diff --git a/README.md b/README.md index cc94ba6e1..b04f49100 100644 --- a/README.md +++ b/README.md @@ -1,142 +1,116 @@ # PreREISE -This package defines the scenario and calls the Matlab simulation engine. -The name stands for pre Renewable Energy Integration Study Engine. -After setting up the scenario in REISE you create an entry in `ScenarioList.csv`. -This file contains all scenraios. The location of the list on the server is `/home/EGM/`. +This package defines the scenario and calls the MATLAB simulation engine. The name stands for pre Renewable Energy Integration Study Engine. After setting up the scenario in REISE you create an entry in ***ScenarioList.csv***. This file contains all scenarios. The location of the list on the server is `/home/EGM/`. -## 1. Creating a Scenario -A scenario can be defined by adding an entry to the scenario list `ScenarioList.csv`. -Fill in all the required information. The following fields are required: -name | folder_location | input_data_location | output_data_location | start_index | end_index | extract | description ------------- | ------------- | ------------ | ------------- | ------------ | ------------- | ------------ | ------------- -`scenario_name` | Folder location of Matlab files | Input data location | Output data location | Start index | End index | True/False to convert data into csv | Description +## 1. Setup/Install +This package requires MATLAB, Gurobi, MATPOWER and WesternInterconnect. -Make sure you choose an unique name in the scenario_name column . Since this list is temporary -and will be replaced by a database there is no check of uniqueness. -Make sure you have stored the `scenario_name.m` file, the related simulation and -data files in the defined locations. -The convention is that we use the scenario name as the output folder name. -Make sure your simulation m-file has the same name as the unique scenario name. - -## 2. Call (Start the simulation) -Simulation can only be launched on server. -After setting up the scenario, the simulation engine can be called. -You launch the simulation the following way: -```python -import prereise - -prereise.launch_scenario_performance('scenario_name') -``` -### Test -To test run: -```python -from prereise.call.test import test_call - -test_call.test() -``` - -### A. Setup/Install -This package requires Matlab, Gurobi, and Matpower. Make sure to put the paths -from Gurobi and Matpower into the `add_path.m` file. -Before installing this package install Matlab, Gurobi and Matpower. - -### B. For Matlab the following setup is required: -On Windows systems — +### A. MATLAB +Install MATLAB and proceed as follows: ``` cd "matlabroot\extern\engines\python" python setup.py install ``` -On Mac or Linux systems — +for Windows systems. + ``` cd "matlabroot/extern/engines/python" python setup.py install ``` +for Mac or Linux systems. + -### C. Install Gurobi and add path -Install Gurobi and add Matlab path to 'add_path.m' +### B. Gurobi +Install Gurobi and add MATLAB path to ***add_path.m***: ``` //matlab ``` -### D. For Matpower the following setup is required: -Download Matpower and add the following directories to the `add_path.m`: + +### C. MATPOWER +Download MATPOWER and add the following directories in ***add_path.m***: ``` — core MATPOWER functions /most — core MOST functions ``` -### E. Install this package -In the folder with the setup.py file type: -`pip3 install .` +### D. WesternInterconnectNetwork +In the WesternInterconnect package, locate the ***setup.py*** file and type: `pip3 install .` Do not forget to update your PYTHONPATH environment variable. -## 3. Gather -This module allows you to gather data for the simulation. -### A. Collect Data +### E. PreREISE +In the PreREISE package, locate the ***setup.py*** file and type: `pip3 install .` Do not forget to update your PYTHONPATH environment variable. -#### α. Wind data -• Rapid Refresh: -[RAP](https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/rapid-refresh-rap) (Rapid Refresh) is the continental-scale NOAA hourly-updated assimilation/modeling system operational at the National Centers for Environmental Prediction (NCEP). RAP covers North America and is comprised primarily of a numerical weather model and an analysis system to initialize that model. RAP provides, every hour ranging from May 2012 to date, the U and V components of the wind speed at 80 meter above ground on a 13x13 square kilometer resolution grid every hour. Data can be retrieved using the NetCDF Subset Service. Information on this interface is described [here](https://www.unidata.ucar.edu/software/thredds/current/tds/reference/NetcdfSubsetServiceReference.html). +## 2. Create Scenario +A scenario can be defined by adding an entry to the scenario list ***ScenarioList.csv*** table. Fill in all the required information. There are 8 columns in the scenario list table. These are: +* **name**: `scenario_name`; +* **folder_location**: path to folder where MATLAB files are located; +* **input_data_location**: path to folder where input data are located; +* **output_data_location**: path to folder where input will be saved; +* **start_index**: start index; +* **end_index**: end index; +* **extract**: True/False. Should output data be converted to csv and; +* **description**: description, problem encountered, ... -Usage in general: -```python -from prereise.gather.winddata import rap +Make sure you choose an unique name in the `scenario_name` column. Since this list is temporary and will be replaced by a database there is no check of uniqueness. Make sure you have stored the ***scenario_name.m*** file, the related simulation and data files in the defined locations. The convention is that we use the scenario name as the output folder name. Make sure your simulation m-file has the same name as the unique scenario name. -rap.retrieve_data(wind_farm) -``` -Check out the demo jupyter notebook in -`prereise/gather/winddata/rap/demo/` -• Techno-Economic Wind Integration National Dataset Toolkit: -The [Techno-Economic WIND (Wind Integration National Dataset) Toolkit](https://www.nrel.gov/grid/wind-toolkit.html) provides 5-min resolution data for 7 years, ranging from 2007 to 2013, at 120,000 points within the continental U.S. selected for their wind resource. This set contains power estimates and forecasts along with a subset of atmospheric variables. Data can be accessed via an [API](https://developer.nrel.gov/docs/wind/wind-toolkit/). +## 3. Gather Data for Simulation +This module aims at gathering the required data for the simulation. -Check out the demo jupyter notebook in -`prereise/gather/winddata/te_wind/demo/` +### A. Wind data -Usage in general: -```python -from prereise.gather.winddata.te_wind import te_wind +#### i. Rapid Refresh +[RAP](https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/rapid-refresh-rap) (Rapid Refresh) is the continental-scale NOAA hourly-updated assimilation/modeling system operational at the National Centers for Environmental Prediction (NCEP). RAP covers North America and is comprised primarily of a numerical weather model and an analysis system to initialize that model. RAP provides, every hour ranging from May 2012 to date, the U and V components of the wind speed at 80 meter above ground on a 13x13 square kilometer resolution grid every hour. Data can be retrieved using the NetCDF Subset Service. Information on this interface is described [here](https://www.unidata.ucar.edu/software/thredds/current/tds/reference/NetcdfSubsetServiceReference.html). -te_wind.get_all_NREL_siteID_for_states(['WA','CA']) -``` -To run a test: +Note that the dataset is incomplete (33 hours are missing in 2016) and, consequently, missing entries need to be imputed. Afterwards, wind speed is converted to power for all the wind farms in the network using the *IEC class 2* power curve provided by NREL in the [WIND Toolkit documentation](https://www.nrel.gov/docs/fy14osti/61714.pdf). + +Check out the ***[rap_demo.ipynb](https://github.com/intvenlab/PreREISE/blob/sam/prereise/gather/winddata/rap/demo/rap_demo.ipynb)*** notebook for demo. + +#### ii. Techno-Economic Wind Integration National Dataset Toolkit +The [Techno-Economic WIND (Wind Integration National Dataset) Toolkit](https://www.nrel.gov/grid/wind-toolkit.html) provides 5-min resolution data for 7 years, ranging from 2007 to 2013, at 120,000 points within the continental U.S. selected for their wind resource. This set contains power estimates and forecasts along with a subset of atmospheric variables. Data can be accessed via an [API](https://developer.nrel.gov/docs/wind/wind-toolkit/). + +The closest site to the wind farm in the network is found in the NREL dataset and the associated power estimate is simply scaled to the plant capacity to obtain a wind power output profile. The procedure is illustrated in the ***[te_wind_demo.ipynb](https://github.com/intvenlab/PreREISE/blob/sam/prereise/gather/winddata/te_wind/demo/te_wind_demo.ipynb)*** notebook. + +Also, a test can be run as follows: ```python from prereise.gather.winddata.te_wind.test import te_wind_test + te_wind_test.test() ``` -#### β. Solar data - -• The Gridded Atmospheric Wind Integration National Dataset Toolkit: +### B. Solar data +#### i. The Gridded Atmospheric Wind Integration National Dataset Toolkit The [Gridded Atmospheric WIND (Wind Integration National Dataset) Toolkit](https://www.nrel.gov/grid/wind-toolkit.html) provides 1-hour resolution irradiance data for 7 years, ranging from 2007 to 2013, on a uniform 2x2 square kilometer grid that covers the continental U.S., the Baja Peninsula, and parts of the Pacific and Atlantic oceans. Data can be accessed using the Highly Scalable Data Service. NREL wrote [example notebooks](https://github.com/NREL/hsds-examples) that demonstrate how to access the data. -• The National Solar Radiation Database: +Power output is estimated using a simple normalization procedure. For each solar plant location the hourly Global Horizontal Irradiance (GHI) is divided by the maximum GHI over the period considered and multiplied by the capacity of the plant. This procedure is referred to as naïve since it only accounts for the plant capacity. Note that other factors can possibly affect the conversion from solar radiation at ground to power such as the temperature at the site as well as many system configuration including tracking technology. +Check out the ***[ga_wind_demo.ipynb](https://github.com/intvenlab/PreREISE/blob/sam/prereise/gather/solardata/ga_wind/demo/ga_wind_demo.ipynb)*** notebook