY5/pUoJ+d4W0pZz48aNY+LEiQwePJhNmzbxj3/8I5SnL5ScdnN1DB5JyeFM\n7oizdRtMp04R88lHQTtPrnpm2ZnIv5gYnG3aoSlVqLpm7dQprFu3oKKjff2eReG5mrUg67Y70BwO\n4l59uVCPzWl/Jp0zQsJkwtWshbyuhILJRPqLEwCIfedNtJMnfTdJf+bASX9mBCo2jqhF32IpwroW\niyxELpSgJ81Vq1Zl1izPu5/GjRszc+ZMpk2bxhtvvEFcsGYqA8C8/3dAOmfkomlkDBsBQOzktyFI\nCzmlnrnwilLXbPsxexfA9h1lk4diSh85BmWzETVnNubfthToMbl2pmsmO9OJ8OPs2h37Db0xpV0g\n7l/ZdbdKYV2fnTQndzRwdOFBJSWR8Y//AyD+xdGF3mwp1/bZwi/Z3CQf0m4ub47reuFs2QrTqZNB\nm22WzhmFl5M0F3xnwJxWc9cGYUSRRa9Rk8wH/4qmFPF++tN6WTZukJ3pRNhLHzseZTIRPe1jzHt2\nY969C9OZM7grX40uf18DIvPxJ9ATK2Hd8Au2bxYU+HHa2VQsu3Z6diOVN+4FIklzXtxu3xba7pq1\njB1LSaNpZHg7BgRptll6NBees007lM2GZesWX/yuSCnfRjXO7tcGd3ARImPocPSEMthWLMNagE2A\nrBtkUxMR/twNGuZsePLSmItazXWSMpkAUfEJpD/zHABxE8ZB9t4Y/vh2I23RCqKigjW8sCJJcx5M\nx46iOZ24r0oK3mK3UsxxfW/PbPPJP4mZFvjZZtkNsAhiY3G2blvgumbTgf2YDx9CL1/esyhKFJuq\nWJGMJ54CIG78C/n2p/Xy1hJKb1QR7nwbniz+jpj3/w2AM1lazQVS1uAHcdWug2XfXqJnTCvQY6w/\nS2lGYUnSnAdvaYZ8dJSPi2qbYya/A5mZgT28zDQXSU6Jhv8ttX1bZ3fuBhf1UhfFk/nIP3BfXQXr\nls1EzZ+T/x2Vyplplj9YIsyppCQyhwwFwLJ3DyCLAAPOaiV91AsAxL3+CqT574Rh/UX6MxeWJM15\nkHpm/xw39MHZohXmP/8g5tOPA3ps30yz1DQXSlGSZqlnDrDYWDKefR6AuInjwW7P827mfXsxnT2L\nO6kyetVqoRyhEIbI+Pv/4c7ewEcvXx53g4YGjyj8OG7p7+lwdfJPYt+bcuU7u92+8gx5415wJT5p\njhs3GtMfJ0J6TkmaC0DTyHj6WQCiP/6g0Ct2r3jos9I9oyicbdujoqOxbtmMedvW/O+o61izO2dI\n0hx4WXfeg6tBQ8yHDhDzyYd53idXaYbUdYpIEBvrmwl19LgOTCU+/Sh9NI30Mdnba09+J1ebv0uZ\nd2zHlJ6Gu0YtVFJSqEZY6pX4Z23sv98l7uVxIT2nL2muLT2ar8RxQ2/0ihWx7NuLecf2gB1XumcU\nUWwsmYMeACDuzdfyvZtl6xZMqam4q9dAl+d44FkspI9+EYDYN19DO3/usrtYf5VFgCLy2O+4m9QF\nS0h79Q2jhxK2nJ27Yr++F6b0NOLe/Ge+9/O1mpP+zIVS4pNmgKiv56GdOxuy85mkR3PBWCzYb7wF\n8FyjQDFJn+YiyxzyFCoqiqgF8/N9I2NduQLInmWWWc6gcPTqgzO5I6YzZzx1/5fI2dRE/mCJyOJK\n7iDrVYIsffSLKE0j+pOPMP2+L8/7+F6DpJ65UEp80uzoei1aZiZRX84OzQmVuqg8Q2bh/LHf0h+A\nqAXzA1aioclMc5Hpla/2zTbHvpH3LINtVXarOSnNCB5NI22s52PS2PemYDpxPOemtAuYd25HWSy4\nWrQ0aoRCiDDlbtwE+533oLlcxL0yPs/7eGeaXTLTXCglPmnOGvwAADHTPg5o3Wx+tNOnMaVdQE8o\ng5KWZ345u3RDr1ABy57dmHfuCMgxZaa5eDKHPOXZnW7B/MuvSVYW1vWelnSOLt0NGF3kcLVLxn7T\nX9AyM4l9/RXf9y2bNqLpOq6mzSAmxsARCiHCVfpzo1DR0UR/NRfLpg25btNOnsR8YD8qNg5XoyYG\njbB0KvFJs73vzeiJiVh2bPOt9Awm84GLSjPko2v/Al2ioVTOTLN8hFck+tVVyLrvfjSliL2kps36\ny3q0rCxcTZqhKlUyaISRI33UCyizmegZ0zDv3gUgreaEEEGnV61G5l//DkDcS2NzTTr6SjPatAWL\nxZDxlVYlPmnGZiPrznsBiP50atBPJz2aC89XorHwq2IfS0tPQ3O5ULGxsrVwMWQ88bRntvmreZh3\n7fR939dqrqvMMoeCu159z25ouu5b0OzrnCGLAIUQQZTxxFPo5cphW7Ma27Klvu/7FgG2ldegwir5\nSTOQdd9gAKLnz0G7cD6o55J2c4Xn7NINvXx5LLt25krQikKTHs0BoVepSta9g9GUImHI34gfOYz4\nkcOImuNZGyBbZ4dO+rARqNhYohZ9g+WndTLTLIQICVWuPBlDhwMQ99IL4HYDF71xl0WAhVYqkmZ3\n3fo4unRDy8ggas4XQT2XJM1FYLVi73szUPwSDZPsBhgwGU88jYqKwrp5EzEfvk/Mh+9jPnIYFRuL\nQ7awDRmVlETG3/8PgIQn/4Hp9Gn0xEroNWoaPDIhRLjLfOgR3NWqY9mxjagvZoHDgXXzRkBaXhZF\nqUiaAbLuux+A6CAvCJQezUVj/8tFXTSKwTfTLIswi02vWo2zcxdyYeJruf6dnbMA4uONHl5Eyfy/\nJz1rM7LbWTplUxMhRChER5P+3CgA4v45AeuGXzzrWupfg5JPdAut1FSA22+8Bb1CBaxbt2BJ2YSr\nZeugnMckM81F4ux6LXq5clh27sC8exfuaxoU6Tgy0xxYrnbJ8hFcCaDiE0h/5jkSRno+KpVaQiFE\nqNhvvxPXfyZj2b6V+GeeAKQ/c1GVmplmoqPJuuMeAGLffD0450hLw/znHyibDf3qKsE5R7gKUImG\n1DSLcJU16EFc2Z9gOTt1MXg0QoiIYTaTNtazS6ll7x4AXLKmokhKT9IMZD7+BCo2jqhF32D9cVXA\nj28+eAAAd42aYDYH/PjhzuEt0Zg/p8glNL6ZZunRLMKNzca5L7/m3LRZ8gdLCBFSzh7X5+qaJDPN\nRVOqkmY9qTIZTz4NQPyYkb6VoIEiiwCLx9Gth6duc/cuLL+lFOkYshugCGd69Ro4+txo9DCEEJFG\n00jP3qVUT6yEu/41Bg+odCpVSTNAxt//z7MSdNtvRM+cHtBjm/d5PraQRYBFZLWS1f82AKK++LxI\nh/DuBqjLTLMQQggRMK4WrTg7ZwHnPp8LplKX/pUIpS9qMTGkj/HU5sS9Mj6gfZutmzxtWFzNWgTs\nmJHGfvudAETP/QJcrkI/3jvTLKt6hRBCiMBydu0uOU4xlL6kGbD3vw1n2/aYTv5J7DtvBuy43v3Z\nXa3bBuyYkcbVqg2uuvUwnfwT66rlhX68b6ZZumcIIYQQogQplUkzmkba+FcAiHlvCqbsBXzFYfrj\nBOZjR9HjE3DXq1/s40UsTcM+8C4Aoi8t0dB1ombPvOKugb6ZZunTLIQQQogSpHQmzYCrTTuybrsD\nzW4n5qP/Fft4Fm9pRstWUutTTFm33QFA1HcLIS3N9/3Yf71Kmf/7G2UG3wW6nudjvd0zZKZZCCGE\nECVJqc4Os+4ZBIBtZeHLAC5l2ZxdmtGqTbGPFen0mrVwJnf0bHv+zdcA2BZ9S9y/XgXAsv93bEsW\n5fFAHS1VWs4JIYQQouQp1Umzs10yKjoay/ataCdPFutY1o2epNkpSXNAZHkXBH75OeY9u0l47BEA\nnC1aAZ6ymktpF86j6Tp6fAJYraEbrBBCCCGEH6U6aSY6GmdyRwBsP64s+nGUwrI5uzyjVXC25440\n9n4DUDYb1tUrKXvvQExpF7Df0p9zc75Gj0/Atmb1Zb2cvbsBSj2zEEIIIUqa0p0049lQA8C6akWR\nj2Ha/zums2dxX5WEXqVqgEYW2VS58jiu742m65gP7MfVsBHn3/k3qkxZsu71lNXE/Df3bHPUgq8A\n0CtdFfLxCiGEEEJcSalPmp3drwWy65qLuHWz1dtqrlVr0LRADS3iZd1xNwB6mbKcm/oZxMcDkPnX\nv6NMJqLmz8H0xwkArCuXEzfR038744mnjRmwEEIIIUQ+Sn3S7GraHL18ecxHDmPa/3uRjpFTmiH1\nzIHk6HsTF159g3NzF6DXqev7vl6zFo4bb0FzOon+6H1MBw9Q5tEH0HSd9KeH4+h7k4GjFkIIIYS4\nXKlPmjGZcHS9FgBbEUs0ZBFgkGgaWQ89gqt5y8tuyvjb4wDETP2QMg/ehyk1Ffv1vcgY/nyoRymE\nEEII4VfpT5oBZ7drAbCtLsJiQKcTy9YtQHaPZhESrvbJOFu1xpSainXrFly163DhPx+A2Wz00IQQ\nQgghLhMWSbOja3cArD+uBLe7UI8179yBlpmJq3YdVHnp2hAymkbm3/8PABUbx/mpn6HKljN4UEII\nIYQQebMYPYBA0GvVxl2jJuZDB7Fs3YKrRcFnjHMWAUppRqjZ+91K2h8ncLZqi7tRY6OHI4QQQgiR\nr7CYaUbTcGSXaFhXrijUQ6U/s4FMJjL//n+4kjsYPRIhhBBCiCsKj6SZi+qaC7kYMGcRYNsAj0gI\nIYQQQoSLsEmaHV2y65p/XgdZWQV7UHo65l07UGYzrqbNgjg6IYQQQghRmoVN0qwSE3E2bY6WlYX1\nl/UFeozlty1objeuRk0gNjbIIxRCCCGEEKVV2CTNcFGJxoplBbq/N7mWemYhhBBCCHElYZU0O67t\nCYBt2fcFun/Uku88j+veI2hjEkIIIYQQpV9YJc3Ojp1RsbFYtv2G6cTxK95XO3UKyy/rUVYrzh7X\nhWiEQgghhBCiNAqrpJmoKBxdugFgXf7DFe9q+34xmq7j7NINlVAmFKMTQgghhBClVNCT5pSUFAYN\nGgTAoUOHuOeee7jvvvt48cUXg3I+R88bALD9sPSK94ta9C0A9t43BmUcQgghhBAifAQ1af7ggw8Y\nPXo0TqcTgFdeeYWnn36a6dOno+s6339fsNrjwnD0vB4A28rl4HLlfaesLGwrPDPRjt59Az4GIYQQ\nQggRXoKaNNesWZMpU6b4vt62bRtt23o2EenWrRvr1q0L+Dn1WrVx1a2H6dxZLBt+zfM+ttUr0DIy\ncDZviV61WsDHIIQQQgghwktQk+YbbrgBs9ns+1op5fv/uLg4Lly4EJTzOq7LLtFYtiTP222Lsrtm\nyCyzEEIIIYQoAEsoT2Yy5eTo6enplCnjfwFe+fKxWCxmv/fL5dZ+8P5/iFu5jLg3X899m67D94sA\niLvnDuIqJRTu2CVEpVI67nAh8TeWxN9YEn/jyTUwlsTfWEbFP6RJc+PGjfnll19o164dq1atokOH\nDn4fk5qaUfgTNWpFYnQ02saNnNq6F5WU5LvJsvFXyh8/jrtqNc5UqQMngzPbHUyVKiVwshSOO1xI\n/I0l8TeWxN94cg2MJfE3VrDjf6WEPKQt55577jneffdd7rrrLlwuF3369AnOiWJicHTuCoB3wZ+X\nbbGna4ajd1/QtOCcXwghhBBChJWgzzRXrVqVWbNmAVCrVi0+/fTTYJ8S8NQ1R/2wFNuypdjvvMf3\n/ajsemZ7n5tCMg4hhBBCCFH6hdfmJhfx9WtesQzcbgDM+/Zg2bENPaEMzk5dDBydEEIIIYQoTUJa\n0xxKep26uGvVxnxgPwlPPoZ5904sW1IAcFx3PdhsBo9QCCGEEEKUFmE70ww5reeiZ8/EunkTmEw4\n23cgY8jTBo9MCCGEEEKUJmE70wyQ8dgTaKdOodeqjaNTF5ztO0BcnNHDEkIIIYQQpUxYJ8169Rpc\n+N9Uo4chhBBCCCFKubAuzxBCCCGEECIQJGkWQgghhBDCD0mahRBCCCGE8EOSZiGEEEIIIfyQpFkI\nIYQQQgg/JGkWQgghhBDCD0mahRBCCCGE8EOSZiGEEEIIIfyQpFkIIYQQQgg/JGkWQgghhBDCD0ma\nhRBCCCGE8EOSZiGEEEIIIfyQpFkIIYQQQgg/JGkWQgghhBDCD0mahRBCCCGE8EOSZiGEEEIIIfyQ\npFkIIYQQQgg/JGkWQgghhBDCD0mahRBCCCGE8EOSZiGEEEIIIfyQpFkIIYQQQgg/JGkWQgghhBDC\nD0mahRBCCCGE8EOSZiGEEEIIIfyQpFkIIYQQQgg/JGkWQgghhBDCD0mahRBCCCGE8EOSZiGEEEII\nIfyQpFkIIYQQQgg/JGkWQgghhBDCD0mahRBCCCGE8EOSZiGEEEIIIfyQpFkIIYQQQgg/JGkWQggh\nhBDCD0mahRBCCCGE8EOSZiGEEEIIIfyQpFkIIYQQQgg/JGkWQgghhBDCD4sRJ7311luJj48HoFq1\nakycONGIYQghhBBCCFEgIU+aHQ4HANOmTQv1qYUQQgghhCiSkJdn7Ny5k4yMDB5++GEeeOABUlJS\nQj0EIYQQQgghCiXkM83R0dE8/PDDDBw4kAMHDvDII4+wePFiTCYprxZCCCGEECWTppRSoTyhw+FA\nKUVUVBQAAwcOZPLkySQlJYVyGEIIIYQQQhRYyKd358yZw6uvvgrAH3/8QXp6OpUqVQr1MIQQQggh\nhCiwkM80O51ORo4cybFjxzCZTAwbNoyWLVuGcghCCCGEEEIUSsiTZiGEEEIIIUobWX0nhBBCCCGE\nH5I0CyGEEEII4YckzUIIIYQQQvghSXMJtWvXLqOHENEk/saTa2Asib+xJP7GkvgbryReA/O4cePG\nGT0IkePbb7/l2Wef5ejRo1gsFmrVqmX0kCKKxN94cg2MJfE3lsTfWBJ/45XkaxDyHQFF/v78809W\nr17N9OnTOXz4MBcuXMDtdmM2m40eWkSQ+BtProGxJP7GkvgbS+JvvJJ+DWSm2WCZmZlcuHCBmJgY\nLly4wMyZM8nKyuKjjz7i+PHjfP/993Tq1AmbzWb0UMOSxN94cg2MJfE3lsTfWBJ/45WmayBJs8FG\njBiBw+Ggfv36OJ1Ozpw5w8GDB/nvf/9Ljx49WLhwIbGxsdStW9fooYYlib/x5BoYS+JvLIm/sST+\nxitN10AWAhpE13UOHTrEunXrWL9+PYcPH6Z8+fKULVuWffv2sWfPHsxmM8nJyaxevdro4YYdib/x\n5BoYS+JvLIm/sST+xiuN10BmmkPo999/Z/fu3SQmJmK1Wtm7dy+NGzcmKyuLc+fO0aRJEypWrEhG\nRgaLFi2iQYMGzJ49m27dutGgQQOjh1/qSfyNJ9fAWBJ/Y0n8jSXxN15pvwaSNAeZrusopXjvvfeY\nOnUqZ86cYfny5dSqVYtatWrRokULYmJiWLZsGUlJSTRq1IgmTZpw4MABfvjhB1q2bMldd91l9I9R\nakn8jSfXwFgSf2NJ/I0l8TdeWF0DJUJi2LBhau/evUoppT7++GM1aNCgXLdPmjRJTZo0SR07dkwp\npZSu68rlcvlu13U9dIMNQxJ/48k1MJbE31gSf2NJ/I0XDtdAapqD5Mcff+Ttt99m1apVHD58mPj4\neFwuF0opHnjgATIzM/n6669997/lllvYsWMHJ0+eBEDTNMxmM7qu+74WBSfxN55cA2NJ/I0l8TeW\nxN944XgNpDwjwHRdZ+rUqXz55Ze0atWKadOm0aFDB1JSUtB1nYYNG2I2m6lQoQJLliyhT58+AJQr\nV45WrVpRr169XMcrCU+S0kTibzy5BsaS+BtL4m8sib/xwvkayExzgLlcLlauXMkrr7zC3XffTdu2\nbUlJSeHBBx9k+fLl7N69G/A8ORo2bAjgexdVpUoVw8YdLiT+xpNrEHpKKd//S/yNJfE3lsTfeOF8\nDWRHwACz2Wzccsstvt1rNE3DarVSr1492rVrx9y5c1m4cCGbNm2ib9++AJhM8t4lEJRSEn+DyTUw\nhncmRtd1ib+B5PlvLIm/8cL+GhhSSR0mtm7dqhYvXqyUUrmK1b3Onz+vHnzwQbVv3z6llFKpqanq\nyJEj6r333lM7duwI6VjD0caNG9XYsWPVli1b8rxd4h9869evVzNnzvTF+FJyDYJr+/bt6pZbblEz\nZszI83aJf3ClpKSojRs3qvT0dKXU5QuVJP7BtWXLFrVlyxaVlpamlFLK7Xbnul3iH3wpKSkqJSVF\nZWZmKqXC/xpITXMxfP7550yZMoVBgwZhtVpRSuWqvdm7dy8ZGRl07tyZCRMmcOHCBTp27EibNm1I\nTEz0faRakup1SjqlFBkZGTz33HOkpKRw++2306pVq1y3e+Mp8Q8OpRRut5v//Oc/zJs3j2bNmnHk\nyBEaN26MpmlyDULgzJkz/POf/2TRokWkp6dz//33k5iYeNn9JP6Bp5TC4XDw6quv8tVXX3H69GnW\nrFlDmzZtiIqKynVfiX/gXRz/BQsWYLfbmTt3Lm3btiUuLg5d1+X1J8iUUjidTv71r38xf/58UlNT\nWbp0Ka1atSI2Njasr0EpmQ8vmTIyMkhISGDKlClA7rpCgIULFzJnzhyeffZZqlSpwh133OG7zZtY\nlJYnSknh/Zhn9+7dDBkyhDNnzvDJJ5+wYsWKy+4r8Q8OTdPQdZ3Dhw/z2muvYbVasdvtbNy48bL7\nyjUIPIfDwaxZs6hZsyYffvgh3bp1Y//+/XneV+IfeJqmkZGRwfHjx5kyZQrDhw/H7XaTkZFx2X0l\n/oGnaRppaWm++D/55JNUrVqVf/7zn77bvST+waFpGk6n03cNnn/+ecqVK8fLL7/su90r3K6B1DQX\n0KJFizCZTDRq1Ijq1auTmpqKUoovv/ySAQMGkJiYSNeuXalVqxZutxuz2UzFihVp164do0aNokKF\nCkDpfJKUBN7416tXjzp16tC3b1+GDh1K27Zt6dChA+PHjyc6OpoOHTrgcDiw2WwS/wBbtGgRZrOZ\nBg0aUKFCBWw2G3PnzuXMmTO0bduW5557jgkTJpCcnCzXIAgWLVqEpmm0bNmSxx57DPDE0m63U6tW\nLd/X3jc1JpNJ4h9A3tegxo0bYzabqVKlCkuWLMFisbBs2TJatGhBkyZNaNiwoTz/g+Di+GdkZBAX\nF4fT6QSgTZs2TJgwgW3bttGkSROcTidWq1XiH2A//vgjlStXpl69ehw4cICyZcty4cLZ3p0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "temp.plot(color='r', linewidth=2)\n", + "plt.ylabel('Temperature' + ' (%s)' % fm.units['temperature'])\n", + "plt.xlabel('Forecast Time ('+str(data.index.tz)+')') " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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RODd5y8z4LaVkzpeSigyjhN72FcUrb08tYzGswUNQLAtt29awo/FNZNmH6Rcn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temperaturewind_speedghidnidhitotal_clouds
2016-04-03 07:00:00-07:0013.9500123.2106.189110278.48742757.3125510.0
2016-04-03 08:00:00-07:0012.9500123.2334.661071651.73448182.8930920.0
2016-04-03 09:00:00-07:0012.2500003.2569.712283829.66830992.6837440.0
2016-04-03 10:00:00-07:0010.7500003.2768.177123921.22800997.3777130.0
2016-04-03 11:00:00-07:009.3500066.4909.195203968.97021699.7501940.0
2016-04-03 12:00:00-07:009.4500126.4980.540706989.349943100.7489990.0
2016-04-03 13:00:00-07:0010.7500006.8976.446778988.238362100.6947200.0
2016-04-03 14:00:00-07:0012.6499946.8897.257250965.33809199.5713460.0
2016-04-03 15:00:00-07:0015.5500186.8749.436252913.97999797.0132800.0
2016-04-03 16:00:00-07:0018.9500125.6545.990447816.02449091.9664050.0
2016-04-03 17:00:00-07:0022.1499944.0309.082267624.33726881.2893760.0
2016-04-03 18:00:00-07:0025.1499943.685.201516210.16672653.0658611.0
2016-04-03 19:00:00-07:0026.6499943.20.0000000.0000000.0000001.0
2016-04-03 20:00:00-07:0027.8500063.20.0000000.0000000.0000000.0
2016-04-03 21:00:00-07:0027.6499943.60.0000000.0000000.0000000.0
2016-04-03 22:00:00-07:0028.3500061.60.0000000.0000000.0000000.0
2016-04-03 23:00:00-07:0028.2500001.60.0000000.0000000.0000000.0
2016-04-04 00:00:00-07:0027.7500001.60.0000000.0000000.0000001.0
2016-04-04 01:00:00-07:0026.8500062.00.0000000.0000000.0000001.0
2016-04-04 02:00:00-07:0024.0500182.00.0000000.0000000.0000001.0
2016-04-04 03:00:00-07:0022.1499942.00.0000000.0000000.0000001.0
2016-04-04 04:00:00-07:0020.1499942.00.0000000.0000000.0000001.0
2016-04-04 05:00:00-07:0018.3500062.00.0000000.0000000.0000001.0
2016-04-04 06:00:00-07:0017.1499942.00.0000000.0000000.0000007.0
2016-04-04 07:00:00-07:0016.1499942.098.403003195.28993263.2585047.0
2016-04-04 08:00:00-07:0014.8500062.0309.468246546.08456096.1864007.0
2016-04-04 09:00:00-07:0014.0500182.0535.149162734.619021109.8364027.0
2016-04-04 10:00:00-07:0012.8500062.8728.411419836.004535116.5825167.0
2016-04-04 11:00:00-07:0011.7500003.2866.467071889.937608120.0395887.0
2016-04-04 12:00:00-07:0012.2500003.2956.960655946.182414112.6525954.0
.....................
2016-04-05 08:00:00-07:0016.0500182.8269.475440389.275176115.79651719.0
2016-04-05 09:00:00-07:0015.6499942.8476.642766582.299043137.22065619.0
2016-04-05 10:00:00-07:0014.0500182.8659.257592694.654159148.35003819.0
2016-04-05 11:00:00-07:0013.2500002.0791.324193756.639967154.18793319.0
2016-04-05 12:00:00-07:0013.2500002.0912.806997870.576552133.34101111.0
2016-04-05 15:00:00-07:0017.7500002.8686.736601779.368886126.68162711.0
2016-04-05 18:00:00-07:0027.8500061.670.34151651.55260262.21423022.0
2016-04-05 21:00:00-07:0030.7500003.60.0000000.0000000.00000022.0
2016-04-06 00:00:00-07:0030.1499945.20.0000000.0000000.00000018.0
2016-04-06 03:00:00-07:0023.7500003.60.0000000.0000000.00000018.0
2016-04-06 06:00:00-07:0018.3500062.00.0000000.0000000.00000018.0
2016-04-06 09:00:00-07:0015.1499942.8486.247698597.936966135.38374918.0
2016-04-06 12:00:00-07:0012.9500122.8821.575346720.695453174.17303825.0
2016-04-06 15:00:00-07:0018.3500064.8606.490010615.942189162.43824025.0
2016-04-06 18:00:00-07:0029.5500186.469.67064936.16371963.88409627.0
2016-04-06 21:00:00-07:0032.6499945.20.0000000.0000000.00000027.0
2016-04-07 00:00:00-07:0032.1499943.60.0000000.0000000.00000045.0
2016-04-07 06:00:00-07:0020.7500004.80.0000000.0000000.00000045.0
2016-04-07 12:00:00-07:0014.9500123.6474.766289166.733576324.50537080.0
2016-04-07 18:00:00-07:0025.6499944.066.5861050.01716966.58331880.0
2016-04-08 00:00:00-07:0027.2500004.00.0000000.0000000.00000077.0
2016-04-08 06:00:00-07:0018.3500062.80.0000000.0000000.00000077.0
2016-04-08 12:00:00-07:0014.0500183.2530.921450253.101074302.10571471.0
2016-04-08 18:00:00-07:0023.9500125.267.5650120.17657167.53593071.0
2016-04-09 00:00:00-07:0025.6499946.80.0000000.0000000.00000050.0
2016-04-09 06:00:00-07:0017.1499943.60.0000000.0000000.00000050.0
2016-04-09 12:00:00-07:0012.7500003.2685.800911494.508131237.36246647.0
2016-04-09 18:00:00-07:0023.3500065.269.2647306.52733668.17442947.0
2016-04-10 00:00:00-07:0025.5500186.40.0000000.0000000.00000039.0
2016-04-10 06:00:00-07:0016.8500064.80.0000000.0000000.00000039.0
\n", + "

79 rows × 6 columns

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" + ], + "text/plain": [ + " temperature wind_speed ghi dni \\\n", + "2016-04-03 07:00:00-07:00 13.950012 3.2 106.189110 278.487427 \n", + "2016-04-03 08:00:00-07:00 12.950012 3.2 334.661071 651.734481 \n", + "2016-04-03 09:00:00-07:00 12.250000 3.2 569.712283 829.668309 \n", + "2016-04-03 10:00:00-07:00 10.750000 3.2 768.177123 921.228009 \n", + "2016-04-03 11:00:00-07:00 9.350006 6.4 909.195203 968.970216 \n", + "2016-04-03 12:00:00-07:00 9.450012 6.4 980.540706 989.349943 \n", + "2016-04-03 13:00:00-07:00 10.750000 6.8 976.446778 988.238362 \n", + "2016-04-03 14:00:00-07:00 12.649994 6.8 897.257250 965.338091 \n", + "2016-04-03 15:00:00-07:00 15.550018 6.8 749.436252 913.979997 \n", + "2016-04-03 16:00:00-07:00 18.950012 5.6 545.990447 816.024490 \n", + "2016-04-03 17:00:00-07:00 22.149994 4.0 309.082267 624.337268 \n", + "2016-04-03 18:00:00-07:00 25.149994 3.6 85.201516 210.166726 \n", + "2016-04-03 19:00:00-07:00 26.649994 3.2 0.000000 0.000000 \n", + "2016-04-03 20:00:00-07:00 27.850006 3.2 0.000000 0.000000 \n", + "2016-04-03 21:00:00-07:00 27.649994 3.6 0.000000 0.000000 \n", + "2016-04-03 22:00:00-07:00 28.350006 1.6 0.000000 0.000000 \n", + "2016-04-03 23:00:00-07:00 28.250000 1.6 0.000000 0.000000 \n", + "2016-04-04 00:00:00-07:00 27.750000 1.6 0.000000 0.000000 \n", + "2016-04-04 01:00:00-07:00 26.850006 2.0 0.000000 0.000000 \n", + "2016-04-04 02:00:00-07:00 24.050018 2.0 0.000000 0.000000 \n", + "2016-04-04 03:00:00-07:00 22.149994 2.0 0.000000 0.000000 \n", + "2016-04-04 04:00:00-07:00 20.149994 2.0 0.000000 0.000000 \n", + "2016-04-04 05:00:00-07:00 18.350006 2.0 0.000000 0.000000 \n", + "2016-04-04 06:00:00-07:00 17.149994 2.0 0.000000 0.000000 \n", + "2016-04-04 07:00:00-07:00 16.149994 2.0 98.403003 195.289932 \n", + "2016-04-04 08:00:00-07:00 14.850006 2.0 309.468246 546.084560 \n", + "2016-04-04 09:00:00-07:00 14.050018 2.0 535.149162 734.619021 \n", + "2016-04-04 10:00:00-07:00 12.850006 2.8 728.411419 836.004535 \n", + "2016-04-04 11:00:00-07:00 11.750000 3.2 866.467071 889.937608 \n", + "2016-04-04 12:00:00-07:00 12.250000 3.2 956.960655 946.182414 \n", + "... ... ... ... ... \n", + "2016-04-05 08:00:00-07:00 16.050018 2.8 269.475440 389.275176 \n", + "2016-04-05 09:00:00-07:00 15.649994 2.8 476.642766 582.299043 \n", + "2016-04-05 10:00:00-07:00 14.050018 2.8 659.257592 694.654159 \n", + "2016-04-05 11:00:00-07:00 13.250000 2.0 791.324193 756.639967 \n", + "2016-04-05 12:00:00-07:00 13.250000 2.0 912.806997 870.576552 \n", + "2016-04-05 15:00:00-07:00 17.750000 2.8 686.736601 779.368886 \n", + "2016-04-05 18:00:00-07:00 27.850006 1.6 70.341516 51.552602 \n", + "2016-04-05 21:00:00-07:00 30.750000 3.6 0.000000 0.000000 \n", + "2016-04-06 00:00:00-07:00 30.149994 5.2 0.000000 0.000000 \n", + "2016-04-06 03:00:00-07:00 23.750000 3.6 0.000000 0.000000 \n", + "2016-04-06 06:00:00-07:00 18.350006 2.0 0.000000 0.000000 \n", + "2016-04-06 09:00:00-07:00 15.149994 2.8 486.247698 597.936966 \n", + "2016-04-06 12:00:00-07:00 12.950012 2.8 821.575346 720.695453 \n", + "2016-04-06 15:00:00-07:00 18.350006 4.8 606.490010 615.942189 \n", + "2016-04-06 18:00:00-07:00 29.550018 6.4 69.670649 36.163719 \n", + "2016-04-06 21:00:00-07:00 32.649994 5.2 0.000000 0.000000 \n", + "2016-04-07 00:00:00-07:00 32.149994 3.6 0.000000 0.000000 \n", + "2016-04-07 06:00:00-07:00 20.750000 4.8 0.000000 0.000000 \n", + "2016-04-07 12:00:00-07:00 14.950012 3.6 474.766289 166.733576 \n", + "2016-04-07 18:00:00-07:00 25.649994 4.0 66.586105 0.017169 \n", + "2016-04-08 00:00:00-07:00 27.250000 4.0 0.000000 0.000000 \n", + "2016-04-08 06:00:00-07:00 18.350006 2.8 0.000000 0.000000 \n", + "2016-04-08 12:00:00-07:00 14.050018 3.2 530.921450 253.101074 \n", + "2016-04-08 18:00:00-07:00 23.950012 5.2 67.565012 0.176571 \n", + "2016-04-09 00:00:00-07:00 25.649994 6.8 0.000000 0.000000 \n", + "2016-04-09 06:00:00-07:00 17.149994 3.6 0.000000 0.000000 \n", + "2016-04-09 12:00:00-07:00 12.750000 3.2 685.800911 494.508131 \n", + "2016-04-09 18:00:00-07:00 23.350006 5.2 69.264730 6.527336 \n", + "2016-04-10 00:00:00-07:00 25.550018 6.4 0.000000 0.000000 \n", + "2016-04-10 06:00:00-07:00 16.850006 4.8 0.000000 0.000000 \n", + "\n", + " dhi total_clouds \n", + "2016-04-03 07:00:00-07:00 57.312551 0.0 \n", + "2016-04-03 08:00:00-07:00 82.893092 0.0 \n", + "2016-04-03 09:00:00-07:00 92.683744 0.0 \n", + "2016-04-03 10:00:00-07:00 97.377713 0.0 \n", + "2016-04-03 11:00:00-07:00 99.750194 0.0 \n", + "2016-04-03 12:00:00-07:00 100.748999 0.0 \n", + "2016-04-03 13:00:00-07:00 100.694720 0.0 \n", + "2016-04-03 14:00:00-07:00 99.571346 0.0 \n", + "2016-04-03 15:00:00-07:00 97.013280 0.0 \n", + "2016-04-03 16:00:00-07:00 91.966405 0.0 \n", + "2016-04-03 17:00:00-07:00 81.289376 0.0 \n", + "2016-04-03 18:00:00-07:00 53.065861 1.0 \n", + "2016-04-03 19:00:00-07:00 0.000000 1.0 \n", + "2016-04-03 20:00:00-07:00 0.000000 0.0 \n", + "2016-04-03 21:00:00-07:00 0.000000 0.0 \n", + "2016-04-03 22:00:00-07:00 0.000000 0.0 \n", + "2016-04-03 23:00:00-07:00 0.000000 0.0 \n", + "2016-04-04 00:00:00-07:00 0.000000 1.0 \n", + "2016-04-04 01:00:00-07:00 0.000000 1.0 \n", + "2016-04-04 02:00:00-07:00 0.000000 1.0 \n", + "2016-04-04 03:00:00-07:00 0.000000 1.0 \n", + "2016-04-04 04:00:00-07:00 0.000000 1.0 \n", + "2016-04-04 05:00:00-07:00 0.000000 1.0 \n", + "2016-04-04 06:00:00-07:00 0.000000 7.0 \n", + "2016-04-04 07:00:00-07:00 63.258504 7.0 \n", + "2016-04-04 08:00:00-07:00 96.186400 7.0 \n", + "2016-04-04 09:00:00-07:00 109.836402 7.0 \n", + "2016-04-04 10:00:00-07:00 116.582516 7.0 \n", + "2016-04-04 11:00:00-07:00 120.039588 7.0 \n", + "2016-04-04 12:00:00-07:00 112.652595 4.0 \n", + "... ... ... \n", + "2016-04-05 08:00:00-07:00 115.796517 19.0 \n", + "2016-04-05 09:00:00-07:00 137.220656 19.0 \n", + "2016-04-05 10:00:00-07:00 148.350038 19.0 \n", + "2016-04-05 11:00:00-07:00 154.187933 19.0 \n", + "2016-04-05 12:00:00-07:00 133.341011 11.0 \n", + "2016-04-05 15:00:00-07:00 126.681627 11.0 \n", + "2016-04-05 18:00:00-07:00 62.214230 22.0 \n", + "2016-04-05 21:00:00-07:00 0.000000 22.0 \n", + "2016-04-06 00:00:00-07:00 0.000000 18.0 \n", + "2016-04-06 03:00:00-07:00 0.000000 18.0 \n", + "2016-04-06 06:00:00-07:00 0.000000 18.0 \n", + "2016-04-06 09:00:00-07:00 135.383749 18.0 \n", + "2016-04-06 12:00:00-07:00 174.173038 25.0 \n", + "2016-04-06 15:00:00-07:00 162.438240 25.0 \n", + "2016-04-06 18:00:00-07:00 63.884096 27.0 \n", + "2016-04-06 21:00:00-07:00 0.000000 27.0 \n", + "2016-04-07 00:00:00-07:00 0.000000 45.0 \n", + "2016-04-07 06:00:00-07:00 0.000000 45.0 \n", + "2016-04-07 12:00:00-07:00 324.505370 80.0 \n", + "2016-04-07 18:00:00-07:00 66.583318 80.0 \n", + "2016-04-08 00:00:00-07:00 0.000000 77.0 \n", + "2016-04-08 06:00:00-07:00 0.000000 77.0 \n", + "2016-04-08 12:00:00-07:00 302.105714 71.0 \n", + "2016-04-08 18:00:00-07:00 67.535930 71.0 \n", + "2016-04-09 00:00:00-07:00 0.000000 50.0 \n", + "2016-04-09 06:00:00-07:00 0.000000 50.0 \n", + "2016-04-09 12:00:00-07:00 237.362466 47.0 \n", + "2016-04-09 18:00:00-07:00 68.174429 47.0 \n", + "2016-04-10 00:00:00-07:00 0.000000 39.0 \n", + "2016-04-10 06:00:00-07:00 0.000000 39.0 \n", + "\n", + "[79 rows x 6 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## RAP" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fm = RAP(resolution=20)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# retrieve data\n", + "data = fm.get_processed_data(latitude, longitude, start, end)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "cloud_vars = ['total_clouds', 'high_clouds', 'mid_clouds', 'low_clouds']" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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KlCmKiIhwxXwAAAAAUCmUebW9sWPH6u9//7s2btyojIwM9e/fX2PGjHHFbAAA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\njTp27JimTJmigwcPauTIkdq/f7/+85/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for varname in cloud_vars:\n", + " data[varname].plot(ls='-', linewidth=2)\n", + "plt.ylabel('Cloud cover' + ' %')\n", + "plt.xlabel('Forecast Time ('+str(data.index.tz)+')')\n", + "plt.title('RAP')\n", + "plt.legend(bbox_to_anchor=(1.18,1.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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temperaturewind_speedghidnidhitotal_cloudslow_cloudsmid_cloudshigh_clouds
2016-04-03 07:00:00-07:0014.2541503.840898106.189110278.48742757.3125510.00.00.00.0
2016-04-03 08:00:00-07:0013.2410583.722868334.661071651.73448182.8930920.00.00.00.0
2016-04-03 09:00:00-07:0012.5186773.776808569.712283829.66830992.6837440.00.00.00.0
2016-04-03 10:00:00-07:0011.6900333.539163768.177123921.22800997.3777130.00.00.00.0
2016-04-03 11:00:00-07:0011.0307013.530295909.195203968.97021699.7501940.00.00.00.0
2016-04-03 12:00:00-07:0010.5362243.573894980.540706989.349943100.7489990.00.00.00.0
2016-04-03 13:00:00-07:0010.2579653.649565976.446778988.238362100.6947200.00.00.00.0
2016-04-03 14:00:00-07:0012.9057923.865550897.257250965.33809199.5713460.00.00.00.0
2016-04-03 15:00:00-07:0019.2282415.671968749.436252913.97999797.0132800.00.00.00.0
2016-04-03 16:00:00-07:0025.4264225.948405545.990447816.02449091.9664050.00.00.00.0
2016-04-03 17:00:00-07:0030.8190004.669879309.082267624.33726881.2893760.00.00.00.0
2016-04-03 18:00:00-07:0034.8639834.08992286.669267223.87968552.4368250.00.00.00.0
2016-04-03 19:00:00-07:0037.8324583.2453030.0000000.0000000.0000000.00.00.00.0
2016-04-03 20:00:00-07:0038.9081731.3772210.0000000.0000000.0000000.00.00.00.0
2016-04-03 21:00:00-07:0039.4824222.2574400.0000000.0000000.0000000.00.00.00.0
2016-04-03 22:00:00-07:0039.7952581.8566430.0000000.0000000.0000000.00.00.00.0
2016-04-03 23:00:00-07:0036.8944401.2144360.0000000.0000000.0000000.00.00.00.0
2016-04-04 00:00:00-07:0033.1927191.8445140.0000000.0000000.0000000.00.00.00.0
2016-04-04 01:00:00-07:0028.7002262.5550760.0000000.0000000.0000000.00.00.00.0
2016-04-04 02:00:00-07:0023.6082463.6613630.0000000.0000000.0000000.00.00.00.0
2016-04-04 03:00:00-07:0021.3423164.9086100.0000000.0000000.0000000.00.00.00.0
2016-04-04 04:00:00-07:0019.9257814.8757800.0000000.0000000.0000000.00.00.00.0
2016-04-04 05:00:00-07:0018.5988164.0745120.0000000.0000000.0000000.00.00.00.0
2016-04-04 06:00:00-07:0017.2319342.5609970.0000000.0000000.0000000.00.00.00.0
2016-04-04 07:00:00-07:0016.0268861.878456110.209559289.01325858.1985510.00.00.00.0
2016-04-04 08:00:00-07:0015.0457761.868246339.780345656.95709583.1955010.00.00.00.0
2016-04-04 09:00:00-07:0014.3715211.889787574.824061832.51650792.8328740.00.00.00.0
2016-04-04 10:00:00-07:0013.7061461.920604773.008364923.05793097.4695390.00.00.00.0
2016-04-04 11:00:00-07:0013.1477661.928311913.655213970.30997899.8160990.00.00.00.0
2016-04-04 12:00:00-07:0012.6993101.913968984.610605990.448411100.8026160.00.00.00.0
2016-04-04 13:00:00-07:0012.2490541.987152980.150327989.244235100.7438380.00.00.00.0
2016-04-04 14:00:00-07:0015.3542791.726539900.647088966.37633499.6224960.00.00.00.0
2016-04-04 15:00:00-07:0023.4684141.808320752.580636915.21299997.0753580.00.00.00.0
2016-04-04 16:00:00-07:0030.7813111.445872548.949165817.76567492.0582250.00.00.00.0