for demo. + +#### ii. The National Solar Radiation Database [NSRDB (National Solar Radiation Database)](https://nsrdb.nrel.gov/) provides 1-hour resolution solar radiation data, ranging from 1998 to 2016, for the entire U.S. and a growing list of international locations on a 4x4 square kilometer grid. Data can be accessed via an [API](https://developer.nrel.gov/docs/solar/nsrdb/). Note that the Physical Solar Model v3 is used. -#### γ. Demand Data -Demand data are obtained from EIA, to whom Balancing Authorities have submitted their data. -The data can be obtained either by direct download from their database using an API or -by download of Excel spreadsheets. A API key is required for the API download and this key -can be obtained by a user by registering at https://www.eia.gov/opendata/. +An API key is required to access and use the above databases. Get your own API key [here](https://developer.nrel.gov/signup/). + +Here, the power output can be estimated using the previously presented naïve method or a more sophisticated one. The latter uses the System Advisor Model ([SAM](https://sam.nrel.gov/)) developed by NREL. The developer tools for creating renewable energy system models can be downloaded [here](https://sam.nrel.gov/sdk). Irradiance data along with other meteorological parameters must first be retrieved from NSRDB for each site. This information are then fed to the SAM Simulation Core (SCC) and the power output is retrieved. The SSC reflect the technology used: photovoltaic (PV), solar water heating and concentrating solar power (CSP). The *[PVWatts v5](https://www.nrel.gov/docs/fy14osti/62641.pdf)* model is used for all the solar plants in the grid. The default values of the parameters of the *PVWatts* model are untouched. Only the system size and the array type (fixed open rack, backtracked, 1-axis and 2-axis) is set for each solar plant. + +The naïve and the SAM method are used in the ***[nsrdb_naive_demo.ipynb](https://github.com/intvenlab/PreREISE/blob/sam/prereise/gather/solardata/nsrdb/demo/nsrdb_naive_demo.ipynb)*** and ***[nsrdb_sam_demo.ipynb](https://github.com/intvenlab/PreREISE/blob/sam/prereise/gather/solardata/nsrdb/demo/nsrdb_sam_demo.ipynb)*** demo notebook, respectively. -The direct download currently contains only published -demand data. The Excel spreadsheets include original and imputed demand data, as well as -results of various data quality checks done by EIA. Documentation about the datasets are in https://www.eia.gov/realtime_grid/docs/userguide-knownissues.pdf. -Excel spreadsheets can be downloaded by clicking on the links in page 9 (Table of all US and -foreign connected balancing authorities). -Module get_eia_data contains functions that converts the data into pandas dataframes for -further processing. +### C. Demand Data +Demand data are obtained from EIA, to whom Balancing Authorities have submitted their data. The data can be obtained either by direct download from their database using an API or by download of Excel spreadsheets. A API key is required for the API download and this key can be obtained by a user by registering at https://www.eia.gov/opendata/. + +The direct download currently contains only published demand data. The Excel spreadsheets include original and imputed demand data, as well as results of various data quality checks done by EIA. Documentation about the datasets can be found [here](https://www.eia.gov/realtime_grid/docs/userguide-knownissues.pdf). Excel spreadsheets can be downloaded by clicking on the links in page 9 (Table of all US and foreign connected balancing authorities). + +Module `get_eia_data` contains functions that converts the data into data frames for further processing. To use, ```python @@ -145,39 +119,39 @@ from prereise.gather.demanddata.eia import get_eia_data data = get_eia_data.from_excel(dir, BA_list, startdate, enddate) ``` -To test EIA download (This requires an EIA API key), +To test EIA download (This requires an EIA API key): ```python from prereise.gather.demanddata.eia.test import test_eia_download + test_eia_download.test_eia_download() ``` -To test EIA download from Excel, +To test EIA download from Excel: ```python from prereise.gather.demanddata.eia.test import test_from_excel + test_from_excel.test_from_excel() ``` -The notebook prereise/gather/demanddata/eia/demo/AssembleBAfromExcel_demo.ipynb -illustrates usage. +The notebook [AssembleBAfromExcel_demo.ipynb](https://github.com/intvenlab/PreREISE/blob/sam/prereise/gather/demanddata/EIA/demo/AssembleBAfromExcel_demo.ipynb) illustrates usage. -##### Outputting Demand Profile -To output the demand profile, cleaning steps were applied to the EIA data. -1) missing data imputation - the EIA method was used, i.e., EIA published data was used; beyond this, NA's were converted to float zeros -2) missing hours were added +To output the demand profile, cleaning steps were applied to the EIA data: +1) missing data imputation - the EIA method was used, i.e., EIA published data was used; beyond this, NA's were converted to float zeros; +2) missing hours were added. The BA counts were then distributed across each region where the BA operates, using the region populations as weights. For example, if a BA operates in both WA and OR, the counts for WA are weighted by the fraction of the total counts in WA relative to the total population of WA and OR. -The demand profile is finally converted to Matlab format. - -### B. Power output calculation - -#### α. Wind power output -• Naïve method: -The *IEC class 2* power curve provided by NREL in the [WIND Toolkit documentation](https://www.nrel.gov/docs/fy14osti/61714.pdf) is used to convert wind speed to power for all the wind farms in the network. This is the method currently implemented. - -#### β. Solar power output +## 4. Start simulation +Simulation can only be launched on server. After setting up the scenario, the simulation engine can be called and the simulation can be started as follows: +```python +import prereise -• Naïve method: +prereise.launch_scenario_performance('scenario_name') +``` +To test, run: +```python +from prereise.call.test import test_call -This estimation method uses a simple normalization procedure to convert the Global Horizontal Irradiance (GHI) to power output. For each plant location the hourly GHI is divided by the maximum GHI over the period considered and multiplied by the capacity of the plant. In other words, each plant reaches its maximal capacity only once over the period considered. This procedure is referred to as naïve since it only accounts for the plant capacity. Note that other factors can possibly affect the conversion from solar radiation at ground to power such as the temperature at the site as well as many system configuration including DC/AC ratio or eventual tilt system. This is the method currently implemented. +test_call.test() +``` diff --git a/prereise/gather/demanddata/EIA/demo/AssembleBAfromExcel_demo.ipynb b/prereise/gather/demanddata/EIA/demo/AssembleBAfromExcel_demo.ipynb index ad20aa1fc..52537a55e 100644 --- a/prereise/gather/demanddata/EIA/demo/AssembleBAfromExcel_demo.ipynb +++ b/prereise/gather/demanddata/EIA/demo/AssembleBAfromExcel_demo.ipynb @@ -20,10 +20,8 @@ "from dateutil.parser import parse\n", "\n", "import os\n", - "import sys\n", - "sys.path.append(\"..\")\n", "\n", - "import get_eia_data" + "from prereise.gather.demanddata.eia import get_eia_data" ] }, { @@ -183,7 +181,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.7rc2" } }, "nbformat": 4, diff --git a/prereise/gather/demanddata/EIA/demo/__init__.py b/prereise/gather/demanddata/EIA/demo/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/prereise/gather/demanddata/EIA/get_eia_data.py b/prereise/gather/demanddata/EIA/get_eia_data.py index feedfee68..a9afebf87 100644 --- a/prereise/gather/demanddata/EIA/get_eia_data.py +++ b/prereise/gather/demanddata/EIA/get_eia_data.py @@ -14,14 +14,14 @@ def from_download(tok, start_date, end_date, offset_days, series_list): """Downloads and assemble dataset of demand data per balancing authority \ for desired date range. - :param string tok: token obtained by registering with EIA. + :param str tok: token obtained by registering with EIA. :param timestamp start: start date. :param timestamp end: end data. :param list series_list: list of demand series names provided by EIA, \ e.g., ['EBA.AVA-ALL.D.H', 'EBA.AZPS-ALL.D.H']. :param int offset_days: number of business days for data to stabilize. - :return: data frame indexed with hourly UTC time and BA series \ - name for column names. + :return: (*pandas*) -- data frame indexed with hourly UTC time and BA \ + series name for column names. """ timespan = pd.date_range(start_date, @@ -44,12 +44,12 @@ def from_excel(directory, series_list, start_date, end_date): """Assembles EIA balancing authority (BA) data from pre-downloaded Excel \ spreadsheets. The spreadsheets contain data from July 2015 to present. - :param string directory: location of Excel files. + :param str directory: location of Excel files. :param list series_list: list of BA initials, e.g., ['PSE',BPAT','CISO']. :param timestamp start_date: desired start of dataset. :param timestamp end_date: desired end of dataset. - :return: data frame indexed with hourly UTC time and BA series \ - name for column names. + :return: (*pandas*) -- data frame indexed with hourly UTC time and BA \ + series name for column names. """ timespan = pd.date_range(start_date, end_date, freq='H') df_all = pd.DataFrame(index=timespan) @@ -70,14 +70,15 @@ def from_excel(directory, series_list, start_date, end_date): class EIAgov(object): + """Copied from `this link `_. + + :param str token: EIA token. + :param list series: id code(s) of the series to be downloaded. + + """ + def __init__(self, token, series): - """Initialises the EIAgov class. Copied from \ - `https://quantcorner.wordpress.com/2014/11/18/downloading-eias-data-with-python/` - - :param string token: EIA token - :param list series: id code(s) of the series to be downloaded - """ - self.token = token self.series = series @@ -107,7 +108,9 @@ def raw(self, ser): print('Reason: ', e.reason) def get_data(self): - """Convert json files into pandas DataFrame + """Converts json files into data frame. + + :return: (*pandas*) -- data frame. """ date_ = self.raw(self.series[0]) diff --git a/prereise/gather/demanddata/EIA/test/__init__.py b/prereise/gather/demanddata/EIA/test/__init__.py index 9bde35a8f..623d164ca 100644 --- a/prereise/gather/demanddata/EIA/test/__init__.py +++ b/prereise/gather/demanddata/EIA/test/__init__.py @@ -1 +1 @@ -__all__= ["test_EIAdownload", "test_from_excel"] +__all__= ["test_eia_download", "test_from_excel"] diff --git a/prereise/gather/demanddata/EIA/test/test_eia_download.py b/prereise/gather/demanddata/EIA/test/test_eia_download.py index f6c445583..64c49ddec 100644 --- a/prereise/gather/demanddata/EIA/test/test_eia_download.py +++ b/prereise/gather/demanddata/EIA/test/test_eia_download.py @@ -8,21 +8,18 @@ import pandas as pd import pytest -from .. import get_eia_data +from prereise.gather.demanddata.eia import get_eia_data @pytest.mark.skip(reason="Need API key") def test_eia_download(): - ''' - Check pandas DataFrame assembled from data download \ - by API call from EIA. Test checks that the correct \ - number of files are downloaded and correct number of \ - columns are created. - - Token string can be obtained by registering in - https://www.eia.gov/opendata/ - - ''' + """Check data frame assembled from data download by API call from \ + EIA. Test checks that the correct number of files are downloaded and \ + correct number of columns are created. + + Token string can be obtained by registering \ + `here `_. + """ print( 'A API key is required for the API download. The key ' 'can be obtained by a user by registering at ' diff --git a/prereise/gather/demanddata/EIA/test/test_from_excel.py b/prereise/gather/demanddata/EIA/test/test_from_excel.py index 9ab0d9e79..a84e57580 100644 --- a/prereise/gather/demanddata/EIA/test/test_from_excel.py +++ b/prereise/gather/demanddata/EIA/test/test_from_excel.py @@ -8,16 +8,14 @@ import pandas as pd import pytest -from .. import get_eia_data +from prereise.gather.demanddata.eia import get_eia_data def test_from_excel(): - ''' - Test pandas DataFrame assembled from Excel spreadsheets \ - manually downloaded from EIA. Test checks that correct \ - number of columns are created. - - ''' + """Tests data frame assembled from Excel spreadsheets manually \ + downloaded from EIA. Test checks that correct number of columns are \ + created. + """ dir1 = os.path.join(os.path.dirname(__file__), 'data') diff --git a/prereise/gather/solardata/ga_wind/demo/ga_wind_demo.ipynb b/prereise/gather/solardata/ga_wind/demo/ga_wind_demo.ipynb index d380ccce1..393c5047c 100644 --- a/prereise/gather/solardata/ga_wind/demo/ga_wind_demo.ipynb +++ b/prereise/gather/solardata/ga_wind/demo/ga_wind_demo.ipynb @@ -21,30 +21,16 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], - "source": [ - "from westernintnet.westernintnet import WesternIntNet\n", - "import sys\n", - "\n", - "sys.path.append(\"../\")\n", - "\n", - "import ga_wind\n", - "from matplotlib import pyplot\n", - "from helpers import to_reise" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Solar Plants in Network" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\program files\\python\\python36\\lib\\site-packages\\matplotlib\\__init__.py:886: MatplotlibDeprecationWarning: \n", + "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n", + " \"found relative to the 'datapath' directory.\".format(key))\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -56,16 +42,30 @@ "Loading branches\n", "Loading resources\n", "Loading net_generation\n", - "Load solar data\n", - "Load wind data\n", - "Load hydro data\n", - "Load demand data\n", "Done loading\n" ] } ], "source": [ - "grid = WesternIntNet()\n", + "from westernintnet.westernintnet import win_data as grid\n", + "from prereise.gather.solardata.ga_wind import ga_wind, helpers\n", + "\n", + "from matplotlib import pyplot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Solar Plants in Network" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ "solar_plant = grid.genbus.groupby('type').get_group('solar')" ] }, @@ -653,7 +653,7 @@ "metadata": {}, "outputs": [], "source": [ - "data_reise = to_reise(data)" + "data_reise = helpers.to_reise(data)" ] }, { diff --git a/prereise/gather/solardata/ga_wind/ga_wind.py b/prereise/gather/solardata/ga_wind/ga_wind.py index 32291fe36..31086d042 100644 --- a/prereise/gather/solardata/ga_wind/ga_wind.py +++ b/prereise/gather/solardata/ga_wind/ga_wind.py @@ -6,7 +6,7 @@ import pandas as pd from tqdm import tqdm -from .helpers import ll2ij +from prereise.gather.solardata.ga_wind import helpers def retrieve_data(solar_plant, hs_api_key, @@ -15,11 +15,12 @@ def retrieve_data(solar_plant, hs_api_key, """Retrieve irradiance data from Gridded Atmospheric Wind Integration \ National Dataset. - :param solar_plant: pandas DataFrame with the following structure: \ - ['plantID'(index), 'lat', 'lon', 'GenMWMax']. - :param year: year. - :return: pandas DataFrame with the following structure: ['Pout', \ - 'plantID', 'ts', 'tsID']. The power output is in MW. + :param pandas solar_plant: data frame with *'lat'*, *'lon'* and \ + *'GenMWMax'* and *'plantID'* as indices. + :param str hs_api_key: API key. + :param str year: year. + :return: (*pandas*) -- data frame with *'Pout'*, *'plantID'*, *'ts'* and \ + *'tsID'* as columns. The power output is in MWh. """ # Information on solar plants @@ -52,7 +53,7 @@ def retrieve_data(solar_plant, hs_api_key, lat_origin, lon_origin = f['coordinates'][0][0] ij = {} for key in coord.keys(): - ij[key] = ll2ij(lon_origin, lat_origin, key[0], key[1]) + ij[key] = helpers.ll2ij(lon_origin, lat_origin, key[0], key[1]) # Extract time serie dt = f['datetime'] diff --git a/prereise/gather/solardata/ga_wind/helpers.py b/prereise/gather/solardata/ga_wind/helpers.py index b22d764cc..d61019e3d 100644 --- a/prereise/gather/solardata/ga_wind/helpers.py +++ b/prereise/gather/solardata/ga_wind/helpers.py @@ -6,9 +6,9 @@ def ll2ij(lon_origin, lat_origin, lon, lat): """Find nearest x/y indices for a given lat/lon. - :param lat: Latitude of coordinate of interest. - :param lon: Longitude of coordinate of interest. - :return: coordinate of the closest pixel in the database. + :param float lat: Latitude of coordinate of interest. + :param float lon: Longitude of coordinate of interest. + :return: (*tuple*) -- coordinate of the closest pixel in the database. """ proj_string = """+proj=lcc +lat_1=30 +lat_2=60 @@ -32,9 +32,9 @@ def ll2ij(lon_origin, lat_origin, lon, lat): def to_reise(data): """Format data for REISE. - :param data: pandas DataFrame as returned by \ - :py:func:`prereise.gather.solardata.ga_wind.ga_wind.retrieve_data`. - :return: pandas DataFrame formated for REISE. + :param pandas data: pandas data frame as returned by \ + :func:`prereise.gather.solardata.ga_wind.ga_wind.retrieve_data`. + :return: (*pandas*) data frame formated for REISE. """ ts = data['ts'].unique() plantID = data[data.tsID == 1].plantID.values diff --git a/prereise/gather/solardata/nsrdb/__init__.py b/prereise/gather/solardata/nsrdb/__init__.py index b7c678ca2..00c73ab9e 100644 --- a/prereise/gather/solardata/nsrdb/__init__.py +++ b/prereise/gather/solardata/nsrdb/__init__.py @@ -1 +1 @@ -__all__ = ["helpers", "nsrdb"] +__all__ = ["helpers", "naive", "sam"] diff --git a/prereise/gather/solardata/nsrdb/demo/nsrdb_demo.ipynb b/prereise/gather/solardata/nsrdb/demo/nsrdb_naive_demo.ipynb similarity index 99% rename from prereise/gather/solardata/nsrdb/demo/nsrdb_demo.ipynb rename to prereise/gather/solardata/nsrdb/demo/nsrdb_naive_demo.ipynb index b73d1f493..b41e2a8a9 100644 --- a/prereise/gather/solardata/nsrdb/demo/nsrdb_demo.ipynb +++ b/prereise/gather/solardata/nsrdb/demo/nsrdb_naive_demo.ipynb @@ -4,47 +4,35 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Solar Data from NSRDB\n", + "# NSRDB and Naïve Model for Solar Power Calculation\n", "---\n", "\n", "**NREL**: National Renewable Energy Laboratory\n", "\n", "**NSRDB**: National Solar Radiation Database \n", "Information can be found at https://nsrdb.nrel.gov \n", - "API: https://developer.nrel.gov/docs/solar/nsrdb/psm3_data_download/\n", + "API key Signup: https://developer.nrel.gov/signup/ \n", + "API instructions: https://developer.nrel.gov/docs/solar/nsrdb/psm3_data_download/\n", "\n", - "NSRDB is a serially complete collection of meteorological and solar irradiance data sets for the United States and a growing list of international locations. NSRDB uses a physics-based modeling (PSM: Physical Solar Model) approach to provide solar radiation data for the entire United States in gridded segments (4x4 square kilometer) using geostationary satellites. Data can be accessed via an API." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from westernintnet.westernintnet import WesternIntNet\n", - "import sys\n", - "\n", - "sys.path.append(\"../\")\n", + "NSRDB is a serially complete collection of meteorological and solar irradiance data sets for the United States and a growing list of international locations. NSRDB uses a physics-based modeling (PSM: Physical Solar Model) approach to provide solar radiation data for the entire United States in gridded segments (4x4 square kilometer) using geostationary satellites. Data can be accessed via an API.\n", "\n", - "import nsrdb\n", - "import getpass\n", - "from matplotlib import pyplot\n", - "from helpers import to_reise" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Solar plants in Network" + "A simple normalization procedure is then used to convert the Global Horizontal Irradiance (GHI) to power output." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\program files\\python\\python36\\lib\\site-packages\\matplotlib\\__init__.py:886: MatplotlibDeprecationWarning: \n", + "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n", + " \"found relative to the 'datapath' directory.