\n", + "
" + ], + "text/plain": [ + " temperature wind_speed ghi dni \\\n", + "2016-04-03 07:00:00-07:00 14.254150 3.840898 106.189110 278.487427 \n", + "2016-04-03 08:00:00-07:00 13.241058 3.722868 334.661071 651.734481 \n", + "2016-04-03 09:00:00-07:00 12.518677 3.776808 569.712283 829.668309 \n", + "2016-04-03 10:00:00-07:00 11.690033 3.539163 768.177123 921.228009 \n", + "2016-04-03 11:00:00-07:00 11.030701 3.530295 909.195203 968.970216 \n", + "2016-04-03 12:00:00-07:00 10.536224 3.573894 980.540706 989.349943 \n", + "2016-04-03 13:00:00-07:00 10.257965 3.649565 976.446778 988.238362 \n", + "2016-04-03 14:00:00-07:00 12.905792 3.865550 897.257250 965.338091 \n", + "2016-04-03 15:00:00-07:00 19.228241 5.671968 749.436252 913.979997 \n", + "2016-04-03 16:00:00-07:00 25.426422 5.948405 545.990447 816.024490 \n", + "2016-04-03 17:00:00-07:00 30.819000 4.669879 309.082267 624.337268 \n", + "2016-04-03 18:00:00-07:00 34.863983 4.089922 86.669267 223.879685 \n", + "2016-04-03 19:00:00-07:00 37.832458 3.245303 0.000000 0.000000 \n", + "2016-04-03 20:00:00-07:00 38.908173 1.377221 0.000000 0.000000 \n", + "2016-04-03 21:00:00-07:00 39.482422 2.257440 0.000000 0.000000 \n", + "2016-04-03 22:00:00-07:00 39.795258 1.856643 0.000000 0.000000 \n", + "2016-04-03 23:00:00-07:00 36.894440 1.214436 0.000000 0.000000 \n", + "2016-04-04 00:00:00-07:00 33.192719 1.844514 0.000000 0.000000 \n", + "2016-04-04 01:00:00-07:00 28.700226 2.555076 0.000000 0.000000 \n", + "2016-04-04 02:00:00-07:00 23.608246 3.661363 0.000000 0.000000 \n", + "2016-04-04 03:00:00-07:00 21.342316 4.908610 0.000000 0.000000 \n", + "2016-04-04 04:00:00-07:00 19.925781 4.875780 0.000000 0.000000 \n", + "2016-04-04 05:00:00-07:00 18.598816 4.074512 0.000000 0.000000 \n", + "2016-04-04 06:00:00-07:00 17.231934 2.560997 0.000000 0.000000 \n", + "2016-04-04 07:00:00-07:00 16.026886 1.878456 110.209559 289.013258 \n", + "2016-04-04 08:00:00-07:00 15.045776 1.868246 339.780345 656.957095 \n", + "2016-04-04 09:00:00-07:00 14.371521 1.889787 574.824061 832.516507 \n", + "2016-04-04 10:00:00-07:00 13.706146 1.920604 773.008364 923.057930 \n", + "2016-04-04 11:00:00-07:00 13.147766 1.928311 913.655213 970.309978 \n", + "2016-04-04 12:00:00-07:00 12.699310 1.913968 984.610605 990.448411 \n", + "2016-04-04 13:00:00-07:00 12.249054 1.987152 980.150327 989.244235 \n", + "2016-04-04 14:00:00-07:00 15.354279 1.726539 900.647088 966.376334 \n", + "2016-04-04 15:00:00-07:00 23.468414 1.808320 752.580636 915.212999 \n", + "2016-04-04 16:00:00-07:00 30.781311 1.445872 548.949165 817.765674 \n", + "\n", + " dhi total_clouds low_clouds mid_clouds \\\n", + "2016-04-03 07:00:00-07:00 57.312551 0.0 0.0 0.0 \n", + "2016-04-03 08:00:00-07:00 82.893092 0.0 0.0 0.0 \n", + "2016-04-03 09:00:00-07:00 92.683744 0.0 0.0 0.0 \n", + "2016-04-03 10:00:00-07:00 97.377713 0.0 0.0 0.0 \n", + "2016-04-03 11:00:00-07:00 99.750194 0.0 0.0 0.0 \n", + "2016-04-03 12:00:00-07:00 100.748999 0.0 0.0 0.0 \n", + "2016-04-03 13:00:00-07:00 100.694720 0.0 0.0 0.0 \n", + "2016-04-03 14:00:00-07:00 99.571346 0.0 0.0 0.0 \n", + "2016-04-03 15:00:00-07:00 97.013280 0.0 0.0 0.0 \n", + "2016-04-03 16:00:00-07:00 91.966405 0.0 0.0 0.0 \n", + "2016-04-03 17:00:00-07:00 81.289376 0.0 0.0 0.0 \n", + "2016-04-03 18:00:00-07:00 52.436825 0.0 0.0 0.0 \n", + "2016-04-03 19:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-03 20:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-03 21:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-03 22:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-03 23:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 00:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 01:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 02:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 03:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 04:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 05:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 06:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 07:00:00-07:00 58.198551 0.0 0.0 0.0 \n", + "2016-04-04 08:00:00-07:00 83.195501 0.0 0.0 0.0 \n", + "2016-04-04 09:00:00-07:00 92.832874 0.0 0.0 0.0 \n", + "2016-04-04 10:00:00-07:00 97.469539 0.0 0.0 0.0 \n", + "2016-04-04 11:00:00-07:00 99.816099 0.0 0.0 0.0 \n", + "2016-04-04 12:00:00-07:00 100.802616 0.0 0.0 0.0 \n", + "2016-04-04 13:00:00-07:00 100.743838 0.0 0.0 0.0 \n", + "2016-04-04 14:00:00-07:00 99.622496 0.0 0.0 0.0 \n", + "2016-04-04 15:00:00-07:00 97.075358 0.0 0.0 0.0 \n", + "2016-04-04 16:00:00-07:00 92.058225 0.0 0.0 0.0 \n", + "\n", + " high_clouds \n", + "2016-04-03 07:00:00-07:00 0.0 \n", + "2016-04-03 08:00:00-07:00 0.0 \n", + "2016-04-03 09:00:00-07:00 0.0 \n", + "2016-04-03 10:00:00-07:00 0.0 \n", + "2016-04-03 11:00:00-07:00 0.0 \n", + "2016-04-03 12:00:00-07:00 0.0 \n", + "2016-04-03 13:00:00-07:00 0.0 \n", + "2016-04-03 14:00:00-07:00 0.0 \n", + "2016-04-03 15:00:00-07:00 0.0 \n", + "2016-04-03 16:00:00-07:00 0.0 \n", + "2016-04-03 17:00:00-07:00 0.0 \n", + "2016-04-03 18:00:00-07:00 0.0 \n", + "2016-04-03 19:00:00-07:00 0.0 \n", + "2016-04-03 20:00:00-07:00 0.0 \n", + "2016-04-03 21:00:00-07:00 0.0 \n", + "2016-04-03 22:00:00-07:00 0.0 \n", + "2016-04-03 23:00:00-07:00 0.0 \n", + "2016-04-04 00:00:00-07:00 0.0 \n", + "2016-04-04 01:00:00-07:00 0.0 \n", + "2016-04-04 02:00:00-07:00 0.0 \n", + "2016-04-04 03:00:00-07:00 0.0 \n", + "2016-04-04 04:00:00-07:00 0.0 \n", + "2016-04-04 05:00:00-07:00 0.0 \n", + "2016-04-04 06:00:00-07:00 0.0 \n", + "2016-04-04 07:00:00-07:00 0.0 \n", + "2016-04-04 08:00:00-07:00 0.0 \n", + "2016-04-04 09:00:00-07:00 0.0 \n", + "2016-04-04 10:00:00-07:00 0.0 \n", + "2016-04-04 11:00:00-07:00 0.0 \n", + "2016-04-04 12:00:00-07:00 0.0 \n", + "2016-04-04 13:00:00-07:00 0.0 \n", + "2016-04-04 14:00:00-07:00 0.0 \n", + "2016-04-04 15:00:00-07:00 0.0 \n", + "2016-04-04 16:00:00-07:00 0.0 " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## HRRR" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fm = HRRR()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# retrieve data\n", + "data = fm.get_processed_data(latitude, longitude, start, end)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "cloud_vars = ['total_clouds', 'high_clouds', 'mid_clouds', 'low_clouds']" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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dunXTuXPn9MUXX2jq1KnKy8tTRkaGK+YDAAAAgFqhyvIkSfv27dMnn3yiVatW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\n3LkcPHiQgIAAzM3N8fPzM9xitmjRIvr27Wu41e3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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for varname in cloud_vars:\n", + " data[varname].plot(ls='-', linewidth=2)\n", + "plt.ylabel('Cloud cover' + ' %')\n", + "plt.xlabel('Forecast Time (' + str(data.index.tz) + ')')\n", + "plt.title('HRRR')\n", + "plt.legend(bbox_to_anchor=(1.18,1.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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temperaturewind_speedghidnidhitotal_cloudslow_cloudsmid_cloudshigh_clouds
2016-04-03 07:00:00-07:0013.6903993.379238106.189110278.48742757.3125510.00.00.00.0
2016-04-03 08:00:00-07:0012.6533813.755251334.661071651.73448182.8930920.00.00.00.0
2016-04-03 09:00:00-07:0011.9708863.924942569.712283829.66830992.6837440.00.00.00.0
2016-04-03 10:00:00-07:0011.5850833.541783768.177123921.22800997.3777130.00.00.00.0
2016-04-03 11:00:00-07:0010.8814393.525367909.195203968.97021699.7501940.00.00.00.0
2016-04-03 12:00:00-07:0010.9851993.425176980.540706989.349943100.7489990.00.00.00.0
2016-04-03 13:00:00-07:0010.9148563.475241976.446778988.238362100.6947200.00.00.00.0
2016-04-03 14:00:00-07:0011.1625672.863182897.257250965.33809199.5713460.00.00.00.0
2016-04-03 15:00:00-07:0013.1945804.317441749.436252913.97999797.0132800.00.00.00.0
2016-04-03 16:00:00-07:0017.4787605.788805545.990447816.02449091.9664050.00.00.00.0
2016-04-03 17:00:00-07:0019.8555914.472356309.082267624.33726881.2893760.00.00.00.0
2016-04-03 18:00:00-07:0021.7540893.58784986.669267223.87968552.4368250.00.00.00.0
2016-04-03 19:00:00-07:0022.5716552.9577900.0000000.0000000.0000000.00.00.00.0
2016-04-03 20:00:00-07:0021.7628171.4649750.0000000.0000000.0000000.00.00.00.0
2016-04-03 21:00:00-07:0023.1404421.9400390.0000000.0000000.0000000.00.00.00.0
2016-04-03 22:00:00-07:0025.7279971.7491750.0000000.0000000.0000000.00.00.00.0
2016-04-03 23:00:00-07:0025.9007261.1529970.0000000.0000000.0000000.00.00.00.0
2016-04-04 00:00:00-07:0026.4670411.9385480.0000000.0000000.0000000.00.00.00.0
2016-04-04 01:00:00-07:0026.1522832.7517430.0000000.0000000.0000000.00.00.00.0
2016-04-04 02:00:00-07:0024.5821533.8502300.0000000.0000000.0000000.00.00.00.0
2016-04-04 03:00:00-07:0022.6556404.6780200.0000000.0000000.0000000.00.00.00.0
2016-04-04 04:00:00-07:0021.7822274.6635010.0000000.0000000.0000000.00.00.00.0
2016-04-04 05:00:00-07:0020.6750183.2375500.0000000.0000000.0000000.00.00.00.0
2016-04-04 06:00:00-07:0019.5709231.5562290.0000000.0000000.0000000.00.00.00.0
2016-04-04 07:00:00-07:0018.5682071.777451110.209559289.01325858.1985510.00.00.00.0
2016-04-04 08:00:00-07:0017.5562742.681900339.780345656.95709583.1955010.00.00.00.0
2016-04-04 09:00:00-07:0017.2231452.280646574.824061832.51650792.8328740.00.00.00.0
2016-04-04 10:00:00-07:0016.9265142.012287773.008364923.05793097.4695390.00.00.00.0
2016-04-04 11:00:00-07:0016.2558591.856279913.655213970.30997899.8160990.00.00.00.0
2016-04-04 12:00:00-07:0015.4187622.401458984.610605990.448411100.8026160.00.00.00.0
2016-04-04 13:00:00-07:0015.1942442.647570980.150327989.244235100.7438380.00.00.00.0
\n", + "
" + ], + "text/plain": [ + " temperature wind_speed ghi dni \\\n", + "2016-04-03 07:00:00-07:00 13.690399 3.379238 106.189110 278.487427 \n", + "2016-04-03 08:00:00-07:00 12.653381 3.755251 334.661071 651.734481 \n", + "2016-04-03 09:00:00-07:00 11.970886 3.924942 569.712283 829.668309 \n", + "2016-04-03 10:00:00-07:00 11.585083 3.541783 768.177123 921.228009 \n", + "2016-04-03 11:00:00-07:00 10.881439 3.525367 909.195203 968.970216 \n", + "2016-04-03 12:00:00-07:00 10.985199 3.425176 980.540706 989.349943 \n", + "2016-04-03 13:00:00-07:00 10.914856 3.475241 976.446778 988.238362 \n", + "2016-04-03 14:00:00-07:00 11.162567 2.863182 897.257250 965.338091 \n", + "2016-04-03 15:00:00-07:00 13.194580 4.317441 749.436252 913.979997 \n", + "2016-04-03 16:00:00-07:00 17.478760 5.788805 545.990447 816.024490 \n", + "2016-04-03 17:00:00-07:00 19.855591 4.472356 309.082267 624.337268 \n", + "2016-04-03 18:00:00-07:00 21.754089 3.587849 86.669267 223.879685 \n", + "2016-04-03 19:00:00-07:00 22.571655 2.957790 0.000000 0.000000 \n", + "2016-04-03 20:00:00-07:00 21.762817 1.464975 0.000000 0.000000 \n", + "2016-04-03 21:00:00-07:00 23.140442 1.940039 0.000000 0.000000 \n", + "2016-04-03 22:00:00-07:00 25.727997 1.749175 0.000000 0.000000 \n", + "2016-04-03 23:00:00-07:00 25.900726 1.152997 0.000000 0.000000 \n", + "2016-04-04 00:00:00-07:00 26.467041 1.938548 0.000000 0.000000 \n", + "2016-04-04 01:00:00-07:00 26.152283 2.751743 0.000000 0.000000 \n", + "2016-04-04 02:00:00-07:00 24.582153 3.850230 0.000000 0.000000 \n", + "2016-04-04 03:00:00-07:00 22.655640 4.678020 0.000000 0.000000 \n", + "2016-04-04 04:00:00-07:00 21.782227 4.663501 0.000000 0.000000 \n", + "2016-04-04 05:00:00-07:00 20.675018 3.237550 0.000000 0.000000 \n", + "2016-04-04 06:00:00-07:00 19.570923 1.556229 0.000000 0.000000 \n", + "2016-04-04 07:00:00-07:00 18.568207 1.777451 110.209559 289.013258 \n", + "2016-04-04 08:00:00-07:00 17.556274 2.681900 339.780345 656.957095 \n", + "2016-04-04 09:00:00-07:00 17.223145 2.280646 574.824061 832.516507 \n", + "2016-04-04 10:00:00-07:00 16.926514 2.012287 773.008364 923.057930 \n", + "2016-04-04 11:00:00-07:00 16.255859 1.856279 913.655213 970.309978 \n", + "2016-04-04 12:00:00-07:00 15.418762 2.401458 984.610605 990.448411 \n", + "2016-04-04 13:00:00-07:00 15.194244 2.647570 980.150327 989.244235 \n", + "\n", + " dhi total_clouds low_clouds mid_clouds \\\n", + "2016-04-03 07:00:00-07:00 57.312551 0.0 0.0 0.0 \n", + "2016-04-03 08:00:00-07:00 82.893092 0.0 0.0 0.0 \n", + "2016-04-03 09:00:00-07:00 92.683744 0.0 0.0 0.0 \n", + "2016-04-03 10:00:00-07:00 97.377713 0.0 0.0 0.0 \n", + "2016-04-03 11:00:00-07:00 99.750194 0.0 0.0 0.0 \n", + "2016-04-03 12:00:00-07:00 100.748999 0.0 0.0 0.0 \n", + "2016-04-03 13:00:00-07:00 100.694720 0.0 0.0 0.0 \n", + "2016-04-03 14:00:00-07:00 99.571346 0.0 0.0 0.0 \n", + "2016-04-03 15:00:00-07:00 97.013280 0.0 0.0 0.0 \n", + "2016-04-03 16:00:00-07:00 91.966405 