\".format(key))\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -56,16 +44,31 @@ "Loading branches\n", "Loading resources\n", "Loading net_generation\n", - "Load solar data\n", - "Load wind data\n", - "Load hydro data\n", - "Load demand data\n", "Done loading\n" ] } ], "source": [ - "grid = WesternIntNet()\n", + "from westernintnet.westernintnet import win_data as grid\n", + "from prereise.gather.solardata.nsrdb import naive, helpers\n", + "\n", + "import getpass\n", + "from matplotlib import pyplot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Solar plants in Network" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ "solar_plant = grid.genbus.groupby('type').get_group('solar')" ] }, @@ -354,7 +357,7 @@ "source": [ "email = getpass.getpass(prompt='email=')\n", "key = getpass.getpass(prompt='api_key=')\n", - "data = nsrdb.retrieve_data(solar_plant, email, key)" + "data = naive.retrieve_data(solar_plant, email, key)" ] }, { @@ -662,7 +665,7 @@ "metadata": {}, "outputs": [], "source": [ - "data_reise = to_reise(data)" + "data_reise = helpers.to_reise(data)" ] }, { diff --git a/prereise/gather/solardata/nsrdb/demo/nsrdb_sam_demo.ipynb b/prereise/gather/solardata/nsrdb/demo/nsrdb_sam_demo.ipynb new file mode 100644 index 000000000..020406aac --- /dev/null +++ b/prereise/gather/solardata/nsrdb/demo/nsrdb_sam_demo.ipynb @@ -0,0 +1,973 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NSRDB and SAM for Solar Power Calculation\n", + "---\n", + "\n", + "**NREL**: National Renewable Energy Laboratory\n", + "\n", + "**NSRDB**: National Solar Radiation Database \n", + "Information can be found at https://nsrdb.nrel.gov \n", + "API key Signup: https://developer.nrel.gov/signup/ \n", + "API instructions: https://developer.nrel.gov/docs/solar/nsrdb/psm3_data_download/\n", + "\n", + "**SAM**: System Advisor Model \n", + "Information can be found at https://sam.nrel.gov/ \n", + "SDK: https://sam.nrel.gov/sdk. The SDK needs to be downloaded.\n", + "\n", + "NSRDB is a serially complete collection of meteorological and solar irradiance data sets for the United States and a growing list of international locations. NSRDB uses a physics-based modeling (PSM: Physical Solar Model) approach to provide solar radiation data for the entire United States in gridded segments (4x4 square kilometer) using geostationary satellites. Data can be accessed via an API.\n", + "\n", + "The [PVWatts v5](https://www.nrel.gov/docs/fy14osti/62641.pdf) model is used to estimate the power output." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\program files\\python\\python36\\lib\\site-packages\\matplotlib\\__init__.py:886: MatplotlibDeprecationWarning: \n", + "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n", + " \"found relative to the 'datapath' directory.\".format(key))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading sub\n", + "Loading bus2sub\n", + "Loading bus\n", + "Loading genbus\n", + "Loading branches\n", + "Loading resources\n", + "Loading net_generation\n", + "Done loading\n" + ] + } + ], + "source": [ + "from westernintnet.westernintnet import win_data as grid\n", + "from prereise.gather.solardata.nsrdb import sam, helpers\n", + "\n", + "import getpass\n", + "from matplotlib import pyplot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Solar plants in Network" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "solar_plant = grid.genbus.groupby('type').get_group('solar')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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busIDPgQgQmaxQminVgmBasestatusPmaxPmin...mu_Qmaxmu_QmintypelatlonGenMWMaxGenMWMinAreaNumZoneNamebase_color
plantID
51107600.000.00.00.01.040069.12061.5013.06...0.00.0000;solar45.584722-122.40560061.50000113.0603571Washington#feb308
57107760.000.00.00.01.031832.07028.003.77...0.00.0000;solar48.448100-122.43310028.0000003.7700001Washington#feb308
581077614.320.00.00.01.031823.98114.3214.32...0.00.0000;solar48.448100-122.43310020.0000006.1453761Washington#feb308
611078213.430.00.00.01.016227.32113.4313.43...0.00.0000;solar46.103887-122.91842918.3333346.3965631Washington#feb308
621078214.650.00.00.01.016218.63114.6514.65...0.00.0000;solar46.103887-122.91842918.3333347.3709761Washington#feb308
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5 rows × 33 columns

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" + ], + "text/plain": [ + " busID Pg Qg Qmax Qmin Vg mBase status Pmax Pmin \\\n", + "plantID \n", + "51 10760 0.00 0.0 0.0 0.0 1.0400 69.12 0 61.50 13.06 \n", + "57 10776 0.00 0.0 0.0 0.0 1.0318 32.07 0 28.00 3.77 \n", + "58 10776 14.32 0.0 0.0 0.0 1.0318 23.98 1 14.32 14.32 \n", + "61 10782 13.43 0.0 0.0 0.0 1.0162 27.32 1 13.43 13.43 \n", + "62 10782 14.65 0.0 0.0 0.0 1.0162 18.63 1 14.65 14.65 \n", + "\n", + " ... mu_Qmax mu_Qmin type lat lon \\\n", + "plantID ... \n", + "51 ... 0.0 0.0000; solar 45.584722 -122.405600 \n", + "57 ... 0.0 0.0000; solar 48.448100 -122.433100 \n", + "58 ... 0.0 0.0000; solar 48.448100 -122.433100 \n", + "61 ... 0.0 0.0000; solar 46.103887 -122.918429 \n", + "62 ... 0.0 0.0000; solar 46.103887 -122.918429 \n", + "\n", + " GenMWMax GenMWMin AreaNum ZoneName base_color \n", + "plantID \n", + "51 61.500001 13.060357 1 Washington #feb308 \n", + "57 28.000000 3.770000 1 Washington #feb308 \n", + "58 20.000000 6.145376 1 Washington #feb308 \n", + "61 18.333334 6.396563 1 Washington #feb308 \n", + "62 18.333334 7.370976 1 Washington #feb308 \n", + "\n", + "[5 rows x 33 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solar_plant.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 391 solar plants in the Western grid.\n" + ] + } + ], + "source": [ + "print(\"There are %d solar plants in the Western grid.\" % len(solar_plant))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Downloading Data\n", + "Visit https://developer.nrel.gov/signup/ to get your API key." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "email=········\n", + "api_key=········\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████| 150/150 [34:53<00:00, 14.93s/it]\n" + ] + } + ], + "source": [ + "email = getpass.getpass(prompt='email=')\n", + "key = getpass.getpass(prompt='api_key=')\n", + "ssc_lib = 'U:\\\\SAM\\\\2017-9-5-r4\\\\win64\\\\'\n", + "data = sam.retrieve_data(solar_plant, email, key, ssc_lib)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PoutplantIDtstsID
00.0512016-01-011
10.0572016-01-011
20.0582016-01-011
30.0612016-01-011
40.0622016-01-011
50.0632016-01-011
60.0642016-01-011
70.0652016-01-011
80.0662016-01-011
90.0672016-01-011
100.0832016-01-011
110.0842016-01-011
120.0942016-01-011
130.01542016-01-011
140.01552016-01-011
150.01562016-01-011
160.02122016-01-011
170.02132016-01-011
180.02142016-01-011
190.02152016-01-011
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" + ], + "text/plain": [ + " Pout plantID ts tsID\n", + "0 0.0 51 2016-01-01 1\n", + "1 0.0 57 2016-01-01 1\n", + "2 0.0 58 2016-01-01 1\n", + "3 0.0 61 2016-01-01 1\n", + "4 0.0 62 2016-01-01 1\n", + "5 0.0 63 2016-01-01 1\n", + "6 0.0 64 2016-01-01 1\n", + "7 0.0 65 2016-01-01 1\n", + "8 0.0 66 2016-01-01 1\n", + "9 0.0 67 2016-01-01 1\n", + "10 0.0 83 2016-01-01 1\n", + "11 0.0 84 2016-01-01 1\n", + "12 0.0 94 2016-01-01 1\n", + "13 0.0 154 2016-01-01 1\n", + "14 0.0 155 2016-01-01 1\n", + "15 0.0 156 2016-01-01 1\n", + "16 0.0 212 2016-01-01 1\n", + "17 0.0 213 2016-01-01 1\n", + "18 0.0 214 2016-01-01 1\n", + "19 0.0 215 2016-01-01 1" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head(n=20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Plot Data\n", + "One solar plant is selected. The power output (in MW) is then plotted for the entire year for 72-h in June." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Capacity of the plant: 61.5 MW\n" + ] + } + ], + "source": [ + "id = 51\n", + "print(\"Capacity of the plant: %.1f MW\" % solar_plant.loc[id].GenMWMax)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "data_oneplant = data[data.plantID == id]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pyplot.figure(figsize=(16,8))\n", + "pyplot.plot(data_oneplant['ts'], data_oneplant['Pout'], lw=3)\n", + "pyplot.title('Pout', y=0.75, loc='right', fontsize=20)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pyplot.figure(figsize=(16,8))\n", + "pyplot.plot(data_oneplant[4000:4072]['ts'], data_oneplant[4000:4072]['Pout'], lw=3)\n", + "pyplot.title('Pout', y=0.75, loc='right', fontsize=20)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Capacity Factor: 0.142\n" + ] + } + ], + "source": [ + "print(\"Capacity Factor: %.3f\" % (data_oneplant['Pout'].sum() / (366 * 24 * solar_plant.loc[id].GenMWMax)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Format Data\n", + "Data are formated for REISE" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "data_reise = helpers.to_reise(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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""" + ts = data['ts'].unique() plantID = data[data.tsID == 1].plantID.values diff --git a/prereise/gather/solardata/nsrdb/nsrdb.py b/prereise/gather/solardata/nsrdb/naive.py similarity index 79% rename from prereise/gather/solardata/nsrdb/nsrdb.py rename to prereise/gather/solardata/nsrdb/naive.py index 45708931a..a73146065 100644 --- a/prereise/gather/solardata/nsrdb/nsrdb.py +++ b/prereise/gather/solardata/nsrdb/naive.py @@ -6,13 +6,17 @@ def retrieve_data(solar_plant, email, api_key, year='2016'): - """Retrieve irradiance data from NSRDB. - - :param solar_plant: pandas DataFrame with the following structure: \ - ['plantID'(index), 'lat', 'lon', 'GenMWMax']. - :param year: year. - :return: pandas DataFrame with the following structure: ['Pout', \ - 'plantID', 'ts', 'tsID']. The power output is in MW. + """Retrieve irradiance data from NSRDB and calculate the power output \ + using a simple normalization. + + :param pandas solar_plant: data frame with *'lat'*, *'lon'* and \ + *'GenMWMax' as columns and *'PlantID'* as index. + :param str email: email used for API key \ + `sign up `_. + :param str api_key: API key. + :param str year: year. + :return: (*pandas*) -- data frame with *'Pout'*, *'plantID'*, *'ts'* \ + and *'tsID'* as columns. The power output is in MWh. """ # Information on solar plants @@ -46,10 +50,6 @@ def retrieve_data(solar_plant, email, api_key, year='2016'): 'utc={utc}'.format(utc=utc) + '&' + \ 'email={email}'.format(email=email) + '&' + \ 'attributes={attr}'.format(attr=attributes) - #'full_name={name}'.format(name=name) + '&' + \ - #'affiliation={affiliation}'.format(affiliation=affiliation) + '&' + \ - #'mailing_list={mailing_list}'.format(mailing_list=list) + '&' + \ - #'reason={reason}'.format(reason=reason) + '&' + \ data = pd.DataFrame({'Pout': [], 'plantID': [], 'ts': [], 'tsID': []}) diff --git a/prereise/gather/solardata/nsrdb/sam.py b/prereise/gather/solardata/nsrdb/sam.py new file mode 100644 index 000000000..ac7f08a0a --- /dev/null +++ b/prereise/gather/solardata/nsrdb/sam.py @@ -0,0 +1,159 @@ +from collections import OrderedDict +from datetime import timedelta + +import numpy as np +import pandas as pd +from py3samsdk import PySSC +from tqdm import tqdm + + +def get_frac(): + """Return fraction of solar plants using no tracking (fix), single-axis \ + or double-axis tracking in Western Interconnect. + """ + + return [0.2870468, 0.6745755, 0.0383777] + + + +def retrieve_data(solar_plant, email, api_key, ssc_lib, year='2016'): + """Retrieve irradiance data from NSRDB and calculate the power output \ + using the System Advisor Model (SAM). + + :param pandas solar_plant: data frame with *'lat'*, *'lon'* and \ + *'GenMWMax' as columns and *'PlantID'* as index. + :param str email: email used for API key \ + `sign up `_. + :param str api_key: API key. + :param str ssc_lib: path to System Advisor Model (SAM) SAM Simulation \ + Core (SSC) library. + :param str year: year. + :return: (*pandas*) -- data frame with *'Pout'*, *'plantID'*, *'ts'* \ + and *'tsID'* as columns. The power output is in MWh. + """ + + # SAM only takes 365 days. + try: + leap_day = (pd.Timestamp('%s-02-29-00' % year).dayofyear - 1) * 24 + is_leap_year = True + dates = pd.date_range(start="%s-01-01-00" % 2015, + freq='H', periods=365*24) + dates = dates.map(lambda t: t.replace(year=int(year))) + except ValueError: + is_leap_year = False + dates = pd.date_range(start="%s-01-01-00" % year, + freq='H', periods=365*24) + + # Information on solar plants + n_target = len(solar_plant) + + # Identify unique location + coord = OrderedDict() + for i in range(n_target): + key = (str(solar_plant.lon.values[i]), + str(solar_plant.lat.values[i])) + if key not in coord.keys(): + coord[key] = [(solar_plant.index[i], + solar_plant.GenMWMax.values[i])] + else: + coord[key].append((solar_plant.index[i], + solar_plant.GenMWMax.values[i])) + + # Build query + attributes = 'dhi,dni,wind_speed,air_temperature' + interval = '60' + utc = 'true' + + # URL + url = 'http://developer.nrel.gov/api/solar/nsrdb_psm3_download.csv?' + url = url + 'api_key={key}'.format(key=api_key) + + payload = 'names={year}'.format(year=year) + '&' + \ + 'leap_day={leap}'.format(leap='false') + '&' + \ + 'interval={interval}'.format(interval=interval) + '&' + \ + 'utc={utc}'.format(utc=utc) + '&' + \ + 'email={email}'.format(email=email) + '&' + \ + 'attributes={attr}'.format(attr=attributes) + + data = pd.DataFrame({'Pout': [], 'plantID': [], 'ts': [], 'tsID': []}) + + for key in tqdm(coord.keys(), total=len(coord)): + query = 'wkt=POINT({lon}%20{lat})'.format(lon=key[0], lat=key[1]) + + info = pd.read_csv(url+'&'+payload+'&'+query, nrows=1) + tz, elevation = info['Local Time Zone'], info['Elevation'] + + data_resource = pd.read_csv(url + '&' + payload + '&' + query, + skiprows=2) + data_resource.set_index(dates + timedelta(hours=int(tz.values[0])), + inplace=True) + + # SAM + ssc = PySSC(ssc_lib) + + resource = ssc.data_create() + ssc.data_set_number(resource, 'lat', float(key[1])) + ssc.data_set_number(resource, 'lon', float(key[0])) + ssc.data_set_number(resource, 'tz', tz) + ssc.data_set_number(resource, 'elev', elevation) + ssc.data_set_array(resource, 'year', data_resource.index.year) + ssc.data_set_array(resource, 'month', data_resource.index.month) + ssc.data_set_array(resource, 'day', data_resource.index.day) + ssc.data_set_array(resource, 'hour', data_resource.index.hour) + ssc.data_set_array(resource, 'minute', data_resource.index.minute) + ssc.data_set_array(resource, 'dn', data_resource['DNI']) + ssc.data_set_array(resource, 'df', data_resource['DHI']) + ssc.data_set_array(resource, 'wspd', data_resource['Wind Speed']) + ssc.data_set_array(resource, 'tdry', data_resource['Temperature']) + + for i in coord[key]: + data_site = pd.DataFrame({'ts': pd.date_range( + start='%s-01-01-00' % year, + end='%s-12-31-23' % year, + freq='H')}) + data_site['tsID'] = range(1, len(data_site)+1) + data_site['plantID'] = i[0] + + for j, axis in enumerate([0, 2, 4]): + core = ssc.data_create() + ssc.data_set_table(core, 'solar_resource_data', resource) + ssc.data_set_number(core, 'system_capacity', i[1] * 1000.) + ssc.data_set_number(core, 'dc_ac_ratio', 1.1) + ssc.data_set_number(core, 'tilt', 30) + ssc.data_set_number(core, 'azimuth', 180) + ssc.data_set_number(core, 'inv_eff', 94) + ssc.data_set_number(core, 'losses', 14) + ssc.data_set_number(core, 'array_type', axis) + ssc.data_set_number(core, 'gcr', 0.4) + ssc.data_set_number(core, 'adjust:constant', 0) + + mod = ssc.module_create('pvwattsv5') + ssc.module_exec(mod, core) + if j == 0: + Pout = get_frac()[j] * \ + np.array(ssc.data_get_array(core, 'gen')) / 1000 + else: + Pout = Pout + \ + get_frac()[j] * \ + np.array(ssc.data_get_array(core, 'gen')) / 1000 + + ssc.data_free(core) + ssc.module_free(mod) + + if is_leap_year is True: + data_site['Pout'] = np.insert(Pout, leap_day, + Pout[leap_day-24:leap_day]) + else: + data_site['Pout'] = Pout + + data = data.append(data_site, ignore_index=True, sort=False) + + ssc.data_free(resource) + + data['plantID'] = data['plantID'].astype(np.int32) + data['tsID'] = data['tsID'].astype(np.int32) + + data.sort_values(by=['tsID', 'plantID'], inplace=True) + data.reset_index(inplace=True, drop=True) + + return data diff --git a/prereise/gather/winddata/rap/demo/rap_demo.ipynb b/prereise/gather/winddata/rap/demo/rap_demo.ipynb index de1429d02..8050057b9 100644 --- a/prereise/gather/winddata/rap/demo/rap_demo.ipynb +++ b/prereise/gather/winddata/rap/demo/rap_demo.ipynb @@ -24,16 +24,11 @@ "metadata": {}, "outputs": [], "source": [ - "from westernintnet.westernintnet import WesternIntNet\n", - "import sys\n", + "from westernintnet import westernintnet\n", + "from prereise.gather.winddata.rap import rap, impute, helpers \n", "\n", - "sys.path.append(\"../\")\n", - "\n", - "import rap\n", - "import impute\n", "import pandas as pd\n", - "from matplotlib import pyplot\n", - "from helpers import to_reise" + "from matplotlib import pyplot" ] }, { @@ -68,7 +63,7 @@ } ], "source": [ - "grid = WesternIntNet()\n", + "grid = westernintnet.WesternIntNet()\n", "wind_farm = grid.genbus.groupby('type').get_group('wind')" ] }, @@ -582,7 +577,7 @@ "metadata": {}, "outputs": [], "source": [ - "data_reise = to_reise(data)" + "data_reise = helpers.to_reise(data)" ] }, { diff --git a/prereise/gather/winddata/rap/helpers.py b/prereise/gather/winddata/rap/helpers.py index 8eef45e0d..f5a7d5ed0 100644 --- a/prereise/gather/winddata/rap/helpers.py +++ b/prereise/gather/winddata/rap/helpers.py @@ -11,10 +11,11 @@ def ll2uv(lon, lat): """Convert (longitude, latitude) to unit vector. - :param lon: longitude of the site (in deg.) measured eastward from \ + :param float lon: longitude of the site (in deg.) measured eastward from \ Greenwich, UK. - :param lat: latitude of the site (in deg.). Equator is the zero point. - :return: 3-components (x,y,z) unit vector. + :param float lat: latitude of the site (in deg.). Equator is the zero \ + point. + :return: (*tuple*) -- 3-components (x,y,z) unit vector. """ cos_lat = math.cos(math.radians(lat)) sin_lat = math.sin(math.radians(lat)) @@ -32,9 +33,9 @@ def ll2uv(lon, lat): def angular_distance(uv1, uv2): """Calculate the angular distance between two vectors. - :param uv1: 3-components vector as returned by :py:func:`ll2uv`. - :param uv2: 3-components vector as returned by :py:func:`ll2uv`. - :return: angle (in degrees). + :param tuple uv1: 3-components vector as returned by :func:`ll2uv`. + :param tuple uv2: 3-components vector as returned by :func:`ll2uv`. + :return: (*float*) -- angle (in degrees). """ cos_angle = uv1[0]*uv2[0] + uv1[1]*uv2[1] + uv1[2]*uv2[2] if cos_angle >= 1: @@ -49,9 +50,9 @@ def angular_distance(uv1, uv2): def get_power(wspd, turbine): """Convert wind speed to power using NREL turbine power curves. - :param wspd: wind speed (in m/s). - :param turbine: class of turbine. - :return: normalized power. + :param float wspd: wind speed (in m/s). + :param str turbine: class of turbine. + :return: (*float*) -- normalized power. """ match = (PowerCurves['Speed bin (m/s)'] <= np.ceil(wspd)) & \ (PowerCurves['Speed bin (m/s)'] >= np.floor(wspd)) @@ -66,9 +67,9 @@ def get_power(wspd, turbine): def to_reise(data): """Format data for REISE. - :param data: pandas DataFrame as returned \ - by :py:func:`prereise.gather.winddata.rap.rap.retrieve_data`. - :return: pandas DataFrame formated for REISE. + :param pandas data: data frame as returned \ + by :func:`prereise.gather.winddata.rap.rap.retrieve_data`. + :return: (*pandas*) -- data frame formated for REISE. """ ts = data['ts'].unique() plantID = data[data.tsID == 1].plantID.values diff --git a/prereise/gather/winddata/rap/impute.py b/prereise/gather/winddata/rap/impute.py index d8fbdfbce..0e70a69ec 100644 --- a/prereise/gather/winddata/rap/impute.py +++ b/prereise/gather/winddata/rap/impute.py @@ -2,7 +2,7 @@ import pandas as pd from tqdm import tqdm -from .helpers import get_power +import prereise.gather.winddata.rap.helpers def simple(data, wind_farm, inplace=True): @@ -12,11 +12,11 @@ def simple(data, wind_farm, inplace=True): are first found for each missing entry. Then, a U and V value are randomly generated between the respective derived ranges. - :param data: pandas DataFrame as returned \ + :param pandas data: data frame as returned \ by :py:func:`prereise.gather.winddata.rap.rap.retrieve_data`. - :param wind_farm: pandas DataFrame of wind farms. - :param inplace: should the imputation be done in place - :return: pandas DataFrame with missing entries imputed. + :param pandas wind_farm: data frame of wind farms. + :param bool inplace: should the imputation be done in place + :return: (*pandas*) -- data frame with missing entries imputed. """ if inplace: @@ -54,7 +54,8 @@ def simple(data, wind_farm, inplace=True): data_impute.at[j, 'V'] = minV + (maxV - minV) * np.random.random() wspd = np.sqrt(data.loc[j].U**2 + data.loc[j].V**2) capacity = wind_farm.loc[k].GenMWMax - data_impute.at[j, 'Pout'] = get_power(wspd, 'IEC class 2') * capacity + data_impute.at[j, 'Pout'] = helpers.get_power( + wspd, 'IEC class 2') * capacity if not inplace: return data_impute diff --git a/prereise/gather/winddata/rap/rap.py b/prereise/gather/winddata/rap/rap.py index 817ada04c..0f2e4bfbf 100644 --- a/prereise/gather/winddata/rap/rap.py +++ b/prereise/gather/winddata/rap/rap.py @@ -10,19 +10,21 @@ from netCDF4 import Dataset from tqdm import tqdm -from .helpers import angular_distance, get_power, ll2uv +import prereise.gather.winddata.rap.helpers -def retrieve_data(wind_farm, start_date='2016-01-01', end_date='2017-12-31'): +def retrieve_data(wind_farm, start_date='2016-01-01', end_date='2016-12-31'): """Retrieve wind speed data from NOAA's server. - :param wind_farm: pandas DataFrame with the following structure: \ - ['plantID'(index), 'lat', 'lon', 'GenMWMax']. - :param start_date: start date. - :param end_date: end date (inclusive). - :return: pandas DataFrame with the following structure: ['PlantID', 'U', \ - 'V', 'Pout', 'ts', 'tsID']. The power output is in MW and the