0.0 0.0 0.0 \n", + "2016-04-03 17:00:00-07:00 81.289376 0.0 0.0 0.0 \n", + "2016-04-03 18:00:00-07:00 52.436825 0.0 0.0 0.0 \n", + "2016-04-03 19:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-03 20:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-03 21:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-03 22:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-03 23:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 00:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 01:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 02:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 03:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 04:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 05:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 06:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-04 07:00:00-07:00 58.198551 0.0 0.0 0.0 \n", + "2016-04-04 08:00:00-07:00 83.195501 0.0 0.0 0.0 \n", + "2016-04-04 09:00:00-07:00 92.832874 0.0 0.0 0.0 \n", + "2016-04-04 10:00:00-07:00 97.469539 0.0 0.0 0.0 \n", + "2016-04-04 11:00:00-07:00 99.816099 0.0 0.0 0.0 \n", + "2016-04-04 12:00:00-07:00 100.802616 0.0 0.0 0.0 \n", + "2016-04-04 13:00:00-07:00 100.743838 0.0 0.0 0.0 \n", + "\n", + " high_clouds \n", + "2016-04-03 07:00:00-07:00 0.0 \n", + "2016-04-03 08:00:00-07:00 0.0 \n", + "2016-04-03 09:00:00-07:00 0.0 \n", + "2016-04-03 10:00:00-07:00 0.0 \n", + "2016-04-03 11:00:00-07:00 0.0 \n", + "2016-04-03 12:00:00-07:00 0.0 \n", + "2016-04-03 13:00:00-07:00 0.0 \n", + "2016-04-03 14:00:00-07:00 0.0 \n", + "2016-04-03 15:00:00-07:00 0.0 \n", + "2016-04-03 16:00:00-07:00 0.0 \n", + "2016-04-03 17:00:00-07:00 0.0 \n", + "2016-04-03 18:00:00-07:00 0.0 \n", + "2016-04-03 19:00:00-07:00 0.0 \n", + "2016-04-03 20:00:00-07:00 0.0 \n", + "2016-04-03 21:00:00-07:00 0.0 \n", + "2016-04-03 22:00:00-07:00 0.0 \n", + "2016-04-03 23:00:00-07:00 0.0 \n", + "2016-04-04 00:00:00-07:00 0.0 \n", + "2016-04-04 01:00:00-07:00 0.0 \n", + "2016-04-04 02:00:00-07:00 0.0 \n", + "2016-04-04 03:00:00-07:00 0.0 \n", + "2016-04-04 04:00:00-07:00 0.0 \n", + "2016-04-04 05:00:00-07:00 0.0 \n", + "2016-04-04 06:00:00-07:00 0.0 \n", + "2016-04-04 07:00:00-07:00 0.0 \n", + "2016-04-04 08:00:00-07:00 0.0 \n", + "2016-04-04 09:00:00-07:00 0.0 \n", + "2016-04-04 10:00:00-07:00 0.0 \n", + "2016-04-04 11:00:00-07:00 0.0 \n", + "2016-04-04 12:00:00-07:00 0.0 \n", + "2016-04-04 13:00:00-07:00 0.0 " + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## HRRR (ESRL)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/holmgren/git_repos/pvlibfx/pvlib-python/pvlib/forecast.py:572: UserWarning: HRRR_ESRL is an experimental model and is not always available.\n", + " warnings.warn('HRRR_ESRL is an experimental model and is not always available.')\n", + "WARNING:siphon.metadata:Value GRIB2 not valid for type dataFormat: must be ['BUFR', 'ESML', 'GEMPAK', 'GINI', 'GRIB-1', 'GRIB-2', 'HDF4', 'HDF5', 'McIDAS-AREA', 'NcML', 'NetCDF', 'NetCDF-4', 'NEXRAD2', 'NIDS', 'image/gif', 'image/jpeg', 'image/tiff', 'text/csv', 'text/html', 'text/plain', 'text/tab-separated-values', 'text/xml', 'video/mpeg', 'video/quicktime', 'video/realtime', 'application/octet-stream', 'audio/x-pn-realaudio', 'application/xml', 'application/octet-stream', 'image/x-cmu-raster', 'application/x-pn-realaudio', 'application/x-bcpio', 'application/x-sh', 'video/mpeg', 'image/x-xwindowdump', 'application/msword', 'image/x-ms-bmp', 'application/x-shar', 'application/javascript', 'application/x-wais-source', 'application/x-dvi', 'audio/x-aiff', 'text/plain', 'application/msword', 'message/rfc822', 'application/x-pkcs12', 'text/css', 'application/x-csh', 'application/vnd.ms-powerpoint', 'application/pdf', 'application/x-netcdf', 'text/plain', 'application/postscript', 'image/jpeg', 'image/jpeg', 'text/x-python', 'text/xml', 'image/jpeg', 'application/postscript', 'application/x-gtar', 'image/x-xpixmap', 'application/x-hdf', 'message/rfc822', 'text/tab-separated-values', 'application/xml', 'application/pkcs7-mime', 'image/vnd.microsoft.icon', 'application/postscript', 'image/ief', 'application/octet-stream', 'application/vnd.ms-excel', 'image/x-portable-bitmap', 'application/x-texinfo', 'application/vnd.ms-excel', 'application/x-tex', 'text/richtext', 'text/html', 'audio/x-aiff', 'audio/x-aiff', 'application/octet-stream', 'text/x-sgml', 'image/tiff', 'video/mpeg', 'application/x-ustar', 'image/gif', 'application/vnd.ms-powerpoint', 'application/vnd.ms-powerpoint', 'text/x-sgml', 'image/x-portable-pixmap', 'application/x-latex', 'text/plain', 'video/quicktime', 'application/vnd.ms-powerpoint', 'application/x-troff', 'application/xml', 'application/xml', 'message/rfc822', 'application/x-netcdf', 'application/x-sv4cpio', 'application/octet-stream', 'text/plain', 'application/x-tcl', 'application/msword', 'application/octet-stream', 'application/octet-stream', 'text/plain', 'audio/x-wav', 'text/x-vcard', 'image/x-xbitmap', 'text/plain', 'audio/basic', 'application/x-troff', 'image/tiff', 'application/x-texinfo', 'application/oda', 'application/x-troff-ms', 'image/x-rgb', 'application/x-troff-me', 'application/x-sv4crc', 'video/quicktime', 'video/mpeg', 'video/mpeg', 'video/mpeg', 'video/x-msvideo', 'image/x-portable-graymap', 'application/vnd.ms-powerpoint', 'application/x-mif', 'application/x-troff', 'text/html', 'application/x-troff-man', 'text/x-setext', 'application/zip', 'video/x-sgi-movie', 'application/x-python-code', 'image/png', 'application/x-pkcs12', 'message/rfc822', 'application/x-tar', 'image/x-portable-anymap', 'application/x-python-code', 'audio/basic', 'application/x-cpio', 'application/x-shockwave-flash', 'audio/mpeg', 'audio/mpeg', 'video/mp4']\n" + ] + }, + { + "ename": "ParseError", + "evalue": "syntax error: line 1, column 0", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32munknown\u001b[0m\n\u001b[0;31mParseError\u001b[0m\u001b[0;31m:\u001b[0m syntax error: line 1, column 0\n" + ] + } + ], + "source": [ + "fm = HRRR_ESRL()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# retrieve data\n", + "data = fm.get_query_data(latitude, longitude, start, end)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "cloud_vars = ['total_clouds','high_clouds','mid_clouds','low_clouds']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [ + "for varname in cloud_vars:\n", + " data[varname].plot(ls='-', linewidth=2)\n", + "plt.ylabel('Cloud cover' + ' %')\n", + "plt.xlabel('Forecast Time ('+str(data.index.tz)+')')\n", + "plt.title('HRRR_ESRL')\n", + "plt.legend(bbox_to_anchor=(1.18,1.0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "data['ghi'].plot(linewidth=2, ls='-')\n", + "plt.ylabel('GHI W/m**2')\n", + "plt.xlabel('Forecast Time ('+str(data.index.tz)+')')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quick power calculation" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pvlib.pvsystem import PVSystem, retrieve_sam\n", + "from pvlib.modelchain import ModelChain\n", + "\n", + "sandia_modules = retrieve_sam(name='SandiaMod')\n", + "sapm_inverters = retrieve_sam('sandiainverter')\n", + "module = sandia_modules['Canadian_Solar_CS5P_220M___2009_']\n", + "inverter = sapm_inverters['ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_']\n", + "\n", + "system = PVSystem(module_parameters=module,\n", + " inverter_parameters=inverter)\n", + "\n", + "# fx is a common abbreviation for forecast\n", + "fx_model = GFS()\n", + "fx_data = fx_model.get_processed_data(latitude, longitude, start, end)\n", + "\n", + "# use a ModelChain object to calculate modeling intermediates\n", + "mc = ModelChain(system, fx_model.location,\n", + " orientation_strategy='south_at_latitude_tilt')\n", + "\n", + "# extract relevant data for model chain\n", + "irradiance = fx_data[['ghi', 'dni', 'dhi']]\n", + "weather = fx_data[['wind_speed', 'temperature']].rename(\n", + " columns={'temperature':'temp_air'})\n", + "mc.run_model(fx_data.index, irradiance=irradiance, weather=weather)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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4NaD9PrNnzwFg+vQTeOyx3w5o+0499QxOPfWMfrdDCDF8mUnESDxCZ0ybPNwQ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\nQQQkEERUQQRB6vmfiN+jULq9E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9uPauVi/WvIVvMpnkkUcewWrV+z4fffRRPv3pT/Pkk0+STqd5/vnnV32QM9E0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\n9baDjeX1qz7ebZWbeaPvBJiihOJpGuu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NTOC+jB3He/e9GYDnM8/2zJCFxVbF1xdQqTSr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\nUGQlkM0La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EBjlZqcmBOsHHh5KyiVzh4lbnDIiJiIxFVH/y16jkGsAqUIJpAmK2b75SpMYH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\nwNq1a+Hj42O/j7e3N6qqqhy/xUREREREDnTLgXjGjBmYMWN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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mc.ac.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/tutorials/forecast_to_power.ipynb b/docs/tutorials/forecast_to_power.ipynb new file mode 100644 index 0000000000..777872b12c --- /dev/null +++ b/docs/tutorials/forecast_to_power.ipynb @@ -0,0 +1,1078 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Forecast to Power Tutorial\n", + "\n", + "This tutorial will walk through the process of going from Unidata forecast model data to AC power using the SAPM.\n", + "\n", + "Table of contents:\n", + "1. [Setup](#Setup)\n", + "2. [Load Forecast data](#Load-Forecast-data)\n", + "2. [Calculate modeling intermediates](#Calculate-modeling-intermediates)\n", + "2. [DC power using SAPM](#DC-power-using-SAPM)\n", + "2. [AC power using SAPM](#AC-power-using-SAPM)\n", + "\n", + "This tutorial has been tested against the following package versions:\n", + "* Python 3.4.3\n", + "* IPython 4.0.1\n", + "* pandas 0.18.0\n", + "* matplotlib 1.5.1\n", + "* netcdf4 1.2.1\n", + "* siphon 0.3.2\n", + "\n", + "It should work with other Python and Pandas versions. It requires pvlib >= 0.3.0 and IPython >= 3.0.\n", + "\n", + "Authors:\n", + "* Derek Groenendyk (@moonraker), University of Arizona, November 2015\n", + "* Will Holmgren (@wholmgren), University of Arizona, November 2015, January 2016, April 2016" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are just your standard interactive scientific python imports that you'll get very used to using." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# built-in python modules\n", + "import datetime\n", + "import inspect\n", + "import os\n", + "\n", + "# scientific python add-ons\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# plotting stuff\n", + "# first line makes the plots appear in the notebook\n", + "%matplotlib inline \n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "# seaborn makes your plots look better\n", + "try:\n", + " import seaborn as sns\n", + " sns.set(rc={\"figure.figsize\": (12, 6)})\n", + " sns.set_color_codes()\n", + "except ImportError:\n", + " print('We suggest you install seaborn using conda or pip and rerun this cell')\n", + "\n", + "# finally, we import the pvlib library\n", + "from pvlib import solarposition,irradiance,atmosphere,pvsystem\n", + "from pvlib.forecast import GFS, NAM, NDFD, RAP, HRRR" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Forecast data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "pvlib forecast module only includes several models. To see the full list of forecast models visit the Unidata website:\n", + "\n", + "http://www.unidata.ucar.edu/data/#tds" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Choose a location.\n", + "# Tucson, AZ\n", + "latitude = 32.2\n", + "longitude = -110.9\n", + "tz = 'US/Mountain'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define some PV system parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "surface_tilt = 30\n", + "surface_azimuth = 180 # pvlib uses 0=North, 90=East, 180=South, 270=West convention\n", + "albedo = 0.2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "start = pd.Timestamp(datetime.date.today(), tz=tz) # today's date\n", + "end = start + pd.Timedelta(days=7) # 7 days from today" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Define forecast model\n", + "fm = GFS()\n", + "#fm = NAM()\n", + "#fm = NDFD()\n", + "#fm = RAP()\n", + "#fm = HRRR()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Retrieve data\n", + "forecast_data = fm.get_processed_data(latitude, longitude, start, end)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the downloaded version of the forecast data." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " temperature wind_speed ghi dni \\\n", + "2016-04-03 06:00:00-06:00 13.149994 4.970030 0.000000 0.000000 \n", + "2016-04-03 09:00:00-06:00 11.250000 4.719343 334.661071 651.734481 \n", + "2016-04-03 12:00:00-06:00 9.850006 5.627148 909.195203 968.970216 \n", + "2016-04-03 15:00:00-06:00 18.750000 4.740433 897.257250 965.338091 \n", + "2016-04-03 18:00:00-06:00 36.350006 3.536001 309.082267 624.337268 \n", + "\n", + " dhi total_clouds low_clouds mid_clouds \\\n", + "2016-04-03 06:00:00-06:00 0.000000 0.0 0.0 0.0 \n", + "2016-04-03 09:00:00-06:00 82.893092 0.0 0.0 0.0 \n", + "2016-04-03 12:00:00-06:00 99.750194 0.0 0.0 0.0 \n", + "2016-04-03 15:00:00-06:00 99.571346 0.0 0.0 0.0 \n", + "2016-04-03 18:00:00-06:00 81.289376 0.0 0.0 0.0 \n", + "\n", + " high_clouds \n", + "2016-04-03 06:00:00-06:00 0.0 \n", + "2016-04-03 09:00:00-06:00 0.0 \n", + "2016-04-03 12:00:00-06:00 0.0 \n", + "2016-04-03 15:00:00-06:00 0.0 \n", + "2016-04-03 18:00:00-06:00 0.0 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forecast_data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a ``pandas DataFrame`` object. It has a lot of great properties that are beyond the scope of our tutorials." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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PlCU7UH7ymxnkuOJ2xpo3ItTgRvAC+zxmFV9ASeH5rzfuhttpwz898pphKotm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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "forecast_data['temperature'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the GHI data. Most pvlib forecast models derive this data from the weather models' cloud clover data." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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A66odmZ2dxT/90z9hZGQEAFbsPtIq0pbUIJcDQCrTNK2UATdT+U7cXW8ERERE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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ghi = forecast_data['ghi']\n", + "ghi.plot()\n", + "plt.ylabel('Irradiance ($W/m^{-2}$)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate modeling intermediates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we can calculate power for all the forecast times, we will need to calculate:\n", + "* solar position \n", + "* extra terrestrial radiation\n", + "* airmass\n", + "* angle of incidence\n", + "* POA sky and ground diffuse radiation\n", + "* cell and module temperatures\n", + "\n", + "The approach here follows that of the pvlib tmy_to_power notebook. You will find more details regarding this approach and the values being calculated in that notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solar position" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the solar position for all times in the forecast data. \n", + "\n", + "The default solar position algorithm is based on Reda and Andreas (2004). Our implementation is pretty fast, but you can make it even faster if you install [``numba``](http://numba.pydata.org/#installing) and use add ``method='nrel_numba'`` to the function call below." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# retrieve time and location parameters\n", + "time = forecast_data.index\n", + "a_point = fm.location" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "solpos = a_point.get_solarposition(time)\n", + "#solpos.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The funny looking jump in the azimuth is just due to the coarse time sampling in the TMY file." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DNI ET\n", + "\n", + "Calculate extra terrestrial radiation. This is needed for many plane of array diffuse irradiance models." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dni_extra = irradiance.extraradiation(fm.time)\n", + "dni_extra = pd.Series(dni_extra, index=fm.time)\n", + "\n", + "#dni_extra.plot()\n", + "#plt.ylabel('Extra terrestrial radiation ($W/m^{-2}$)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Airmass\n", + "\n", + "Calculate airmass. Lots of model options here, see the ``atmosphere`` module tutorial for more details." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "airmass = atmosphere.relativeairmass(solpos['apparent_zenith'])\n", + "\n", + "#airmass.plot()\n", + "#plt.ylabel('Airmass')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The funny appearance is due to aliasing and setting invalid numbers equal to ``NaN``. Replot just a day or two and you'll see that the numbers are right." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### POA sky diffuse" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the Hay Davies model to calculate the plane of array diffuse sky radiation. See the ``irradiance`` module tutorial for comparisons of different models." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "poa_sky_diffuse = irradiance.haydavies(surface_tilt, surface_azimuth,\n", + " forecast_data['dhi'], forecast_data['dni'], dni_extra,\n", + " solpos['apparent_zenith'], solpos['azimuth'])\n", + "#poa_sky_diffuse.plot()\n", + "#plt.ylabel('Irradiance ($W/m^{-2}$)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### POA ground diffuse\n", + "\n", + "Calculate ground diffuse. We specified the albedo above. You could have also provided a string to the ``surface_type`` keyword argument." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "poa_ground_diffuse = irradiance.grounddiffuse(surface_tilt, ghi, albedo=albedo)\n", + "\n", + "#poa_ground_diffuse.plot()\n", + "#plt.ylabel('Irradiance ($W/m^{-2}$)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### AOI\n", + "\n", + "Calculate AOI" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "aoi = irradiance.aoi(surface_tilt, surface_azimuth, solpos['apparent_zenith'], solpos['azimuth'])\n", + "\n", + "#aoi.plot()\n", + "#plt.ylabel('Angle of incidence (deg)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that AOI has values greater than 90 deg. This is ok." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### POA total\n", + "\n", + "Calculate POA irradiance" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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sEEIIkR6zI6WfknhMkQC8gyQDnl9xI4DicqE6HGm/V/pfCCGESE+mO1A3f48p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\n5ZLX9HNC8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RDaq4vQX12E/mND1IC7idjYWPz8/NDpdH12Rr1dtZYt\nW6bm0CY9Qnt1Efqri9BfPYT26iL0Vxeh/9gQnTAFAoFAIBAIBIIJRJSguBlZltUewhWL0F5dhP7q\nIvRXD6G9ugj91UXoPzpEBlwgEAgEAoFAIJhARAZcIBAIBAKBQCCYQEQALhAIBAKBQCAQTCAiABcI\nBAKBQCAQCCYQEYALBAKBQCAQCAQTiAjABQKBQCAQCASCCeT/AXoxYVNlId6oAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "poa_irrad = irradiance.globalinplane(aoi, forecast_data['dni'], poa_sky_diffuse, poa_ground_diffuse)\n", + "\n", + "poa_irrad.plot()\n", + "plt.ylabel('Irradiance ($W/m^{-2}$)')\n", + "plt.title('POA Irradiance')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cell and module temperature\n", + "\n", + "Calculate pv cell and module temperature" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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azYY6BweODdM5OCFBszhTup553ZygeTA4zDeOPYoGfHDXLTTYVyY70+isQEup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FOYHvgj0qpz2bne2swzy1JYX7+Z5JgkZmRN07VNw11ynJW81Oh8QjIUEzICHbf6Lv0X9XlzXTU+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\nQ1MIIUa6xeUXYHQlY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Llc5Ofn9/2/96JGVVXJP0h6P3tKS0sxGAxkZ2ezcuVKjEYjn3zy\nCVOnTqWsrIyJEyfKuR8CR+bvdDqxWq14PB4Apk+fzmOPPcb27dspKyvD4/FgMpkk/yBav349WVlZ\nFBUVUVNTQ1JSEna7ncTERO677z4WLFjAl19+yfTp0yP2/Fe0Y2+HilPyeDz84Q9/oKKignnz5vHB\nBx/w9NNPk5qaytKlS7nwwgu5++67aWho4K233iIzM7NvgLvD4cBqtQL0Pb4YiSeNXo7NfsWKFfzu\nd7+jsrKS3bt38/3vfx+DwcDrr7/O8uXLeemll/q2leyH7sj8586dy8cff8yvfvUrnn76aZxOJ488\n8ghZWVm8+uqrLF++nJdffrlvW8l/6E702fPUU0+RnZ2NwWDgvvvuo6SkhNtvv/24oWGS/9Cc6Nx/\n4okn8Hg8/PKXv6Szs5N7772XHTt28Oqrr7JixYq+bSX7oTs2/08++YTHHnuM3/72t0ycOJHi4mJ2\n7NiBw+HAYrFwzz339G0r+QfPj370I7q6uvjLX/6Cx+PhnnvuYdGiRZx77rkYjUaWLl3Kvn37ePjh\nh/u2ibT85U7zAHV3d7Nt2zaee+45jEYjXV1dvPXWW+Tn57Ns2TK2bNnCv/3bv/GHP/yBgwcPMnr0\n6L5te0+c3jvPYmCOzd5ut/Pee+9x3nnnMW/ePLxeLwaDgUmTJtHQ0AD864pWsh+6Y/Pv7Oxk3bp1\nzJkzh5UrV1JdXU1WVhZTpkyhtrb2qG0l/6E70WfPm2++ydVXX012djaLFi1iw4YNuFyu48bVSv5D\nc6Lsly9fzlVXXUVRURFnnXUWc+bMYfz48dTW1h71M5Dsh+5Enz0bNmzgu9/9Lh6Ph/fff59rrrkG\np9NJd3c3IJ/9wbZr1y4OHTrEgQMHePfdd7niiitYsGAB7733HgUFBRQWFpKamorRGOhWRmr+MhFw\nADRNIzY2lo0bN+J0OikpKWHcuHGsXLmSefPmUVhYyB133MGkSZOwWq00NDQwZcqU4/YzYkqrDCMn\ny/6DDz4gPz+fjo4OXnjhBTZs2MArr7zCWWedRXFx8XFXtJL94Jws/3feeYdzzjkHo9HI6tWr2bBh\nAy+++CLnnHMOpaWlx+1H8h+cU332jB49mtzcXOrq6ti7dy95eXl9j0GPJfkP3Mmy/+ijjygsLGTL\nli20t7ezefNm/vjHP3L22WdzxhlnHLcfyX5wTpb/22+/TWlpKdOmTcNqtXLgwAFeeeUVZs2aRUFB\ngXz2B1lbWxuXXnopZ511Fk8++SQ33HADEyZMYNeuXWzZsoWNGzfyzjvvMHfuXMaPHx+x+Uun+RQ0\nTTvqMaeiKLjdbrq7u9m9ezfjx48nMzOTyspKNm7cyJ133onJZMLv91NaWnrCDrM4Paeb/d69e/n6\n66+55pprSEhIoLGxkbvvvpsZM2bo/B2MbAM597/44gvuvfdeiouLcTgc3HnnncyePVvn72BkO938\n9+3bx/r167n44otJSEigtbWVGTNmYDKZdP4ORq6BnPtbt27loYceIiYmhurqan784x8zd+5cnb+D\nkW0gn/1ffPEFCxYsoLGxkY0bN/LAAw8wdepUnb+Dke3Y/HslJydjsVgYO3Ysa9eupaamhpkzZ1JW\nVsa4ceNoaGjg7rvv5swzz9Sp5eEhneZT6B17s3//frZs2cKYMWMwm819X9u5cyczZ85EVVUaGxuZ\nPXs2qqoedbKd6OQT/Tvd7AHq6uqYNWsWubm5zJo1i8TExGG1Vv1INJBz/+DBg8yYMYO0tDSmTJki\n+QfBQM7/5uZmZsyYQXx8PJMnT5YO8xAN5Nzfv38/c+bMITc3l7lz58q5HwQDOffr6+uZPXs2eXl5\nnH/++SQlJUn+Q3Si/A0GA6qq9g29KCsr49FHH+Wyyy4jLS2N1NRUysvLo+L8j4z75UHk8/n6/q1p\nGm+88Qbf//73iY+P7zthiouLueKKK1i/fj0//elP+c///E/mzJlzwvE6kXrihMJgs587dy5ms/mo\nbY+9eBH9G8q5L/kPXTDzFwMzlM+eIy9SequVyLk/MEPJv/d1kPwH61T5H3sR7vf7KSgoYOHChezb\nt++o16Lhsz/qq2ccW5qpV01NDTk5OSxbtozly5fz+uuvAxz1vpaWFvbv309paSlxcXG6tH8kk+z1\nJfnrS/LXj2SvL8lfXwPN/8gn5sduE22ifniGx+PBYDD0nRBVVVX85Cc/4aOPPqK+vp6SkhJ8Ph+N\njY2UlpYedfJYrVays7MxmUz4fL6oPpEGQ7LXl+SvL8lfP5K9viR/fQ0l/2gffhq1Z5vP5+M3v/kN\nd9xxBzU1NQA8++yzPPXUU9x000089dRTWCyWvuoAa9asoaWl5aS/oJFQbzaPBgAAAkZJREFUSiVc\nJHt9Sf76kvz1I9nrS/LXV7Dzj7YOM0Rxp1nTNGpqakhPT2fp0qWsWLGC8ePH43A4KCkpITU1lbPP\nPpuEhARSU1MpKCjg4MGDejc7Ikj2+pL89SX560ey15fkry/Jf+iistPs9/sxGo1MnjyZ+Ph4vve9\n77F06VJsNhs+n4/PP/8cv9/Pxo0b8fl8FBcXc9ddd52w9qYYGMleX5K/viR//Uj2+pL89SX5B0dU\nrgjY+6ghPz+fxMREXC4XDoeD1atXs3XrVtrb2/noo48wm83cdtttQOAxUDSO3wk2yV5fkr++JH/9\nSPb6kvz1JfkHR1RPBKysrOTJJ5/kwIED3Hjjjdxxxx3U19ezZ88ecnJyeOKJJ0hPT+87aeTECR7J\nXl+Sv74kf/1I9vqS/PUl+Q+RFsV6enq0JUuWaHv27On7msvl0hobG7Vvf/vb2hdffKH5/X4dWxi5\nJHt9Sf76kvz1I9nrS/LXl+Q/NFE5prlXa2srSUlJxMXF9RX3VlWVzMxM7rjjDoqKiuQqK0Qke31J\n/vqS/PUj2etL8teX5D80UTmmuVd2djYWiwWj0dhXuqZ3daHzzz9fz6ZFPMleX5K/viR//Uj2+pL8\n9SX5D03UrwgohBBCCCFEf6J6eEYvv9+vdxOilmSvL8lfX5K/fiR7fUn++pL8B0fuNAshhBBCCNEP\nudMshBBCCCFEP6TTLIQQQgghRD+k0yyEEEIIIUQ/pNMshBBCCCFEP6TTLIQQQgghRD+k0yyEEEII\nIUQ//j8i5AmDFxIK7gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "temperature = forecast_data['temperature']\n", + "wnd_spd = forecast_data['wind_speed']\n", + "pvtemps = pvsystem.sapm_celltemp(poa_irrad['poa_global'], wnd_spd, temperature)\n", + "\n", + "pvtemps.plot()\n", + "plt.ylabel('Temperature (C)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DC power using SAPM" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get module data from the web." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sandia_modules = pvsystem.retrieve_sam(name='SandiaMod')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Choose a particular module" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Vintage 2009\n", + "Area 1.701\n", + "Material c-Si\n", + "Cells_in_Series 96\n", + "Parallel_Strings 1\n", + "Isco 5.09115\n", + "Voco 59.2608\n", + "Impo 4.54629\n", + "Vmpo 48.3156\n", + "Aisc 0.000397\n", + "Aimp 0.000181\n", + "C0 1.01284\n", + "C1 -0.0128398\n", + "Bvoco -0.21696\n", + "Mbvoc 0\n", + "Bvmpo -0.235488\n", + "Mbvmp 0\n", + "N 1.4032\n", + "C2 0.279317\n", + "C3 -7.24463\n", + "A0 0.928385\n", + "A1 0.068093\n", + "A2 -0.0157738\n", + "A3 0.0016606\n", + "A4 -6.93e-05\n", + "B0 1\n", + "B1 -0.002438\n", + "B2 0.0003103\n", + "B3 -1.246e-05\n", + "B4 2.11e-07\n", + "B5 -1.36e-09\n", + "DTC 3\n", + "FD 1\n", + "A -3.40641\n", + "B -0.0842075\n", + "C4 0.996446\n", + "C5 0.003554\n", + "IXO 4.97599\n", + "IXXO 3.18803\n", + "C6 1.15535\n", + "C7 -0.155353\n", + "Notes Source: Sandia National Laboratories Updated 9...\n", + "Name: Canadian_Solar_CS5P_220M___2009_, dtype: object" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sandia_module = sandia_modules.Canadian_Solar_CS5P_220M___2009_\n", + "sandia_module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the SAPM using the parameters we calculated above." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Po61J/dPv8o84WCM9K2VutPhA+z0OTIQTql+XiIioXsmyjEgshbYGt+rXLj6I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\nNuKv//qvkUqlsHz5cmzevLmq16jV46mLLSoqL9A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eyqyJFnv20R1kyHLE/xdr0kX7Y6uJ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sapm_out = pvsystem.sapm(sandia_module, poa_irrad.poa_direct, poa_irrad.poa_diffuse,\n", + " pvtemps['temp_cell'], airmass, aoi)\n", + "#print(sapm_out.head())\n", + "\n", + "sapm_out[['p_mp']].plot()\n", + "plt.ylabel('DC Power (W)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## AC power using SAPM" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the inverter database from the web" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sapm_inverters = pvsystem.retrieve_sam('sandiainverter')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Choose a particular inverter" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Vac 208.000000\n", + "Paco 250.000000\n", + "Pdco 259.522050\n", + "Vdco 40.242603\n", + "Pso 1.771614\n", + "C0 -0.000025\n", + "C1 -0.000090\n", + "C2 0.000669\n", + "C3 -0.018900\n", + "Pnt 0.020000\n", + "Vdcmax 65.000000\n", + "Idcmax 10.000000\n", + "Mppt_low 20.000000\n", + "Mppt_high 50.000000\n", + "Name: ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_, dtype: float64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sapm_inverter = sapm_inverters['ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_']\n", + "sapm_inverter" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0, 250.0)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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j/Xd/93cQRRGPPvooPvjBD2Lr1q1GrE1X6hZHUOM+xqpSCzzW6S7IiNIOO94Z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\nu8UAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p_ac = pvsystem.snlinverter(sapm_inverter, sapm_out.v_mp, sapm_out.p_mp)\n", + "\n", + "p_ac.plot()\n", + "plt.ylabel('AC Power (W)')\n", + "plt.ylim(0, None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot just a few days." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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wxYI4QvUOOtFnd2F9gQWGqMh9mSuLk3GsdQg1TTYWxEREODuqee7oQbiRJAlZqSZkpZrw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\n+NzGDPQPT+Lnz9Zjyu0THSmsRcS7x7v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HdCG+rra2lszMTGMuiH9nYGDAa989\n6w6Dg4NDTygyRWlpKe3t7RQWFt6RPx2PBJOvfZOzg/Jfp+43q/vV+95z7XtkIRYxkcvlMvJVIRER\nU6n3vYcWYhERERExmvc/D1BERERE5BZoIRYRERERo2khFhERERGjaSEWEREREaNpIRYRERERo2kh\nFhERERGj/R9yUjOHbWhErAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p_ac[start:start+pd.Timedelta(days=2)].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some statistics on the AC power" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 57.000000\n", + "mean 41.669520\n", + "std 60.813727\n", + "min -0.020000\n", + "25% -0.020000\n", + "50% -0.020000\n", + "75% 67.355114\n", + "max 203.601845\n", + "dtype: float64" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_ac.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<3 * Hours>" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_ac.index.freq" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "7125.4878479372464" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# integrate power to find energy yield over the forecast period\n", + "p_ac.sum() * 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/pvlib/__init__.py b/pvlib/__init__.py index 6311f9b7be..d8b0657822 100644 --- a/pvlib/__init__.py +++ b/pvlib/__init__.py @@ -4,6 +4,7 @@ from pvlib import tools from pvlib import atmosphere from pvlib import clearsky +# from pvlib import forecast from pvlib import irradiance from pvlib import location from pvlib import solarposition diff --git a/pvlib/atmosphere.py b/pvlib/atmosphere.py index 5f86036012..831597a6fd 100644 --- a/pvlib/atmosphere.py +++ b/pvlib/atmosphere.py @@ -242,3 +242,35 @@ def relativeairmass(zenith, model='kastenyoung1989'): am = np.nan if z > 90 else am return am + + +def transmittance(cloud_prct): + ''' + Calculates transmittance. + + Based on observations by Liu and Jordan, 1960 as well as + Gates 1980. + + Parameters + ---------- + cloud_prct: float or int + Percentage of clouds covering the sky. + + Returns + ------- + value: float + Shortwave radiation transmittance. + + References + ---------- + [1] Campbell, G. S., J. M. Norman (1998) An Introduction to + Environmental Biophysics. 2nd Ed. New York: Springer. + + [2] Gates, D. M. (1980) Biophysical Ecology. New York: Springer Verlag. + + [3] Liu, B. Y., R. C. Jordan, (1960). "The interrelationship and + characteristic distribution of direct, diffuse, and total solar + radiation". Solar Energy 4:1-19 + ''' + + return ((100.0 - cloud_prct) / 100.0) * 0.75 diff --git a/pvlib/forecast.py b/pvlib/forecast.py new file mode 100644 index 0000000000..e888232e04 --- /dev/null +++ b/pvlib/forecast.py @@ -0,0 +1,946 @@ +''' +The 'forecast' module contains class definitions for +retreiving forecasted data from UNIDATA Thredd servers. +''' +import datetime +from netCDF4 import num2date +import numpy as np +import pandas as pd +from requests.exceptions import HTTPError +from xml.etree.ElementTree import ParseError + +from pvlib.location import Location +from pvlib.irradiance import liujordan +from siphon.catalog import TDSCatalog +from siphon.ncss import NCSS + + +class ForecastModel(object): + """ + An object for querying and holding forecast model information for + use within the pvlib library. + + Simplifies use of siphon library on a THREDDS server. + + Parameters + ---------- + model_type: string + UNIDATA category in which the model is located. + model_name: string + Name of the UNIDATA forecast model. + set_type: string + Model dataset type. + + Attributes + ---------- + access_url: string + URL specifying the dataset from data will be retrieved. + base_tds_url : string + The top level server address + catalog_url : string + The url path of the catalog to parse. + data: pd.DataFrame + Data returned from the query. + data_format: string + Format of the forecast data being requested from UNIDATA. + dataset: Dataset + Object containing information used to access forecast data. + dataframe_variables: list + Model variables that are present in the data. + datasets_list: list + List of all available datasets. + fm_models: Dataset + TDSCatalog object containing all available + forecast models from UNIDATA. + fm_models_list: list + List of all available forecast models from UNIDATA. + latitude: list + A list of floats containing latitude values. + location: Location + A pvlib Location object containing geographic quantities. + longitude: list + A list of floats containing longitude values. + lbox: boolean + Indicates the use of a location bounding box. + ncss: NCSS object + NCSS model_name: string + Name of the UNIDATA forecast model. + model: Dataset + A dictionary of Dataset object, whose keys are the name of the + dataset's name. + model_url: string + The url path of the dataset to parse. + modelvariables: list + Common variable names that correspond to queryvariables. + query: NCSS query object + NCSS object used to complete the forecast data retrival. + queryvariables: list + Variables that are used to query the THREDDS Data Server. + time: DatetimeIndex + Time range. + variables: dict + Defines the variables to obtain from the weather + model and how they should be renamed to common variable names. + units: dict + Dictionary containing the units of the standard variables + and the model specific variables. + vert_level: float or integer + Vertical altitude for query data. + """ + + access_url_key = 'NetcdfSubset' + catalog_url = 'http://thredds.ucar.edu/thredds/catalog.xml' + base_tds_url = catalog_url.split('/thredds/')[0] + data_format = 'netcdf' + vert_level = 100000 + + units = { + 'temperature': 'C', + 'wind_speed': 'm/s', + 'ghi': 'W/m^2', + 'ghi_raw': 'W/m^2', + 'dni': 'W/m^2', + 'dhi': 'W/m^2', + 'total_clouds': '%', + 'low_clouds': '%', + 'mid_clouds': '%', + 'high_clouds': '%'} + + def __init__(self, model_type, model_name, set_type): + self.model_type = model_type + self.model_name = model_name + self.set_type = set_type + self.catalog = TDSCatalog(self.catalog_url) + self.fm_models = TDSCatalog(self.catalog.catalog_refs[model_type].href) + self.fm_models_list = sorted(list(self.fm_models.catalog_refs.keys())) + + try: + model_url = self.fm_models.catalog_refs[model_name].href + except ParseError: + raise ParseError(self.model_name + ' model may be unavailable.') + + try: + self.model = TDSCatalog(model_url) + except HTTPError: + raise HTTPError(self.model_name + ' model may be unavailable.') + + self.datasets_list = list(self.model.datasets.keys()) + self.set_dataset() + + def __repr__(self): + return '{}, {}'.format(self.model_name, self.set_type) + + def set_dataset(self): + ''' + Retrieves the designated dataset, creates NCSS object, and + creates a NCSS query object. + ''' + + keys = list(self.model.datasets.keys()) + labels = [item.split()[0].lower() for item in keys] + if self.set_type == 'best': + self.dataset = self.model.datasets[keys[labels.index('best')]] + elif self.set_type == 'latest': + self.dataset = self.model.datasets[keys[labels.index('latest')]] + elif self.set_type == 'full': + self.dataset = self.model.datasets[keys[labels.index('full')]] + + self.access_url = self.dataset.access_urls[self.access_url_key] + self.ncss = NCSS(self.access_url) + self.query = self.ncss.query() + + def set_query_latlon(self): + ''' + Sets the NCSS query location latitude and longitude. + ''' + + if (isinstance(self.longitude, list) and + isinstance(self.latitude, list)): + self.lbox = True + # west, east, south, north + self.query.lonlat_box(self.latitude[0], self.latitude[1], + self.longitude[0], self.longitude[1]) + else: + self.lbox = False + self.query.lonlat_point(self.longitude, self.latitude) + + def set_location(self, time): + ''' + Sets the location for the query. + + Parameters + ---------- + time: datetime or DatetimeIndex + Time range of the query. + ''' + if isinstance(time, datetime.datetime): + tzinfo = time.tzinfo + else: + tzinfo = time.tz + + if tzinfo is None: + self.location = Location(self.latitude, self.longitude) + else: + self.location = Location(self.latitude, self.longitude, tz=tzinfo) + + def get_data(self, latitude, longitude, start, end, + vert_level=None, query_variables=None, + close_netcdf_data=True): + """ + Submits a query to the UNIDATA servers using Siphon NCSS and + converts the netcdf data to a pandas DataFrame. + + Parameters + ---------- + latitude: float + The latitude value. + longitude: float + The longitude value. + start: datetime or timestamp + The start time. + end: datetime or timestamp + The end time. + vert_level: None, float or integer + Vertical altitude of interest. + variables: None or list + If None, uses self.variables. + close_netcdf_data: bool + Controls if the temporary netcdf data file should be closed. + Set to False to access the raw data. + + Returns + ------- + forecast_data : DataFrame + column names are the weather model's variable names. + """ + if vert_level is not None: + self.vert_level = vert_level + + if query_variables is None: + self.query_variables = list(self.variables.values()) + else: + self.query_variables = query_variables + + self.latitude = latitude + self.longitude = longitude + self.set_query_latlon() # modifies self.query + self.set_location(start) + + self.start = start + self.end = end + self.query.time_range(self.start, self.end) + + self.query.vertical_level(self.vert_level) + self.query.variables(*self.query_variables) + self.query.accept(self.data_format) + + self.netcdf_data = self.ncss.get_data(self.query) + + # might be better to go to xarray here so that we can handle + # higher dimensional data for more advanced applications + self.data = self._netcdf2pandas(self.netcdf_data, self.query_variables) + + if close_netcdf_data: + self.netcdf_data.close() + + return self.data + + def process_data(self, data, **kwargs): + """ + Defines the steps needed to convert raw forecast data + into processed forecast data. Most forecast models implement + their own version of this method which also call this one. + + Parameters + ---------- + data: DataFrame + Raw forecast data + + Returns + ------- + data: DataFrame + Processed forecast data. + """ + data = self.rename(data) + return data + + def get_processed_data(self, *args, **kwargs): + """ + Get and process forecast data. + + Parameters + ---------- + *args: positional arguments + Passed to get_data + **kwargs: keyword arguments + Passed to get_data and process_data + + Returns + ------- + data: DataFrame + Processed forecast data + """ + return self.process_data(self.get_data(*args, **kwargs), **kwargs) + + def rename(self, data, variables=None): + """ + Renames the columns according the variable mapping. + + Parameters + ---------- + data: DataFrame + variables: None or dict + If None, uses self.variables + + Returns + ------- + data: DataFrame + Renamed data. + """ + if variables is None: + variables = self.variables + return data.rename(columns={y: x for x, y in variables.items()}) + + def _netcdf2pandas(self, netcdf_data, query_variables): + """ + Transforms data from netcdf to pandas DataFrame. + + Parameters + ---------- + data: netcdf + Data returned from UNIDATA NCSS query. + query_variables: list + The variables requested. + + Returns + ------- + pd.DataFrame + """ + # set self.time + try: + time_var = 'time' + self.set_time(netcdf_data.variables[time_var]) + except KeyError: + # which model does this dumb thing? + time_var = 'time1' + self.set_time(netcdf_data.variables[time_var]) + + data_dict = {key: data[:].squeeze() for key, data in + netcdf_data.variables.items() if key in query_variables} + + return pd.DataFrame(data_dict, index=self.time) + + def set_time(self, time): + ''' + Converts time data into a pandas date object. + + Parameters + ---------- + time: netcdf + Contains time information. + + Returns + ------- + pandas.DatetimeIndex + ''' + times = num2date(time[:].squeeze(), time.units) + self.time = pd.DatetimeIndex(pd.Series(times), tz=self.location.tz) + + def cloud_cover_to_irradiance(self, cloud_cover): + """ + Parameters + ---------- + cloud_cover: Series + + Returns + ------- + irradiance: DataFrame + keys include ghi, dni, dhi + """ + # in principle, get_solarposition could use the forecast + # pressure, temp, etc., but the cloud cover forecast is not + # accurate enough to justify using these minor corrections + self.solar_position = self.location.get_solarposition(self.time) + + rads = ['dni', 'dhi', 'ghi'] + new_rads = liujordan(self.solar_position['apparent_zenith'], + cloud_cover) + new_rads = new_rads.fillna(0) + + return new_rads + + def kelvin_to_celsius(self, temperature): + """ + Converts Kelvin to celsius. + + Parameters + ---------- + temperature: numeric + + Returns + ------- + temperature: numeric + """ + return temperature - 273.15 + + def isobaric_to_ambient_temperature(self, data): + """ + Calculates temperature from isobaric temperature. + + Parameters + ---------- + data: DataFrame + Must contain columns pressure, temperature_iso, + temperature_dew_iso. Input temperature in K. + + Returns + ------- + temperature : Series + Temperature in K + """ + + P = data['pressure'] / 100.0 + Tiso = data['temperature_iso'] + Td = data['temperature_dew_iso'] - 273.15 + + # saturation water vapor pressure + e = 6.11 * 10**((7.5 * Td) / (Td + 273.3)) + + # saturation water vapor mixing ratio + w = 0.622 * (e / (P - e)) + + T = Tiso - ((2.501 * 10.**6) / 1005.7) * w + + return T + + def uv_to_speed(self, data): + """ + Computes wind speed from wind components. + + Parameters + ---------- + data : DataFrame + Must contain the columns 'wind_speed_u' and 'wind_speed_v'. + + Returns + ------- + wind_speed : Series + """ + wind_speed = np.sqrt(data['wind_speed_u']**2 + data['wind_speed_v']**2) + + return wind_speed + + def gust_to_speed(self, data, scaling=1/1.4): + """ + Computes standard wind speed from gust. + Very approximate and location dependent. + + Parameters + ---------- + data : DataFrame + Must contain the column 'wind_speed_gust'. + + Returns + ------- + wind_speed : Series + """ + wind_speed = data['wind_speed_gust'] * scaling + + return wind_speed + + +class GFS(ForecastModel): + """ + Subclass of the ForecastModel class representing GFS + forecast model. + + Model data corresponds to 0.25 degree resolution forecasts. + + Parameters + ---------- + resolution: string + Resolution of the model, either 'half' or 'quarter' degree. + set_type: string + Type of model to pull data from. + + Attributes + ---------- + dataframe_variables: list + Common variables present in the final set of data. + model: string + Name of the UNIDATA forecast model. + model_type: string + UNIDATA category in which the model is located. + variables: dict + Defines the variables to obtain from the weather + model and how they should be renamed to common variable names. + units: dict + Dictionary containing the units of the standard variables + and the model specific variables. + """ + + _resolutions = ['Half', 'Quarter'] + + def __init__(self, resolution='half', set_type='best'): + model_type = 'Forecast Model Data' + + resolution = resolution.title() + if resolution not in self._resolutions: + raise ValueError('resolution must in {}'.format(self._resolutions)) + + model = 'GFS {} Degree Forecast'.format(resolution) + + self.variables = { + 'temperature': 'Temperature_surface', + 'wind_speed_gust': 'Wind_speed_gust_surface', + 'wind_speed_u': 'u-component_of_wind_isobaric', + 'wind_speed_v': 'v-component_of_wind_isobaric', + 'total_clouds': 'Total_cloud_cover_entire_atmosphere_Mixed_intervals_Average', + 'low_clouds': 'Total_cloud_cover_low_cloud_Mixed_intervals_Average', + 'mid_clouds': 'Total_cloud_cover_middle_cloud_Mixed_intervals_Average', + 'high_clouds': 'Total_cloud_cover_high_cloud_Mixed_intervals_Average', + 'boundary_clouds': 'Total_cloud_cover_boundary_layer_cloud_Mixed_intervals_Average', + 'convect_clouds': 'Total_cloud_cover_convective_cloud', + 'ghi_raw': 'Downward_Short-Wave_Radiation_Flux_surface_Mixed_intervals_Average', } + + self.output_variables = [ + 'temperature', + 'wind_speed', + 'ghi', + 'dni', + 'dhi', + 'total_clouds', + 'low_clouds', + 'mid_clouds', + 'high_clouds',] + + super(GFS, self).__init__(model_type, model, set_type) + + def process_data(self, data, cloud_cover='total_clouds', **kwargs): + """ + Defines the steps needed to convert raw forecast data + into processed forecast data. + + Parameters + ---------- + data: DataFrame + Raw forecast data + cloud_cover: str + The type of cloud cover used to infer the irradiance. + + Returns + ------- + data: DataFrame + Processed forecast data. + """ + data = super(GFS, self).process_data(data, **kwargs) + data['temperature'] = self.kelvin_to_celsius(data['temperature']) + data['wind_speed'] = self.uv_to_speed(data) + data = data.join(self.cloud_cover_to_irradiance(data[cloud_cover]), + how='outer') + return data.ix[:, self.output_variables] + + +class HRRR_ESRL(ForecastModel): + """ + Subclass of the ForecastModel class representing + NOAA/GSD/ESRL's HRRR forecast model. + This is not an operational product. + + Model data corresponds to NOAA/GSD/ESRL HRRR CONUS 3km resolution + surface forecasts. + + Parameters + ---------- + set_type: string + Type of model to pull data from. + + Attributes + ---------- + dataframe_variables: list + Common variables present in the final set of data. + model: string + Name of the UNIDATA forecast model. + model_type: string + UNIDATA category in which the model is located. + variables: dict + Defines the variables to obtain from the weather + model and how they should be renamed to common variable names. + units: dict + Dictionary containing the units of the standard variables + and the model specific variables. + """ + + def __init__(self, set_type='best'): + import warnings + warnings.warn('HRRR_ESRL is an experimental model and is not always available.') + + model_type = 'Forecast Model Data' + model = 'GSD HRRR CONUS 3km surface' + + self.variables = { + 'temperature': 'Temperature_surface', + 'wind_speed_gust': 'Wind_speed_gust_surface', + 'total_clouds': 'Total_cloud_cover_entire_atmosphere', + 'low_clouds': 'Low_cloud_cover_UnknownLevelType-214', + 'mid_clouds': 'Medium_cloud_cover_UnknownLevelType-224', + 'high_clouds': 'High_cloud_cover_UnknownLevelType-234', + 'ghi_raw': 'Downward_short-wave_radiation_flux_surface', } + + self.output_variables = [ + 'temperature', + 'wind_speed' + 'ghi_raw', + 'ghi', + 'dni', + 'dhi', + 'total_clouds', + 'low_clouds', + 'mid_clouds', + 'high_clouds',] + + super(HRRR_ESRL, self).__init__(model_type, model, set_type) + + def process_data(self, data, cloud_cover='total_clouds', **kwargs): + """ + Defines the steps needed to convert raw forecast data + into processed forecast data. + + Parameters + ---------- + data: DataFrame + Raw forecast data + cloud_cover: str + The type of cloud cover used to infer the irradiance. + + Returns + ------- + data: DataFrame + Processed forecast data. + """ + + data = super(HRRR_ESRL, self).process_data(data, **kwargs) + data['temperature'] = self.kelvin_to_celsius(data['temperature']) + data['wind_speed'] = self.gust_to_speed(data) + data = data.join(self.cloud_cover_to_irradiance(data[cloud_cover]), + how='outer') + return data.ix[:, self.output_variables] + + +class NAM(ForecastModel): + """ + Subclass of the ForecastModel class representing NAM + forecast model. + + Model data corresponds to NAM CONUS 12km resolution forecasts + from CONDUIT. + + Parameters + ---------- + set_type: string + Type of model to pull data from. + + Attributes + ---------- + dataframe_variables: list + Common variables present in the final set of data. + model: string + Name of the UNIDATA forecast model. + model_type: string + UNIDATA category in which the model is located. + variables: dict + Defines the variables to obtain from the weather + model and how they should be renamed to common variable names. + units: dict + Dictionary containing the units of the standard variables + and the model specific variables. + """ + + def __init__(self, set_type='best'): + model_type = 'Forecast Model Data' + model = 'NAM CONUS 12km from CONDUIT' + + self.variables = { + 'temperature': 'Temperature_surface', + 'wind_speed_gust': 'Wind_speed_gust_surface', + 'total_clouds': 'Total_cloud_cover_entire_atmosphere_single_layer', + 'low_clouds': 'Low_cloud_cover_low_cloud', + 'mid_clouds': 'Medium_cloud_cover_middle_cloud', + 'high_clouds': 'High_cloud_cover_high_cloud', + 'ghi_raw': 'Downward_Short-Wave_Radiation_Flux_surface', } + + self.output_variables = [ + 'temperature', + 'wind_speed', + 'ghi', + 'dni', + 'dhi', + 'total_clouds', + 'low_clouds', + 'mid_clouds', + 'high_clouds',] + + super(NAM, self).__init__(model_type, model, set_type) + + def process_data(self, data, cloud_cover='total_clouds', **kwargs): + """ + Defines the steps needed to convert raw forecast data + into processed forecast data. + + Parameters + ---------- + data: DataFrame + Raw forecast data + cloud_cover: str + The type of cloud cover used to infer the irradiance. + + Returns + ------- + data: DataFrame + Processed forecast data. + """ + + data = super(NAM, self).process_data(data, **kwargs) + data['temperature'] = self.kelvin_to_celsius(data['temperature']) + data['wind_speed'] = self.gust_to_speed(data) + data = data.join(self.cloud_cover_to_irradiance(data[cloud_cover]), + how='outer') + return data.ix[:, self.output_variables] + + +class HRRR(ForecastModel): + """ + Subclass of the ForecastModel class representing HRRR + forecast model. + + Model data corresponds to NCEP HRRR CONUS 2.5km resolution + forecasts. + + Parameters + ---------- + set_type: string + Type of model to pull data from. + + Attributes + ---------- + dataframe_variables: list + Common variables present in the final set of data. + model: string + Name of the UNIDATA forecast model. + model_type: string + UNIDATA category in which the model is located. + variables: dict + Defines the variables to obtain from the weather + model and how they should be renamed to common variable names. + units: dict + Dictionary containing the units of the standard variables + and the model specific variables. + """ + + def __init__(self, set_type='best'): + model_type = 'Forecast Model Data' + model = 'NCEP HRRR CONUS 2.5km' + + self.variables = { + 'temperature_dew_iso': 'Dewpoint_temperature_isobaric', + 'temperature_iso': 'Temperature_isobaric', + 'pressure': 'Pressure_surface', + 'wind_speed_gust': 'Wind_speed_gust_surface', + 'total_clouds': 'Total_cloud_cover_entire_atmosphere', + 'low_clouds': 'Low_cloud_cover_low_cloud', + 'mid_clouds': 'Medium_cloud_cover_middle_cloud', + 'high_clouds': 'High_cloud_cover_high_cloud', + 'condensation_height': 'Geopotential_height_adiabatic_condensation_lifted'} + + self.output_variables = [ + 'temperature', + 'wind_speed', + 'ghi', + 'dni', + 'dhi', + 'total_clouds', + 'low_clouds', + 'mid_clouds', + 'high_clouds', ] + + super(HRRR, self).__init__(model_type, model, set_type) + + def process_data(self, data, cloud_cover='total_clouds', **kwargs): + """ + Defines the steps needed to convert raw forecast data + into processed forecast data. + + Parameters + ---------- + data: DataFrame + Raw forecast data + cloud_cover: str + The type of cloud cover used to infer the irradiance. + + Returns + ------- + data: DataFrame + Processed forecast data. + """ + + data = super(HRRR, self).process_data(data, **kwargs) + data['temperature'] = self.isobaric_to_ambient_temperature(data) + data['temperature'] = self.kelvin_to_celsius(data['temperature']) + data['wind_speed'] = self.gust_to_speed(data) + data = data.join(self.cloud_cover_to_irradiance(data[cloud_cover]), + how='outer') + return data.ix[:, self.output_variables] + + +class NDFD(ForecastModel): + """ + Subclass of the ForecastModel class representing NDFD forecast + model. + + Model data corresponds to NWS CONUS CONDUIT forecasts. + + Parameters + ---------- + set_type: string + Type of model to pull data from. + + Attributes + ---------- + dataframe_variables: list + Common variables present in the final set of data. + model: string + Name of the UNIDATA forecast model. + model_type: string + UNIDATA category in which the model is located. + variables: dict + Defines the variables to obtain from the weather + model and how they should be renamed to common variable names. + units: dict + Dictionary containing the units of the standard variables + and the model specific variables. + """ + + def __init__(self, set_type='best'): + model_type = 'Forecast Products and Analyses' + model = 'National Weather Service CONUS Forecast Grids (CONDUIT)' + self.variables = { + 'temperature': 'Temperature_surface', + 'wind_speed': 'Wind_speed_surface', + 'wind_speed_gust': 'Wind_speed_gust_surface', + 'total_clouds': 'Total_cloud_cover_surface', } + self.output_variables = [ + 'temperature', + 'wind_speed', + 'ghi', + 'dni', + 'dhi', + 'total_clouds', ] + super(NDFD, self).__init__(model_type, model, set_type) + + def process_data(self, data, **kwargs): + """ + Defines the steps needed to convert raw forecast data + into processed forecast data. + + Parameters + ---------- + data: DataFrame + Raw forecast data + + Returns + ------- + data: DataFrame + Processed forecast data. + """ + + cloud_cover = 'total_clouds' + data = super(NDFD, self).process_data(data, **kwargs) + data['temperature'] = self.kelvin_to_celsius(data['temperature']) + data = data.join(self.cloud_cover_to_irradiance(data[cloud_cover]), + how='outer') + return data.ix[:, self.output_variables] + + +class RAP(ForecastModel): + """ + Subclass of the ForecastModel class representing RAP forecast model. + + Model data corresponds to Rapid Refresh CONUS 20km resolution + forecasts. + + Parameters + ---------- + resolution: string or int + The model resolution, either '20' or '40' (km) + set_type: string + Type of model to pull data from. + + Attributes + ---------- + dataframe_variables: list + Common variables present in the final set of data. + model: string + Name of the UNIDATA forecast model. + model_type: string + UNIDATA category in which the model is located. + variables: dict + Defines the variables to obtain from the weather + model and how they should be renamed to common variable names. + units: dict + Dictionary containing the units of the standard variables + and the model specific variables. + """ + + _resolutions = ['20', '40'] + + def __init__(self, resolution='20', set_type='best'): + + resolution = str(resolution) + if resolution not in self._resolutions: + raise ValueError('resolution must in {}'.format(self._resolutions)) + + model_type = 'Forecast Model Data' + model = 'Rapid Refresh CONUS {}km'.format(resolution) + self.variables = { + 'temperature': 'Temperature_surface', + 'wind_speed_gust': 'Wind_speed_gust_surface', + 'total_clouds': 'Total_cloud_cover_entire_atmosphere_single_layer', + 'low_clouds': 'Low_cloud_cover_low_cloud', + 'mid_clouds': 'Medium_cloud_cover_middle_cloud', + 'high_clouds': 'High_cloud_cover_high_cloud', } + self.output_variables = [ + 'temperature', + 'wind_speed', + 'ghi', + 'dni', + 'dhi', + 'total_clouds', + 'low_clouds', + 'mid_clouds', + 'high_clouds', ] + super(RAP, self).__init__(model_type, model, set_type) + + def process_data(self, data, cloud_cover='total_clouds', **kwargs): + """ + Defines the steps needed to convert raw forecast data + into processed forecast data. + + Parameters + ---------- + data: DataFrame + Raw forecast data + cloud_cover: str + The type of cloud cover used to infer the irradiance. + + Returns + ------- + data: DataFrame + Processed forecast data. + """ + + data = super(RAP, self).process_data(data, **kwargs) + data['temperature'] = self.kelvin_to_celsius(data['temperature']) + data['wind_speed'] = self.gust_to_speed(data) + data = data.join(self.cloud_cover_to_irradiance(data[cloud_cover]), + how='outer') + return data.ix[:, self.output_variables] diff --git a/pvlib/irradiance.py b/pvlib/irradiance.py index e92ff4919e..910f000d67 100644 --- a/pvlib/irradiance.py +++ b/pvlib/irradiance.py @@ -17,6 +17,7 @@ from pvlib import tools from pvlib import solarposition +from pvlib import atmosphere SURFACE_ALBEDOS = {'urban': 0.18, 'grass': 0.20, @@ -2000,3 +2001,55 @@ def erbs(ghi, zenith, doy): data['kt'] = kt return data + + +def liujordan(zenith, cloud_prct, pressure=101325., dni_extra=1367.0): + ''' + Determine DNI, DHI, GHI from extraterrestrial flux, transmittance, + and optical air mass number. + + Liu and Jordan, 1960, developed a simplified direct radiation model. + DHI is from an empirical equation for diffuse radiation from Liu and + Jordan, 1960. + + Parameters + ---------- + zenith: pd.Series + True (not refraction-corrected) zenith angles in decimal + degrees. If Z is a vector it must be of the same size as all + other vector inputs. Z must be >=0 and <=180. + + cloud_prct: integer or float + Cloud coverage in percentage, %. + + pressure: float + Air pressure + + dni_extra: float + Direct irradiance incident at the top of the atmosphere. + + Returns + ------- + irradiance: DataFrame + Modeled direct normal irradiance, direct horizontal irradiance, + and global horizontal irradiance in W/m^2 + + References + ---------- + [1] Campbell, G. S., J. M. Norman (1998) An Introduction to + Environmental Biophysics. 2nd Ed. New York: Springer. + + [2] Liu, B. Y., R. C. Jordan, (1960). "The interrelationship and + characteristic distribution of direct, diffuse, and total solar + radiation". Solar Energy 4:1-19 + ''' + + tao = atmosphere.transmittance(cloud_prct) + airmass_relative = atmosphere.relativeairmass(zenith) + airmass = atmosphere.absoluteairmass(airmass_relative, pressure=pressure) + + dni = dni_extra*tao**airmass + dhi = 0.3 * (1.0 - tao**airmass) * dni_extra * np.cos(np.radians(zenith)) + ghi = dhi + dni * np.cos(np.radians(zenith)) + + return pd.DataFrame({'ghi': ghi, 'dni': dni, 'dhi': dhi}) diff --git a/pvlib/test/__init__.py b/pvlib/test/__init__.py index 947bde1a54..7a015064cd 100644 --- a/pvlib/test/__init__.py +++ b/pvlib/test/__init__.py @@ -18,10 +18,17 @@ except ImportError: has_scipy = False +try: + import siphon + has_siphon = True +except ImportError: + has_siphon = False def requires_scipy(test): return test if has_scipy else unittest.skip('requires scipy')(test) +def requires_siphon(test): + return test if has_siphon else unittest.skip('requires siphon')(test) try: import ephem @@ -29,11 +36,9 @@ def requires_scipy(test): except ImportError: has_ephem = False - def requires_ephem(test): return test if has_ephem else unittest.skip('requires ephem')(test) - def incompatible_conda_linux_py3(test): """ Test won't work in Python 3.x due to Anaconda issue. diff --git a/pvlib/test/test_atmosphere.py b/pvlib/test/test_atmosphere.py index f570d75ebb..4650d787a4 100644 --- a/pvlib/test/test_atmosphere.py +++ b/pvlib/test/test_atmosphere.py @@ -65,3 +65,7 @@ def test_absoluteairmass_numeric(): def test_absoluteairmass_nan(): np.testing.assert_equal(np.nan, atmosphere.absoluteairmass(np.nan)) + +def test_transmittance(): + assert atmosphere.transmittance(0) == 0.75 + assert atmosphere.transmittance(100) == 0.0 \ No newline at end of file diff --git a/pvlib/test/test_forecast.py b/pvlib/test/test_forecast.py new file mode 100644 index 0000000000..b6dc8b4276 --- /dev/null +++ b/pvlib/test/test_forecast.py @@ -0,0 +1,122 @@ +from datetime import datetime, timedelta +import inspect +from math import isnan +from pytz import timezone + +import numpy as np +import pandas as pd + +from nose.tools import raises, assert_almost_equals +from nose.plugins.skip import SkipTest +from numpy.testing import assert_almost_equal + +from . import requires_siphon, has_siphon + +if has_siphon: + import requests + from requests.exceptions import HTTPError + from xml.etree.ElementTree import ParseError + + from pvlib.forecast import GFS, HRRR_ESRL, HRRR, NAM, NDFD, RAP + from pvlib.location import Location + + # setup times and location to be tested. Tucson, AZ + _latitude = 32.2 + _longitude = -110.9 + _tz = 'US/Arizona' + _start = pd.Timestamp.now(tz=_tz) + _end = _start + pd.Timedelta(days=1) + _models = [GFS, NAM, HRRR, RAP, NDFD, HRRR_ESRL] + _working_models = [] + _variables = ['temperature', 'wind_speed', 'total_clouds', 'low_clouds', + 'mid_clouds', 'high_clouds', 'dni', 'dhi', 'ghi',] + _nonnan_variables = ['temperature', 'wind_speed', 'total_clouds', 'dni', + 'dhi', 'ghi',] + +@requires_siphon +def test_model_creation(): + for model in _models: + if model.__name__ != 'HRRR_ESRL': + try: + resolutions = model._resolutions + except AttributeError: + amodel = model() + _working_models.append(amodel) + else: + for resolution in resolutions: + amodel = model(resolution=resolution) + _working_models.append(amodel) + +@requires_siphon +def test_data_query(): + for model in _working_models: + yield run_query, model + +def run_query(model): + model.data = model.get_processed_data(_latitude, _longitude, _start, _end) + +@requires_siphon +def test_dataframe_variables(): + for amodel in _working_models: + yield run_variables, amodel + +def run_variables(amodel): +# for variable in _variables: +# assert variable in amodel.data.columns + for variable in _nonnan_variables: + assert not amodel.data[variable].isnull().values.any() + +@requires_siphon +def test_vert_level(): + amodel = _working_models[0] + vert_level = 5000 + data = amodel.get_processed_data(_latitude, _longitude, _start, _end, + vert_level=vert_level) + +@requires_siphon +def test_datetime(): + amodel = _working_models[0] + start = datetime.now() + end = start + timedelta(days=1) + data = amodel.get_processed_data(_latitude, _longitude , start, end) + +@requires_siphon +def test_queryvariables(): + amodel = _working_models[0] + old_variables = amodel.variables + new_variables = ['u-component_of_wind_height_above_ground'] + data = amodel.get_data(_latitude, _longitude, _start, _end, + query_variables=new_variables) + data['u-component_of_wind_height_above_ground'] + +@requires_siphon +def test_latest(): + GFS(set_type='latest') + +@requires_siphon +def test_full(): + GFS(set_type='full') + +@requires_siphon +def test_temp_convert(): + amodel = _working_models[0] + data = pd.DataFrame({'temperature': [273.15]}) + data['temperature'] = amodel.kelvin_to_celsius(data['temperature']) + + assert data['temperature'].values == 0.0 + +# @requires_siphon +# def test_bounding_box(): +# amodel = GFS() +# latitude = [31.2,32.2] +# longitude = [-111.9,-110.9] +# new_variables = {'temperature':'Temperature_surface'} +# data = amodel.get_query_data(latitude, longitude, _start, _end, +# variables=new_variables) + +@requires_siphon +def test_set_location(): + amodel = _working_models[0] + time = datetime.now(timezone('UTC')) + amodel.set_location(time) + diff --git a/pvlib/test/test_irradiance.py b/pvlib/test/test_irradiance.py index a5918cacb4..1f1039e1cd 100644 --- a/pvlib/test/test_irradiance.py +++ b/pvlib/test/test_irradiance.py @@ -146,6 +146,12 @@ def test_perez(): dni_et, ephem_data['apparent_zenith'], ephem_data['azimuth'], AM) + +def test_liujordan(): + cloud_prct = np.array([40]*len(ephem_data['apparent_zenith'])) + irradiance.liujordan(ephem_data['apparent_zenith'], cloud_prct) + + # klutcher (misspelling) will be removed in 0.3 def test_total_irrad(): models = ['isotropic', 'klutcher', 'klucher',