U and \ - V component of the wind speed 80-m above ground level are in m/s. + :param pandas wind_farm: data frame with *'lat'*, *'lon'* and \ + *'GenMWMax'* as columns and *'plantID'* as index. + :param str start_date: start date. + :param str end_date: end date (inclusive). + :return: (*tuple*) -- First element is a pandas data frame with \ + *'PlantID'*, *'U'*, *'V'*, *'Pout'*, *'ts'* and *'tsID'* as columns. \ + The power output is in MWh and the U and V component of the wind \ + speed 80-m above ground level are in m/s. Second element is a list \ + of missing files. """ # Information on wind farms @@ -70,7 +72,7 @@ def retrieve_data(wind_farm, start_date='2016-01-01', end_date='2017-12-31'): step = datetime.timedelta(hours=1) for i, file in tqdm(enumerate(files), total=len(files)): - if i != 0 and i % 1000 == 0: + if i != 0 and i % 2500 == 0: time.sleep(300) query = file + var + '&' + box + '&' + extension request = requests.get(query) @@ -93,9 +95,9 @@ def retrieve_data(wind_farm, start_date='2016-01-01', end_date='2017-12-31'): # The angular distance is calculated once. The target to grid # correspondence is stored in a dictionary. for j in range(n_target): - uv_target = ll2uv(lon_target[j], lat_target[j]) - angle = [angular_distance(uv_target, - ll2uv(lon_grid[k], lat_grid[k])) + uv_target = helpers.ll2uv(lon_target[j], lat_target[j]) + angle = [helpers.angular_distance(uv_target, + helpers.ll2uv(lon_grid[k], lat_grid[k])) for k in range(n_grid)] target2grid[id_target[j]] = np.argmin(angle) @@ -104,8 +106,8 @@ def retrieve_data(wind_farm, start_date='2016-01-01', end_date='2017-12-31'): data_tmp['V'] = [v_wsp[target2grid[id_target[j]]] for j in range(n_target)] wspd = np.sqrt(pow(data_tmp['U'], 2) + pow(data_tmp['V'], 2)) - data_tmp['Pout'] = [get_power(val, - 'IEC class 2') * capacity_target[j] + data_tmp['Pout'] = [helpers.get_power(val, 'IEC class 2') * + capacity_target[j] for j, val in enumerate(wspd)] tmp.close() diff --git a/prereise/gather/winddata/te_wind/demo/te_wind_demo.ipynb b/prereise/gather/winddata/te_wind/demo/te_wind_demo.ipynb index 6be88ad05..b37f54acb 100644 --- a/prereise/gather/winddata/te_wind/demo/te_wind_demo.ipynb +++ b/prereise/gather/winddata/te_wind/demo/te_wind_demo.ipynb @@ -15,17 +15,7 @@ "metadata": {}, "outputs": [], "source": [ - "import sys\n", - "sys.path.append(\"..\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import te_wind" + "from prereise.gather.winddata.te_wind import te_wind" ] }, { @@ -38,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "scrolled": true }, @@ -58,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -143,7 +133,7 @@ "4 121182 46.355736 -117.400574 16.0 0.351" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -162,9 +152,18 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\program files\\python\\python36\\lib\\site-packages\\matplotlib\\__init__.py:886: MatplotlibDeprecationWarning: \n", + "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n", + " \"found relative to the 'datapath' directory.\".format(key))\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -176,31 +175,26 @@ "Loading branches\n", "Loading resources\n", "Loading net_generation\n", - "Load solar data\n", - "Load wind data\n", - "Load hydro data\n", - "Load demand data\n", "Done loading\n" ] } ], "source": [ - "from westernintnet.westernintnet import WesternIntNet\n", - "win = WesternIntNet()" + "from westernintnet.westernintnet import win_data" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "wind_farm_bus = win.genbus.groupby('type').get_group('wind')" + "wind_farm_bus = win_data.genbus.groupby('type').get_group('wind')" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -425,7 +419,7 @@ "[5 rows x 33 columns]" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -443,7 +437,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "scrolled": true }, @@ -452,7 +446,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 243/243 [00:10<00:00, 22.20it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 243/243 [00:10<00:00, 22.39it/s]\n" ] } ], @@ -462,7 +456,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -542,7 +536,7 @@ "52 122079 10.0 289.906536" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -560,7 +554,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -578,7 +572,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "scrolled": true }, @@ -592,7 +586,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -5239,7 +5233,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -5251,7 +5245,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -5456,7 +5450,7 @@ "[5 rows x 69 columns]" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -5467,27 +5461,38 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\program files\\python\\python36\\lib\\site-packages\\matplotlib\\__init__.py:886: MatplotlibDeprecationWarning: \n", + "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n", + " \"found relative to the 'datapath' directory.\".format(key))\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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eIihxVygUceHxBbjn0d3c9/he2nvcMb2mszco7gM3VPf+CewNsP5L4V8052Lp5XJ2OxCM3JO0odpqd5NvHaFSpuUwFCwaeT7qJGPqrFShUEwKHt1ew8nWXvwBjZcONsX0mk6HB6vZgEE/QHIOPwMFS6SbYjjKzwOhg7NvAyFxT86GastIkbum9Yv7FEKJu0KhiIutJ9uZX5DOvHwLz++PzSOw2+klyzIgavc44OwOmHdZ5KYgc7oU/+AgjFSjHo8/gD+Q2BaaXrcPh8cf2RqhtxUc7VC4JKHvm2yUuCsUirhosjkpz07l6iVFvH26A48vejTd6fCQlTpgM/XsDvB7oGL9yC8sWw11uyEQIMUo5SrRqZmWaBOimg/JRxW5KxSK6UxTt4uizFTm5lkIaNBsc0V9TZdjiPXA6X+BziDr20eibC24u6H9BKkmPUDCu1Rbo02IajkiHwtU5K5QKKYpbp+fth4PxZkplAYnFtV3OaO+rsvhGdzAVL8LipbL1MtIlK2Vj3U7STFIcU985C4/nCKmZVoOQVoepE8ts0Ml7gqFImaau2WUW5SZ0jeOrr4zurh3O72DrQeaD8eWw85dAIZUaDlMiik54t4XuUeqlmk5MqXq20MocVcoFDETatMvyUztG2zRECVy1zQNu8vXN02JnhY5izQWcdfpIG8+tFaTagymZTyJrZhptbsx6MTgMs0QgQC0HIUCJe4KhWIa0xTMrxdlppBi1JOXbqahe2Rxd/sC+AIa6SFxD3V7xrpBmVcFbdX9G6oJ7lJtscshIjpdmKqdrjPg7Z2S4h6TK6RCoVBAv3tiKGovzUqhvmvkDdXQUGtrSjAybg6Je4wblPlVcPApLMj0SaK7VFvtYSZEnXgVXvoS5M4DhKy5n2IocVcoFDHT1O0kI8WAJWiwVZKVyvGWnhFfY3fJuacZfZH7IbDkx75BmVcpX+84AySnFLJk4OxUvw/++WVoPy7/O++TULAwoe85HihxVygUMVPfNXjOaElWKq8fa0XTtIimWz1BcU8POS42HYivIShfCmu6/RSQm5RSyJXlmf0HDv4N2o7BtT+Sc13Puyeh7zdeKHFXKBQxc7K1h4VF1r6vy7JTcXj8dPR6yI3QBBSK3K0pRmkW1nwILrgv9jfNqQChJ812EshNaOTu8wdo73UPbmA68ixklMHau6bMSL1wqA1VhUIREy6vn5r2XhYU9ov7nDwLAGfaeyO+zh7MuaebDTJqD/igdHXsb2wwQdYszN2ng+tIXLVMR68HTYP8jGBaxuuCk5uh8l1TWthBibtCoYiRk609BDSoLOxvPKoIivup1hHE3R2K3A3QsEceLF0V35vnzsPYJcU9kWmZYdYDNW/K6pjKDQl7j4lCibtCoYiJY812ACoHRO6lWakYdILTbSNF7gPEvX43WIshoyS+N8+Zh+g8BWgJTcuEGpj6qmWObZRNU3MvTth7TBRK3BUKRUwca+7BoBPMybX0HTPodczKTRtR3AdtqNZujy8lEyKnAuHpodhgT2jk3t+dapbWvsf+Kc3MjBEGZU8hlLgrFIqYONZkZ26eBZNhsGxU5FmiRO5e0kx6DN010FUT3QkyHLnzAKgytCR0juogX5nWo9BVK/Pt0wAl7gqFIio9bh9vnWzjvIqcYc/NDYp7IILPut3lk1H7qc3yQMX6+BcQHEw9T9+c8Mg9I8VAilEvo3ZQ4q5QKGYO/zzYhMsb4N3nlA57bnauBbcvQLM9fKdqjzvoK3NqiywxzJ0f/wKyZoPOwFxdE+4Y/ONjpWVgd+qxjdKpMt79gEmKEneFQhGV5/Y1MCsnjVWzsoc9VxgsIwzlr4dic3ll5F6zVW5UjqbEUG8AawlFtONOYClkk80l1+/okANEpkGVTAgl7gqFIipnOxwsL8sM24Waly592tsiDMvucfuYbeyS4+pK4iyBHEhGMflaJ+4EGofVdTopy06FE6+AFlDirlAoZhY9bl+/fQBAT2vfhKK8YI14m90T9rV2l4/F2kn5RcnK0S/CWkSe1p6wtIzL66fV7qYsOw2Ovyz9bkrOSci1JwNK3BUKRVR63b4+szCObYSfr4IHL4JjL/flrFsjRO52l5f5/hMg9FC4dPSLsJaQE+hImLiHfOhLM1PkfkDFZdI/fpowfb4ThUKRFAIBDYfXjyU4CYnN34X0Aulx/tRHSAk4sZoNEXPuPS4fs93HpQGYKW30C8koJlVzoPPaR3+NAdQFJ0gtELUyZVSxPiHXnSwocVcoFCPi9PrRNGTk3l0Pje/Ayg/Au74Lnh44sYk8qzlszj0Q0Oj1+ClxVo8tJQOysxWwutvGdp0godmvs7p3ygMVlybkupMFJe4KhWJEej2yw9RiNvTXglddC7MvkIOjDz9LXropbOTu9PrJpAeLtyP2yUuRCIp7hi8x4l7X6UCvE2Q0vClntWaWJeS6k4WYxF0IsUEIUS2EOCGE+HKEc24TQhwWQhwSQvw5sctUKBQTRa9bVqdYzHrpdZ49V05H0ulh0fVwbCOllkDYyL3X42O+qJdf5FWNbSFBcc/ytY7tOkHqOp2UZ+jR1WyddikZiEHchRB64AHgGmAxcIcQYvGQcxYAXwEu1DRtCfCZJKxVoVBMAL1BV8cS+yGoeWuwz/ny28Hby2Wef9HWM7xaxuH2s0AXFPf8yrEtJCMo7oGOsV0nSG2Hg0sttdIFct5lCbnmZCKWyP1c4ISmaac0TfMAjwM3DTnn48ADmqZ1Amia1pLYZSoUiomi1+3DjIeFB38EKZmw+kP9T85aB4VLuaDj73Q7PcNq0EORu1+fApmzxrYQkwWXPp1cf/vYrgO4fX4ONdi4wnQYhA7mXDTma042YhH3UuDsgK/rgscGUglUCiHeEkJsF0KE7QQQQtwthNglhNjV2pqYWyuFQpFcej0+vm98iIyWnbDh+2Dut/xFCFjzEfJ7j1MlztI+JHp3ePzMFw04M+YmpMyw15RHrtaBpoX3sYmV/XXdeHwBVjp3QOka+aE1zYjlpx2uV3joT9YALADWA3cAvxFCZA17kaY9pGnaGk3T1uTnxzgcV6FQTCg9bj8X6w5gr7oFVt4x/IQF0mjrQt2hYXl3h8fPfF09nuwFCVmLy5RDrrDhi2BSFitvn+5gnqgno+sQLH1PQtY22YhF3OuA8gFflwENYc55RtM0r6Zpp4FqpNgrFIopjreng1xhRwsOqh5GVjmujAou0h0YJu4uRy8ltOPPGYVZWBhc5jzy6RpzI9OO0x18LGOnbKxa+t6ErG2yEYu47wQWCCHmCiFMwO3As0PO+QdwGYAQIg+ZpjmVyIUqFIqJQR8cb2fIjxyveWdfwnm6I7R39wx+oqsGndDQBS17x4onJY880Y17DLa/mqbRfPYkN/tegvlXyoasaUhUcdc0zQfcC2wEjgBPaJp2SAjxbSHEjcHTNgLtQojDwGbgi5qmjX3XQ6FQTDgp3WcAMBdEjr7NlZdjEW70oRmpQQzB1xryEiPu3tR8MoQTtyvycJBotPd6+IrvVxjxwYb/Tci6JiOG6KeApmkvAi8OOfb1Af+vAZ8L/qdQKKYRlt4zBDSBIW9exHNM8y/BrwlymrcCt/YdN9tr5WNB5NfGgy81DwC/rQXyhg8OiYXGA1tYr9/HqeVfpiI3MeuajKgO1ZnG/ifg5a9B3a6JXoliimDtraVZ5IIxJfJJqdkc08+nvGvHoMNpPbX0aCmYMxKT+gikyUIMv71p1NfI3vNz2jUrlovuTsiaJitK3GcSbjv845Ow9efwzL1yILBCEYVs11nqddGnEx1JXcUc1xFw2fqOWR111FGISJDbohbMj2v2UbbSeHopbt3KP8TlFOSMLvKfKihxn0nUbIWAD/fiW6H1CJx5Y6JXpJjsaBr57loaDdF9V2oyz0VPYNDvVaa7jgZdUeLWYwneAfSOUtzrdqHHT0PWmrCDR6YTStxnEqe24MbE2j1X4zBkws7fTvSKFJMdWz1pWi+N5rlRT+3OW00vKXB8kzzg95HtaaTFkLiZpLp0mZbRjVbca7cTQOAtXp2wNU1WlLjPILSTm3k7UIUNC1uMl0iHP3divLEV05TgtKXW1OgbjzmZ6bzpX4p2/GWZ8qvdilHzcso8RsOwAZjMqXRq6eh6R9fhrtVu42hgFhlZeQlb02RFiftMwdWNaD3Cdr+0XX3KfS74XHKqjmJm4HMPyofHRMthADrTo5cy5qWb2RJYgbAFPd+PPIcHE0fS1o1mtWExG3W0apkYnaMQ90AAzr7NrkAlORZTwtY0WVHiPlNoOgjAIW0Oq2Zlsdkxl0B6kayeUUx/fG545Dr46XI4uTn2zfSWI7SSg0iNvvmYl27iZf8afCk58OSH4dDT7DatRp9qGdvaB2A26GjScjA5GuN/cdcZhLeXg9occtOVuCumC00HADgUmM3auTlo6GiZfxsc3wgtRyd4cYqk8Nx/wG+ugm0PwHOfgbqdYEyDP90MD62H1uro12g5zDGtjIxUY9RT861m2slkz4UPynSfp5fn9VdgMcXUThMTZoOeRi2XVOcoSiGb5V1IdaCc7DQl7orpQtMBXKZcWsni3DkyCttddJv8Y3/9e9OvLFLTZLQ6U2k7AbsfAVsDbPwv2PdnWP9f8KkdcN2PobsOHr4CWo9FvkZPK1rzYfb555CREl3cCzJkHfwJ0yL44kn4aiOvBVaRFpq9mgDMBh2N5JDibgffcP/4EWk+hIbgmFam0jKKaUTzAVotCwDBqlnZ6ARU20xw4Wfg0NPwzKegfk/Uy0wJAn74653wi7XgGX2b+pTm4FOAgI+9DO/6X7jx57D+S9Kud+3H4O4tYDDLn5PfG/4a7zyKCHj5m/9iMlKjR9/56WYAWuyuvmEevW6fHM+XIMxGHQ1aLgIN7EP9C6PQcoietDKcpChxV0wT/F5oOcJZ83xSjXqyLSZKs1M51dYLl/4nrPsU7HscfnMlVL800asdO2/+GI4+D101sPUXE72aieHAU3IARWYpnP/vsOrfBj+fVQ7XfB/aqqF22/DXu2yw83e4Ss/npFYaU+RuMujIsZhoCc5S1TQNh8ef0MjdpNfRqOXKL7rr43tx8+G+qh8l7orpQdsx8Hs4rptLvlVGV7Ny0uT0dyFgw3fhP0/K6fRPfgQ6prChp88D2x+Eyg2w6AbZjTvTond7E7Qfh6prRj6vcgPoTYMrppydMl3z4IVgb6BxxX0AMeXcAQqsZlpsUtzdvgC+gJbQyN2g19FCsIzRFoe4+9zQcZJ601zSTHpSjIn7wJmsKHGfCQQ3Uw/6Z5EXrBIoyUylvtPZf05qNrzvUdAZ4PnPgd83ESsdO8c3gqMN1nwUzv0EeOyynn8mUb9bPpauGfk8c7qM7geK+97HoH4XpBfBe39LY865AGSkxCbQ+VYzrXYXAE3d8rEwYwRPmlHQYQgO+umui/1FXbWgBagVJTMiagcl7jODpgNgSOWAM4+8YF60JCuVFrt78MzLjBK46ltwajP8+TbwuiZowWNg3+NSmOZdAbMvAGuJTFHMJOp3yw/p4uXRz13wLhnld5ySdeA7fwPl6+CuTbDkZmwumY+3xpCWASiwpvSlZRq6ZPBQkplYcfcbLTj01vgi984zAJz25ylxV0wjmvZDwSJaev19aZnS7FQAmruHVJSs/Rhcfz+cfBVe/dZ4r3Rs+H1w+l9Q+S7QG0CnlyPUjm+SkdtMoX43FC4BY2r0cyuvlo/HXobardB5Wv4OBLE55R1cLBuqAAUZZlrtbgIBjYZg5F6cFcM64sBs0NFlKIgvcg+K+3GvEnfFdEHToOkAgcJldDo85AYj99LgH1x9l3P4a9Z8BM67B7b/Erb9cjxXOzYa94HbBhWX9h9b90kp8q98S1bRTHf8PqjfC6UxeqfkVEDuApm6OvIc6M1QdW3f06HIPZ6cuy+g0enw0Bj83SpOcORuNuhoNRbHtzfUeQYMKZx0WMiZATXuoMR9+mNvBGcnjpyFaBrkpMk/0pKguDeEE3eAq74tNyQ3fgVe+ebUqIM/vUU+zrmk/1hmGZz/KVka+JMlsv57OnPkGXB3w4KrY39N5bug5i04/AzMu1zm4oPYnF6EgPQYG5EKrFLIW+xuGrpd5FoLGtsUAAAgAElEQVRMCd+8NBv01BtmQfvJ2GvdO89A9hw6HF6yVeSumBa0ySaV7nRZAhb6xQ5FUxHF3WCGW/8gNybf/InscpzsnNoCBUsg6BzYx2VfhVt+Jysmnvrw9G1ucnTAWz+T0fiCd8X+uuW3yUd7Iyy6ftBTNpcPq9mAThebPW5hhrwzrOt00tDlpDgrsVE7QJpZT42uDDR/7NF7Zw3+rNk4PH6VllFME9qOA9BqngXQ13adYtSTl26ioTuCuINMZ1z3Y1h4vcy/x9KuPlE4OuDMWzIKHYouOOH+hp/KzeUjz43/+pJJ7Q54YB38oEIadl38BQgzHONgfTebq8NY5RavgPvekY1Oy24b9JTN5Y15MxVgaWkmFpOe14620NjtpDgzsfl2gDSTnlME/eXbYvid1DToPIPbIl+TGWOKaaqjxH2603YMTFZaAlkAgzw1SrJSqescQdxB1sHf8FMQetjxYDJXOjaqX5SR3OIbI5+z8HrILJcVNdOFmq3whxukw+f6r8An3oBzPhD21K8+fYB7/rSbtp4wdy6ZpbLRyTA4qrU5fTHn20EGDZcvKuTlQ03UdTr79nYSSZrJwDF/sfwiloDD0QEeOz1pUtxngq8MKHGfXoTLi7cdh7wFdAWrHrLS+v9Q5+WnU90Ug5+7JQ8W3yRLCj2ORK02sRx+BrJmQfHKyOfodLDsVlkJZG8ev7Uli9422XSWWQYff03aC0Qof6zvcrKvrhu3L8Dv3jwd81vYXN6Ya9xDXLu0iPZeDw6PP+GbqQAWk55OrxEyZ8Um7sGSyW6jTNcN/BuYzihxnw7YGqVHyP9VDfdnD4p7h0NuPA3MNy4tzaTF7qbFFkM9+6oPykqUw/9I5MoTg6tb2tguurHP0yQiS98DWkDW8k91Nn4VHO1w2x8gbbglb4vdxdkOB5qmsfGgdFE8Z1YWD/3rFE/uOhvTW9ic3rgid4DLFhZw57pZfOC8WdywInFTmEKkmQ04PD7Ir4wtLRMcpt2ul52tKi2jmBoEAvDUR+D4K2DOgL/c0VfTi6cXbHWQt4BOhweTXjfI52N5WSYAB+q7o7/P7AuhYLHcsAsEkvCNjIHqf0LAC4tvjn5uwWIwWeHs28lfVyKwN8FLX4ZHb4HXfyA/yABOvAL7H4eLPgNFy8K+9L2/2srFP9jMN549xDPv1FNVaOUPHz2X8ypy+NLf9nOsOfpdm93li8lXZiApRj3/ffMy/ufdy/qqshKJxaSn1+2H/IUyeIlW4ho0GGtDetKoahnF1GDXb6Xx03X/Bx/8u8w7H/ybfC7k055XRVevl2yLcdBQ4MXFGQgRo7gLARd9Tg7WPjbJzMWOPCs7UWOp7dbpoWw11E0BcXfZ4Hfvgp0PS+vezf8DD5wH+/4KT34U8hfJf5Mw2FxeznY4yUw18sdtNeyr6+bO82eTkWLk53eswmI28N0Xj4z49oGARkevZ9KlMdJMBpxeP4HcSrnXEK1BLRi5N/ozAMhSkbsiYfh90lL3lW8l1rOlu07WoFdcBivfL3PO5efBgaC493mMrKLD4Rm2kWQxG5iXn87BWMQdYMm7wVos/UcmC85O2YG6+KawFSJhKTsXmg+Buye5axsrG78ihetDz8G/b4W7XpNR6tN3S+veDzwBprSwLz3TJs3S/nNDFWaDjuw0I7eskhuKORYT91w6jy3VrZxui2yqVt/lxOn1My8/PeI5E4HFLO8+XdkL5IG2ETzpQX4wWvLpcINRLxLqUjmZUeI+Hmz8Cux9VFrRvhA+0oobdw889VGZP77h/v5c89JboOWQHGwcMoDKKKXLET4CWzsnm20n2+lxx/ChozdIgT+xCZxdifk+xsrBv4PfDSvviP015efKn1vDJPav766Dd/4iO4VnXyCPla2W/uw3/wo+vUt+mEcgJNpr5+Two1tX8INbVpA6QNRuPqcUgJcORh5Xd7xFpm2qiiaXuKcFG6p6M4JzXVujTBKzN4G1iC6Hl8xU06C71+mMEvdk01ULbz8Maz8O594tRb4rts2siHid8Jfb5di0m38F2XP6n1tyMwidTM3U7YKyNSAEHb2esM0bt64pp9fj59l3Yhx8sOQ94PfI0sOJRtPgncdk41LR4CqRH286xg0/fzP860pWycfGfbG9z95H5Vi68bQO3vU7QJPiPpCcufIuLYpvzOm2XoSQ1s43rCjhqsWFg54vzUplZXkWLx2IPK6uukne2cwvsI7qW0gWoci9V1jBUjDyNCmQOXdrCV0OD9mTLMWUTJS4J5vdf5BR9YX/ARdIb2zeul8+nngVnv10/C3xz38OzrwJ7/61FPOBpBfA3EukIHWchFIpZF0OL1lh6nvPKc9iYZGVX71+gkMNMaRnytbIP6jT/4pvzdGo2wU1YYZGDCUQgFOvy87E45tk6mnNRwZVyfgDGn/eUcuB+u7w3jmWXJleCg4NHxGfG177b2jYO36DP3xu+XtTeQ1kzx7VJU639VKSmTpi6//VSwo5UN9Ne7i6d+B4s53izJRJV13SF7l7fJBfFVfkPtn2D5KJEvdk4vfCnj/KVvCscvnf6g9LW9Ut35cpmj1/hIcvjz3/G/DLDstz7uxvGx/K0ltkK7nOCPOvIhDQ6HJ6w0YtQgi+eeMSnJ4Atz+0nd5o6RkhZEdj0CMekKL8+g9lp+RoqN8Nj1wHj1wL7/w58nkeBzz6HvjjjfCzc+Dx98tW+1UfGnTa9lPtfY06u850hL9W4VJojkHc9/5J/izzKuGtn/ZtziWVQ/+QnvTn3jXqS5xu66Ui3zLiOZXBiLy2I3zvQnWznQWFkytqB/oGbjs8wYqZ1urI3kc+D/S2QkYJXU6ZlpkpKHFPJkdfgN4W6c8S4pofwPL3wZbvypLF8z4pjZ5irbtu2i8HUFSsj3zOijvglt/DfXugeDl2lw9/QIvYmbeuIpcH71yF3eXjhf2Rc7B9FC2T0ZLPLf+o/vFJ2Pzf8Pgd8TsvBvzwt4/Lu4Gyc+GfX4m86bz3Uflzuvp/5M+xaoNsmR/SVfnigUYsJj1pJj27azojfA9LpSiEM56yNUBnjbzd3/QNmH0RvP+vMh21+X/i+/5Gw86HIXc+zF0/6kucaetlTu7I4l6WE9kZ1Onxc6Klh8qCyZVvB+ktA3I+K4WL5d9Dd4RUZ0+wWc1aPOPSMombf6UYzq7fyi66+Vf0H9Mb4OYHIS1Pzvi86ttyMn31S9KFMRo1W+VjaJMtHHqDbNYJ0hlsYBqp7Xr17Gwq8i08vrOW29aWj7yG4uUQ8MlNW59beoDPu0J2fjbslambWKl+UaaPbn1EWhw88UHpUDjQthdkOmbHr6BsLVxwrzx23ifCXvJgg40V5VkIAbvORBD3wqWyNr6tenCd+MnN8MSHpGDoDGCywHt+LbtAz71bruG8e6RfejJo2Cv3UjZ8P/bqnyE4PX5sLh9FUbpDQ9YA4SwoXjzQiNsXGJarnwwMitwLFsuDLUfCbzDbg8GKtVilZcIhhNgghKgWQpwQQnx5hPNuEUJoQog4/rqnKbYGmZde9W+ytnogOp2cW3r7YzLqnH+V7CyNJeo98xZkz5VTk2KkM0x36lCEELx3VRl7artojtaxGtq8bNovG2kMqcGKHZ3Mg8eKpslUR9ZsWHiD/BA0pMg7nqGcek3m2YduMA67pMbJlh7mF6RzTnk2R5tsg6dN9X0PQUEPlYuCnDz1t7vkz/bCz8Dau+DuLVLYAS75gmwUe/n/xf49xsvbvwGjJb7qnyGEUlKhkYqRsKYYyUw1Dhq36PD4uOOh7fxwYzVz8yycO3d45+tEEypl7HX7ZFoGoOVw+JNtslDAYynE6fVPuv2DZBJV3IUQeuAB4BpgMXCHEGJxmPOswH3AKBOv04yQyC28Lvq5VdfIHGvdrujnNuyRpXxxEBL3aFHLpZXSe2PrybaRL5g9V3Z5nnlTliIuul5GTaWr4fjL0Rdkb5LpkOqXZJR60Wfl3YbJIu8Ajr4wPIe6/0lIyYx6d9Nkc9Hj9rGgIJ15BRYCGpztCLOpmlcpUx8Dc/yHnpb/Dhv+F678hnwcWImUlgOXfFHeodQnoYzS1ih955ffJr/XUdIv7uao55ZmpVLX2Z9zP1hvY9updppsLj64bvakLBsMibvD44fULMgog+YI4h7cI7EbpfVAvFYKU5lYIvdzgROapp3SNM0DPA7cFOa87wA/AKbg4M0kcPxl+UtXsCj6ufOvlCmAaJ2fzi55mxnLNQfQ2Sun6URzw1tcnEFWmpG3TrSPfEGdTjYN7f8ruLpg+e3y+LwrpOXsSDXwXbVw/3J4YK2sFMqdD+d8sP/5hddJy4SGvf3HbI1w9HnpHWMYWbBOtMiN6XkF6czNk/nisI06QsDqj8DZHf1VM28/JEW/Yn3kN1hxh7xDScbQ7de/L+/eLvrMmC7T1iM/zGMR97Ls1EE59+omGwD//MzFfOTCOWNaR7KwmAdUy4D8e2iJ0G1rbwCdEZuQH5bWOE3QpjKxiHspMHC3oi54rA8hxDlAuaZpz490ISHE3UKIXUKIXa2trXEvdsrg88hyvQVXRjeyAhl9zL5ARrIgfUN+ugL2PzH4vFDJV36c4h7KuUfx1NDpBOdX5LLtZDtatMlLF3xaPloK+sVwzkWyOah2e+TXbX9Q5ust+VByDrznYRm1h6i6Rubej74gBf7Xl8KPF4KnRzo6RiEk7vML0pkb3FA83RahEmnl+8GYBm/8SN41NeyRefWR/s0suXLjN9HivvO3sOcPsppq4N3CKOiL3K2xiHsadZ3Ovn/vo012rCkGqgqtkzJqBzlmTyfA4Q6m2woWyb2TcBvx9iawFtMTPDfdrCL3gYT7F+77yxdC6ICfAJ+PdiFN0x7SNG2Npmlr8vPzo50+dTm7XW7IxTPqbOH1Urz/dhf8+XYZrf79bpljDxGKTuKN3B0e9DoRk3Xr+qp86rucbD0ZJXovWAgXfx4u/1q/OJetAb0JaiI0D7ntUsCWvhfuegXufKqvDr+PtBz5QffOY/DYbbKM7cpvwR1/lfX7UTje0kNmqpH8dDOZaUZyLKbILfZpOfJD6tDT8PxnZKppxe1R34OqDbIByhZj41c0jr4gy2LnXwVXfnPMlwvVrefGYJBVmp2Kw+OnyyHv7qqb7CwsmrzCDnJ/yGIyyLQMyM1tv0duzA/F1gAZxdiDs2BV5D6YOmBg+UQZMPC32gosBbYIIc4A64BnZ/Sm6vFNssZ87qXRzw2x6kNBz/Qn5TShz+wPbi4OuBlqPSo32zKjVLMModPhJSvVGNMf7E0rSynOTOH/Xq6OHr1f8XVYPaDG3Jgqq1lO/0t6jR94SpYUhji+SUbgqz888nXXf0V+7wEvfPAfMk1RtSGmu6Azbb3MzbP0fa9z8ywj+qdwwX1QuEzW7a/9qPRsicbCYN7/wFPRz41Gb7tMTxUtg/c9Omh+6Whp6/FgNRtiml1alt1fMaNpGtVNdqqKJl9t+1DSzHpp+wv9wU64TdVg5G4P9m+km2eOuMfyne4EFggh5gL1wO3A+0NPaprWDeSFvhZCbAG+oGlaDLuD0wh3D+z7i7zVP75JRp/x/KEaU+CWR2T0kTtfClnZGlkWGKLliOzIi7NErrPXE7PNaYpRzyfXz+PrzxziYL2NZWVxbuxVboBN/w9+ulLevejNcPdmGV1VvwhpuTBr3cjXmHMh3LdXRmNRcuxDaep2sag4o+/ruXkW3jg+QgrQnA6ffFNaCxjDm3ANI2++TM3s/r2M4F3dMrXW0yyHNq/+MFz6n7Fd680fS/OzDz03rF5/tLT2uGNKyUB/OWR9l4OcdBN2t4+qoowor5p4LCYDvaHIPa9S7oO0HJHeRwOxN8L8K7G7pLjHa188lYmqEpqm+YB7gY3AEeAJTdMOCSG+LYQYYabZDGPrz+DFL0g/9dYjsOCq+K+h00Hegv4Idc5FMqJ0dcs67+aD/XW9cdAZZ/PGdcuKEQJeOTKKaUXn3wuXfkm2zd/+F7lR/NbP5D7EsZdlTn1oaWg4hIhb2DVNo7HbNai+uyLfQrPNTXcw7RARkyW2/ZEQ53xAlmYe2ygrbM7ukB8Qabmw5XuRqzcGYmuQvkMr7kho3Xyb3R21DDJEebb8QKvrdHK6Vd7hzIvS2ToZSDPrcYS6qY2pkDNPOn0OxG2Xd4oZxfQE0zLpMygtE9N3qmnai8CLQ459PcK568e+rCmG2w47fg2mdDj9urzFPufOsV939gX9G5TWIjl1Z+7FcV+my+FlVk6MUSmQm25m9axsXjnSzGevqozvzXQ6uOy/5H8g0zZvPyR/Ru5umW9PEjanD6d38Gi3leVyduye2k4uW1iQuDdb/j45qm/ZLZA7r/94bzv8YrXsZL09ijXy7kfk3cklX0zcupAbqpUx2gZkpBqwmg3UdTr7PFtmR+lsnQxYTIa+VAsgUzNDxd3W38Bkb515aRllP5AIdj8iSwI/8CTc9AB86HlIzR77dcvWygahE6/AydfksYr1cV+mo3e4l3s0rlhUyKEGG2cj+I7EzIWfkdU91S/IqH7e5WO73gg02mRJX9EQcdfrBLtqInjMjBZjqpxZOlDYQVbTrPyAjOidEbpjQfoO7f6DvMPLmZvQpbX3emIqgwS5OVmaLQel13Y4MOoFRRmJn3uaaDJTjdicA+7GCpfIO6mBzp2hIR6ZZfS4fZgNOkyGmSN5M+c7TRY+N2x7QFZyzL5ARuypWYm5tjFVdm0eeV46SBYskRF8HGiaJtuuLfHlGm9YUYxeJ/jjtjNxvW4Y1kLZ5fnRjdJqIYk0dcsWi4GRe5rJwJKSjMg2BMlg2S1yM/jws5HPefsh6Gka7DuUAFxeWfkSq7iD3FSt63RwtsNBWXYaet3krZQJkZVmpHuQuC8FtMH17u1Bt9Xc+dhcPqwzKN8OStzHzrYH5KbNRZ9NzvUX3SAbMc68Iato4sTh8ePxB8iJM3Ivy07jumXF/HlH7eA/otGgN8hN1Fhy7WMgJO5FmYO9zlfPzmZfXVd4+99kULxSborv+m34ebNn3oJNX4eqa6VjaAI53ixr+hcUxr6ZX5olG5lqOxyUx5G+m0gyU4195ZuANIKDwW6lHSdleaslH7vLG1Mp8HRCiftYqNkmvb4X3ShH3SWDynfJXP7cS2KvwBhAR29007BI3H1JBb0eP3/eEWVG5SShsduFEFAwpFLk3eeUoheCa+7/F6328N7lCUUIuancuE/W64Mc4v34B+DVb8OTH5aNSu9+cNTmYJE43Cg9+RcXx17xUpadht3l42BDN7OniLhnpZlwev24vMGKmazZ0vdnoI1z+wmZNhOCHrdvRm2mghL30eP3ysaXzDK4+ZfxVVrEQ2q2LAu88+mo03fCEYpuRuOGt7Q0k4vm5/H7t06HN9+aZDR1u8hPN2PUD/61Xl6WxV/uXofN5WPjoXHwYwfZTVu+Dl7+mvTg+fvHZdfyGz+Wvyvve2xM/jGRONJox2LSx7WBHorWNY24XjeRhAzA+vLuQsi8+8ABLO0n+/ZE7C7fjGpgAiXuo+fth2VT0Ybvxdb4MhbSCwa36MdBLI6QI/HxSyposbvZdHgUZZHjTKPNNSjfPpBlpZnMyU0bv+9DCLm57vfIQSQg6+m/0QmfOyo7fJPA4QYbC4sz0MWRN19fld+Xoy/PiT+AmAhCwUrX0Lx78yGZCvO5pcd7jhT3HpdvRlXKgBL30dHTAlv+VxplVV0z0asZkX5HyNGJ+0Xz88hKM/LakZZELisp1HU4KM0OL05CCK5aXMi2k+19rehJJ28+vPe3cNHn4BOvy1SMEAlPxYQIBDSONNriSsmAbFzb9NlLuO+KBVy8YGrYgmQFJyoNyruXrpaNcy2H5CAcLSD3PgC7y6s2VBVRcHTIYQ5eJ1zz/eSlYxJEZ+/YIne9TnBpZT5bjrUSCESxI5hAfP4AtR2OEacPra8qwOMP8M7ZEVwrE83Ca6V9cE5FUt+mrtPBvX/Zg93tG5UHe7bFxOeuquxzXJzshNIyXY4Bk7Qq1svHk6/B2bfl/wfvkOxuFbkrovHsp6F+l9wMy1sw0auJSofDixCMaUjB5QsL6Oj1sK9uHEUxTuo6nfgCGnPyIov7wqBnyrHmGOfVThEO1ndz+Y9eZ+OhZr5yzUKuX1480UtKOqG0zKBKroxi2VNxcjMceEJ+oBYtJxDQ6HH7Zly1zMz6bsdK+0np4Hfx52Ut8xSgy+EhI8U4ptrli+ZL66C3T3dwzqwENGclgdPtsnmlYgRxz003k2sxcazJPl7LGhd++upxUow6Xrjv4ilTyjhWMsOJO8gmuZ0Py4KH9V8GIXB4fGjazLIeABW5x8fbD0uvlLWjn0o/3nQ6vKNOyYTITTdTnJnCoQZbglaVeEK+KCNF7iDrv4+1TB9xr26ys+lwMx+9aO6MEXYAq9mAXicG59wB1n5M+i+Z0vvsm/vtfmdWzn1mfZSNBU2Dw/+QdecZU+e2t7PXk5ChwEtKMjjU0J2AFSWHM+29WFMMUT3Mqwqt/G1PPZqmTWrP8lh5dHsNJoOOD50/Z6KXMq4IIWQjk9Mz+InceXLzWtP69sN6go6QqhRSEZ7GfbITteraiV5JXEhHyLFbyS4uyeRUW2+/h/Yk4/QQH/dILCi00uP20dA99adBOjw+/rG3nuuXFcds6TydGNalOpABvwc218wzDQMl7rFzbCMg4puuNI40dDl5bl/DsAEbXQ5vQsR9SUkGmiabZCYjJ1t6mBslJQOwoEC25YfG8U1lXjrQhN3t4/ZzZ030UiaEzFRjTNYYKi2jiIymwZFn5fCM9MlXB9zQ5eTWB7dR3+XE4fHxvrX9f+zSEXLsv9RLS2U35cH6blbPnlybql0ODw1DhnREYlZuyL98jG6Xk4Dn9zdQmpXK2jmT699jvMhKM/bNix2JHrdKyygiUbdLelasfH/0cyeA+185Rkevh5XlWXzj2UN90YzL68fp9Sfklr0kM4XSrFS2RZutOgGE7iZiEfcCawoGnaCuc5xMxJJEt8PLG8fbuH558bTYOxgNeelm2ns8Uc+zq5y7IiK7fit335fdOtErGYbHF+CfB5u4ZmkRn7+6Epc3wMF6ufEZykcmIi0jhODC+blsO9WOf5I1Mx1ulFU8sXRm6nWCkqxU6qeIuNtcXj78+7dZ9Z1NfP2Zg3j90mXyuf0N+AIa1y6bOpv7iSbfaqbV7o7aXNejcu6KsNga5CDkFXck30NmFLx5ohWby8f1K4pZUiJTJ6GqltAt61hLIUNcOD+PbqeXw5OsJPJIo428dDP5ccwNnSppmc8+/g5vHm9j9exs/rithv94fC8+f4CH3zjFirJMlsc743YaUWA14wtog/1lwmB3yUY+i0mJu2IgW38hPSou+PREryQsz77TQEaKgYvm55NjMQ2qRw+lHsoi+K3EywXzZDPT1pNtCbleojjSaGNxSTwWt6lTIi1T2+7g1aMtfOqy+Tz8b2v44ruqePFAE/c9vpeadgf3XDpvxqZkgL4P8xb7yJVPdrePdJMhLjO16YAS95ForYadv4Hlt8mBz2Pgm88e4pG3TidoYRKby8s/DzVxw4qSvvFhsh5dintDcDhFSVZixD3faqYsO5WDkyhyd/v8HGu2x+1f3mJ3T3ob48d31qITcPu55QB84pIKqgqtvHigiUsr87l6SXxTuaYbBVbpABrNo38m2v2CqpaJjKbBM/eCyTLm8XB7ajt5ZOsZdAKWl2exKkEt/C/ub8TlDXDrmvK+Y4tLMnntaAtOj5+GLicpRl1CqmX6rl88uZqZjjTa8fo1VsSRngg5RzZ0uWIqn5woXjjQyMUL8ikOTpYy6HXcf/tKXj3SzN2XzJsS4/CSSV/kbhtZ3Htm4Ig9UJF7ZI4+D3Vvw9XfkX7qY+DBLSfJTDVSYE3hxy8fS8jyfP4Av3nzNFWF1kHCtqQkg4AGR5ps1Hc5Kc1KTeit++KSDE5Pomam/UEzs+Xlsc+tDaWpJnPevcXmoqbd0efrE2JRcQb3Xr5gRg16jkRo4lZrlHJIu9s743xlQIl7eAJ+2Py/0gt6+e1jupTL6+e1oy3curqMa5YVsaumIyHpgL/vqedESw+fvWrBIPFeEsw9H26w0dDlTFhKpv/6mZOqmWnf2W7y0k2URBjSEY6SYCTcGGOXqqZp+PxhZqEmkV01cqD32lHY984ULGYDaSZ91Mh9pqZllLiHY88fpeH/Zf816glIIfbXdeMLaKyryGVdRS4ub4D9dWNLa7i8fn7yyjFWlmfxriF519KsVDJTjRxqkJF7ojZTQyzu+/CYHKmZ/XVdLC/LiuvupCAjdDsfm7j/YesZLvjea/3zOseBnWc6SDHq+j6sFeEpsJqjRu4zcQoTKHEfjKbBwb/BK9+EWRfAkveM+ZK7gxHYqtnZnDc3ByFge7ARyOcPjGoq0B+3naGx28WXNiwcJmpCCJaUZLC3tpO2Hk9flJooSjJTsJoNk6J9v9nm4nhLT9wdsylGPZmpRpqjRHwhXjrYRIvdPa4NXLvOdHJOefawebCKweRbzVE/pG0q5z7DcXbC76+Fpz4KmeVw488TMmVpd00nFXkWciwmstJMLCzK4NWjLWiaxk9eOcayb77Mm8fjKy18dl8Da+dkc/683LDPLynJ4GjQszzRaRkhBEWZKTTFGPXGytkOB2c7YsuBN3Q58fkDvHJEzkO9clFh3O9XmGGOWkIH0pxrT638gH55nOav9rp9HG60sWaG2grEQ4E1JXrk7vaqtMy0xdkFW74v/dgj8fLX4OwOuP5+aRmaN3/Mb6tpGntrOwcNuHj/ebN452wXGw8189eddQDc8+huPL7YcrqaplHT5hix9C/UzAQkxQemMCOFphij3ljw+QPc8fB23v3LtwaPTQvDpsPNXPj917jkB5v57RunmZ2bRmVhetzvWZiRElPkvvNMJ16/RoHVzCtHmiH+clwAAB5kSURBVMdl1OA7Z7vwB7RJ5+EzGcm3mmkd4d/R6w/g8gawqrTMNOXvH4ct35UC7gkTHZ7aAnsflY1Kaz4COn1C3ram3UF7r2fQH+kda8upLEznG88epK3HzbLSTHrcPk639cZ0zS6HF7vbx6wRZoVuWFrE165bxNYvXx51eMVoKMxIoXnAZuQ/Dzbx1acP8Mbx1lFd77n9DdR1Omnr8fDDjdURz2vvcfP5J96hqtBKSVYqp9p62bCkaFTVQAXWlJhy7luqWzDqBZ+5spJWu5v99cnfa9h1phMhZCpPMTL5VjN2tw+nJ/x+SJ/1gIrcpyEumxyYW7IKfC4p5APpaYHn/gNy5smxXAkklG8fKO4GvY6vXre4L2r82EVzAahujq36pCaYupg1wtSdFKOeuy6uSHhKJkRRptzECnnM/Py14zy2o5YvPrl/VNf7w9YaKgvTuXFFCRsPRU59vHmiDZvLx/feu5wn7zmfv33yfO67YnRzbGVaZmRfEpfXz9N767lyUSHXLitCrxNsOtw0qveLh101HVQVWsmYgXnieAnVukdqZOo3DZt5P8vpL+6nX4eAD674OpgzofoFqNkGj90GP5wP/7cQuuvgxp+BMbFiuLu2E6vZ0OchHuLSynwuX1hAaVYqG5ZK0Tgeo7jXBsV9du7EjVQrykjBH9Bo73Hj8QU4Flx7k82FLc4N4m6nl/11XVy7rJjlZZm09bhpj5BD3XWmE4tJz9KSDIQQrJ6dg2WUt9uFGSn4AhodYdJAhxts7K/r4rl9DXQ5vNy5bjZZaSbWzslmU5Lz7v6Axt7aLpWSiZGCKBYEod/HmTYcG2LsUBVCbAB+CuiB32ia9r0hz38OuAvwAa3ARzVNq0nwWkfH8U1gzoA5F0HVNTL9svcxsBbLkXnWElj+voTk2Ieyp6aTlbOywnpaPPD+VfR6fKQY9czJTaM6xqHNtcFB0OXZEyfuhRmyprzJ5qK1x43Xr3HTyhKeeaeBky09cQ3R3nm6g4AG6ypy+xwPq5vsXDB/uAnYrhq5f2FIQAVJSBSabS7y0vvfq8Xm4o6Ht9Pj9mHQCRYWWbkguHF91eIivvP8YWrae5k9QlpsLBxtstHj9qnN1BiJFrmHnFGzEuCMOtWI+lcihNADDwDXAIuBO4QQi4ecthdYo2nacuAp4AeJXuioCPjlBKWK9aA3wrU/gCu+ARd9Fu59G256AC7/alKEvdfto7rZHtFqINWk7xOVqiIrx2MsLaztcFBgNZNqSsy+wGgoCjYMNXW7OFQvfWZuXFECwMnW2PYOQmw/1Y7JoGNleRZVRdJ182iYDzq7y0t1ky1hEW1B8ANqaAPMd144gsvr57KqfFbPzuZPHzuvL6d/9WJZlZPM6D2UylszWzUvxULIX6YlkrgHZ6xmps68tEwskfu5wAlN004BCCEeB24CDodO0DRt84DztwN3JnKRo6Z2O/Q0weKb5NcpmXDx58blraub7WgaMTWhVBZaeelgE06PP6po17Q7Rsy3jwdFQWFstrk40dKDxaTn4gX5GPUi7vr3rSfbWTUrixSjHrNBR47FFPYuZm9tFwGNhEW0xcEPqLMDLAg8vgCvHG7mfWvL+fZNS4e9pjwnjYVFVjYdbuauiysSso6h7DrTSUHQoE0RnRyLCZ2IHLmHBtckYkj8VCOW+9tS4OyAr+uCxyLxMeClsSwqZjrPwJ/eA/Ym2YB07GVwdPQ/f+hpMKRA5YZxWc5AQgIVy3SgRcVyPunRpuhui3WdTsonWNxz083odYImm4sD9d0sKs7AZNAxJ9cSl7jXdTo43GhjfZX07hFCUFmYztEw+w+7ajrRCeJK+YxEcWYKhRlmdp7p7Dt2oL4Lp9ffZ20cjisXFbLzTAedvdEnAMWLpmnsrulk7ZycGW3lGw96nSAvPXLPQigtMxMj91jEPdxvWdgSAyHEncAa4IcRnr9bCLFLCLGrtXV0ZXOD2P4gnHwV3vkz7HgQ/nwr/PJ8qN8Dm74hJygtvA7M8ddBj5WjjTYsJj2lMVSsLBswn3QkfP4ATTZXTNdMJnKaUQr7znazv66bc4P+J/Py0znZGl3cQ0O8/3lQVp5sGGChsKQkk6ONtmF1/7trOlhYlJGwNnIhBOsqctl2sr1vPdtPycDgvBH8XC6pzCeg0dfYlEiONNqp73JGbE5ThCc0kSkcNqcXs0FHinHi0pgTRSziXgeUD/i6DGgYepIQ4krgq8CNmqaF/UlrmvaQpmlrNE1bk58/ykHTXhecfgO8Ttj3F3lsx69h41eh4jKZW//jTfDW/XLm6fU/Gd37jJGjTXaqiqwxDQgozkwhx2LiYP3IkXuzXZYflk6CW/b1lQW8eaINX0Dri3Qri6zUtPdGrDkGmTu/+Aeb+dwT7/DkrjoWFlkH1eKfMysLty8w6C7G5w+wt7Yr4ZuM51fk0tbj7tsn2H6qnYVF1hFnzi4tzUAnYN/ZroSuBeCZd+ox6MSMHp03Ggqs5sg5d4d3RqZkIDZx3wksEELMFUKYgNuBZweeIIQ4B/g1UthbEr/MAbzxIynebz8Eri6ouk7m1XPnwW1/hFt+B55emHMx3PAzmWcfZzRN42iTnYUxDpAI+cEcCEbuvW4fD75+cthk99Dcz2TVr8fDhqUy2jbpdX2iu7hY2g2PVLP/8qFm6jqd/H1PPafbevnEpYNz16EN6D3BjcXqJjvffv4wDo8/4eWB6ypkhPzG8VZ63D52nO7gwvmRUzIAaSYDlYVW3hmj+dtQXF4/z+5r4JLK/ISNRZwp5I8g7t1O74xMyUAMG6qapvmEEPcCG5GlkL/TNO2QEOLbwC5N055FpmHSgSeDucJaTdNuTMqKV9wB//ohvPItyKmAm34BrxbI7tKUDCg/Fz65FbLKE9ZpGi+N3S66nV4WFsU+c3VZ6f9v786jq6qvBY5/dwYyQEIGSSAhDDIEARkDRYvgUEFsHVqLYK1Fq9Xnq621S2t9rk76upZdWrV9rfah+Ko+rDjUik8sWIeCVhlkHmSUkEAYQ8KQQEiy3x/nJFzCvTf33twpyf6sdRc3J+cc9j032feX3/n99q87sxfvoKaunnvmrWbRxn28t2kfc2+b0Fy7u2llpVh3y4DTdZGdnkxxz4zmP3mbSiJsqjjCKB/11eev2UNhVhrz7phARkoy3Vu0qgqy0uiZmcrKXVWkp5Rx/+trSUoQBuV1O6u2eVv1zU1neGEmf1m2i5yuXairb2z+0PJnVFEWf9+wF1UNS994Y6Py41dWU1F9gkeuG9Hm83U2hVnpHDh6khOnGs7qfqmqrSMrrXN+WAbUgamqC4AFLbb93OP5V8Icl2+5A6DvRCj9CMbMgvQcuOrJM/fJGxK1cLxp6o/1leC8mTS4B099uJ1LH/sne4+cYOqwfBZu2Mdba/Zw3djeAOxuXjYv8NrlkZKUmMCzs0rOaBX1zk4jIyXJ5wLa1TWn+GjbQb530bn09jNOf0zfLJZsPcD7n+9nfL8c/vumsREZpywizLqgH/e9tpbHFm0mLyOFsQHcsB3RO4uXl5dReqgmLOUd3l5XwYJ1e3lg2hAmDw6xu7IT63eO87NUVlnDoPwzG1TVtfWdduRR+5yheuFdkFkIo26MdSRerSytIjU5IaCRMk0mnJvLA9OGsP/oCR688jyevnEs6V0Sm7tqwBkpk9O1C+lxsor72L45DMw7/cuUkCCc1yuTTRVOcm+5KMmynZU0NCqXFPtPYLdPGkCm+6Hx2PSREZ2ActXIAorzMyirrOWqkQUB3SMZ53ZDfbKj7SWATzU08ttFmxnSMyNiwys7uqahwTsPnV03qrqmzrpl2pXiac4jzjQta/fZrsOMKMwKuhb3HZMH8O0JfZun1J/XK/OMVrCzslLsW+3+DC1wujnuf20tr60sZ8rQfJ6YMYrU5ESW76ykS2ICI1v5i2ZUURbv/XgyNacaIl5fJTU5kXfuvoj9R0+S2y2wD5GBed0ozErjg8/387URvaitayC7axe2HzjGjgPHGds3u3kWb2sWbtjLzkM1zL5pbKdfEzVU/dzZwqWHzp5AV1V7iixL7qYt3tu0j1ufX8HTN45h455qbp0YWivMs1bK0F6Z/G3V7ua+3d1VtQzoEb8LOgPcdlF/3llfwbwVZYzpk8U76/cyddherh1dyNIvKhlZ1D2gYWlJiQlkRmmhioQEaZ51GwgR4ZIhPXhlRTljH/4HdQ2NiDhTLQAuHJDLS9+bENC5Xlq6i97ZaVwWQk1648hKTyYjNam57lKTuvpGauoaOm3LvX12y8ShV1c4tdnvfnk1pxqUSYPafvNvaEEmR0/WU364lhOnGth58Djn9oj+mP1g9M5O5+XbL+Dha4fzyh0XUJiVxt9W7+b4yXo27K5mXL+OMa3+kuI86uobGVaYycPXDOOuSwby2PSR/NvkAfxr+6GAhkruPHicf20/xA3j+1irvQ1EhL656Wd1y3Tm2algLfewqK45xfuf72dAj65sP3Cc7365PxeGYWRH0+iTDXuOkJeZQn2jMrJ34DdpY6X/OV3p795ovHpUAbMX7+ChtzZS36hcOiQvxtGFxyXFefzxW2OYNPicM8rJHj1xirlLS/nzv3byxIxRfs/xjjuJ6+uj/U34NoHom9uVDS0mATYl98xO2nK35B4Gb6+roK6hkSdnjCYxQZoLYLVVcc8MkhOFVWWHm+u5BDMCJx7cMK4P85aXMW9FGVePLKCkg7TcExKEr444e7JRRmoyXzkvn8VbDtDYqH5v0L67cS/nF3aPi3kL7V3fnHQWrt9LfUNjc9XQSrdERGedN2DdMmHwxqpyBuZ1Y3hhJkMLMsP2J3ZqciIje2ex7ItK1pZXk5eRElTfcDzok5vO//1gIt+/ZAC/vHpYrMOJiokDz+HQ8To2+akVtP/oCVaVVTVXmjRt0y+3K/WNyp6q0zVmKqqdocO9wrxIfHthyb2NyiprWL7zMF8fXRiRYk/j++ewrryaZV9UtjrKJF4VZKVx39QhnaYFNdG93+Jv4fNnFu8AYNr5rU+aMq3r4y5eU1p5esRMPM0LiQVL7m30wic7SRC4ZlRBRM4/vn8O9Y3K7qpaLrdWXruQn5nK4PxuLPJR933z3qM89/FOZo7rc8Y8ARO6ppXJSj1uqlZUnSArPTlu5oVEmyX3Nth35AQvfFLKtaML/c64bIuSfjmkJCUwdVg+092Zqib+XV9SxGelh5tHzRw7Wc/qsirq6hv52ZvryUxN4idTi2McZceRn5FKSlLCGWPd91TVUtBJu2TAbqi2yR/e30ZDo/KjywZH7P/olpLEonsm0at7mtX4bkdmjCvid//YyuwlO/jjt8Zw/2treXtdBQkCjQqPfON8v9UnTXASEoQ+OelntNx3V9V22tIDYMk9ZGWVNby8fBfXjytq7u+LlEit12kiJyM1mZsu6MtTH25nfL+dvL2ugm+MLqQwO41zuqVwfUlR6ycxQemb2/WMiUwV1Sea1xrojCy5h+j3721FRPjBpeFff9V0DHdMHsDcpbv4xfwN9OqeysPXDj9jBrIJr7656Xy87SCqSk1dA9W1pzrtSBmw5B6SHQeO8frKcm6+sH+n/uEx/nVPS+bRb45gTXkVN1/Y3xJ7hBXnZ1B7qoEt+47RVLmis46UAUvuQdtx4Bi3v/gZKUmJ3HnxgFiHY+LclGE9mTLMhjtGw0WDnSGo/9yyn55uoyvW6w3HkiX3IKgq98xbzaFjJ5kzq4QeGSmxDskY4+rVPY3i/Aw+3HyAxAShMCutXZTriBQbChmEJVsPsqa8mvumDglL7RhjTHhdXNyDZV9UsmTrQWaMK+rUBdms5R6EOR99QX5mCteNtUJPxsSjW77cn237j7GrsoaZ4zr3iCRL7gE6fLyueYm4lKTYrM1qjPGvZ/dU5tw8LtZhxAXrlgnQuxv30dCofPX8sysBGmNMvLHkHqC31u6hd3YawwsDXxfVGGNixZJ7ALbuO8qSrQeZPrbISgAYY9oFS+4BmPPRF6QkJfDtCX1iHYoxxgTEknsryipreH1lOdNLepPbzca1G2PaB0vurXj83S0kiHDXJYNiHYoxxgTMkrsfzy7ZwRurdnPbRf3b3fJ2xpjOzZK7Dy9+Wsp/vr2JacN78uPLbVEFY0z7YpOYWmhoVF5aWsrP3tzAZUPy+N3M0Z16CrMxpn3qNMldVf0OY6yuOcVjizbzzvq9HDx2kouLe/DUt8fQJcn+uDHGtD+dIrn/76el/P69rRTlpPPqHReQ0KIlvutQDbf8eRm7KmuYMrQnU4blM214L0vsxph2q8Mn9z1VtTz01kZ6dk/ls9LDvLFqN5MG92DxlgPsqaqlsqaO+av3UN+ovHjrl5hwbm6sQzbGmDYLKLmLyBXA74BE4FlVfaTF91OAF4CxwCFghqruDG+ooXnyH1tQlLm3fYnvv7SSe19bQ4IIDY0KQEpSAmP7ZvPwtcMZ0KNbjKM1xpjwaDW5i0gi8EfgcqAcWC4i81V1o8dutwKHVXWgiMwEfgPMiETA5Ydr2HfkJIVZaWzZd5TSyhr65aZTU9fAoWN1pHdJJLtrF+rqG1lXXsUrK8q5Y9K5FOWk88x3Spi3vIwTpxr42ogCBuZ1IzlRrKSAMabDCaTlPh7Ypqo7AETkZeAawDO5XwP80n3+GvAHERFV1TDGCsCbq/fw6MLNAe9/cXEP7pvqDGXMz0zlh5fZZCRjTMcXSHIvBMo8vi4HvuRrH1WtF5FqIBc46LmTiNwO3A7Qp09odVquLyliYF43KqpqGZyfQVFOOmWVNWSkJpPbrQs1dQ0crqkjNSmRrPRkCrPSzrqBaowxHV0gyd1bZmzZIg9kH1R1NjAboKSkJKRWfY+MFKa2WHC4My+Ca4wx3gQy1q8c8Fyvqjewx9c+IpIEdAcqwxGgMcaY4AWS3JcDg0Skv4h0AWYC81vsMx+Y5T7/JvB+JPrbjTHGBKbVbhm3D/0uYCHOUMjnVHWDiDwErFDV+cAc4EUR2YbTYp8ZyaCNMcb4F9A4d1VdACxose3nHs9PANPDG5oxxphQ2fx6Y4zpgCy5G2NMB2TJ3RhjOiBL7sYY0wFJrEYsishRIPA6AmfqA+wKYzjh0h2ojnUQPsRrbBZXcCyu4MVrbKHmsWJVzWhtp1iW/N2sqiWhHCgiB0I9NpJEZLaq3h7rOLyJ19gsruBYXMGL19hCzWMisiKQ/dprt0xVrAPw4a1YB+BHvMZmcQXH4gpevMYW0TwWy26ZFW1ouYd8rDHGxINQ81igx8Wy5T47RscaY0w8CDWPBXRczFruxhhjIqe99rlHhYhcISKbRWSbiPzU3TZHRNaIyFoReU1EvK7NJyIPuMdtFpGp/s4ZprhERH4tIltEZJOI/NDHsbNEZKv7mOWxfayIrHPP+XsJYXkqH3FdKiIrRWS9iDzvVg2NdlzPich+EVnvse1REfncfR/fEJGsQF+Tu72/iCx1453nFtULR1y/FJHdIrLafVwZJ3GNEpFP3ZhWiMh4H8dG8n0sEpEP3J/vDSJyt7t9uvt1o4j47K6I5DWLS6oa8wdwBc6wyG3AT91t/YGlwFZgHtDFx7EPuMdtBqb6O2eQMSUC24FzgS7AGmAokOmxz+Pezu3utwZIcV/Hdvd8Xs8ZprhuwVnHNsHdL8/LsTnADvffbPd5tvu9ZcAFOLX53wGmhSmuMmCwu89DwK3RjMs9xyRgDLDeY9sUIMl9/hvgN4G+Jvd7rwAz3ed/Au4MU1y/BO4N5VpHOK5FTdceuBL4MAbvYy9gjPs8A9ji/oydBxQDHwIlMbpmcZfDYt5yl9NrtE7DeaNuEJGhOL9wT6jqIOAwzjqtLY8dilOBchjOhXhKRBL9nDMYzcsLqmod8DJwjaoecf9vAdLwsigJzrKDL6vqSVX9AufNGe/rnOGIC7gTeEhVGwFUdb+XY6cC76pqpaoeBt4FrhCRXjgfWp+o81P1AnBtGOK6Djipqlvcfd51t0UzLlR1MS3WF1DVRapa7375Kc46BYG8pmvc9/5SnCUlAZ4PV1wBikVcCmS6z7tz9poOEPn3sUJVV7rPjwKbgEJV3aSqrc2Zidg1i9ccFvPkju9kFcgFj2QS9ba8YCGAiPwPsBcYAvyXu+1qccog+zvW5znDENcAYIb7J/M7IjLIjatERJ4NIK7yCMTVE0j2+FP5m5xe1CVacQXiuzitSUSkQESaKqD6iisXqPL4cAh3XHe53UXPiUh2nMT1I+BRESkDHsNpbcbsfRSRfsBonJaxr32idc3iMofFQ3L3ddG9XvAoJlGfSweq6i1AAU7LYYa7bb6eLoPs69iAliMMMa4U4IQ6Q6SeAZ5z41qhqrfFKK5GnFbJEyKyDDgK1Ec5Lr9E5EE3prluXHtUtamfOxZxPY3zQT0KqAB+Gydx3Qnco6pFwD04azjE5H0U5z7X68CPmv6S9iaK1ywuc1g8JHdvFzfRy7amxBqtJOp3eUFVbcDpR/PWzeDr2ECWLAw1rnKcH3iAN4ARQR7b28v2Nsfl/il+kaqOBxbj9D9GMy6f3Bt+XwNudLsLAo3rIJAlp28Ohy0uVd2nqg1u99ozOC24mMeFs9LaX93nrwYZV9jeRxFJxvk5n6uqf21t/wBiC8c1i8scFg/J3dtF30VgFzySSdTr8oIiMhCa+9yvAj73cux8YKaIpIhIf2AQzg2lQJYsDCku4G84fwYCTMa52dTSQmCKiGS7f+5PARaqagVwVEQmuK/rO8Cb4YhLRPIARCQFuB/nhlU04/JKRK5w47laVWuCeU3uB8EHON1M4CS+cMXVy+PLrwPrvewW9bhwfn8mu88vxfuHdETfR/fYOcAmVX08yMMjec3iM4cFewc23A+c+jY7cO4sN93FHobTOvC8g/3vXo4dxpmjUnbgfGJ6PWcIsV2JkyS3Aw/ifBh+DKzD+aWbizt6Brga54Zm07EPusdtxmNkQMtzhnjNzjoHkAW87cb2CTDS3V4CPOtx7Hdx+vW2Abd4bC9xX9N24A+4cyDCENejON1Xm3H+jCYGcf0Fp4vjFM4vza3u/1MGrHYff3L3LQAWtPZ+4Yy6WOae51UgJUxxvei+h2txPrR7xUlcE4HPcH6XlgJjY/A+TsRpva71eN+uxPkQLAdOAvtwPlCids2I0xwWdGKJxMPbRfd1wYliErWHPexhj0Ae8ZjDbIaqMcZ0QPHQ526MMSbMLLkbY0wHZMndGGM6oJgmd2+FfETkLvdrFZFz/Bz7ob8iQcYYEw0+8thcd9t6d6Zxso9jI5bHYpbc/dRO+Bj4ClAaq9iMMSYQfvLYXJzyJOfj1KC6zedJIiSWLXdfhblWqerOYE4kIk+7NVU2iMivPLbvFJFfiVNydp2IDAnvSzDGdHK+8tgCdeEMh/RWmO4M4c5jsUzu4aj/0uRBdWqqjAAmi4jn1PuDqjoGp2bHvSGe3xhjvPGbx9zumJuAvwdwrrDmsVgm93AWE7peRFYCq3BmfHmWxmyqP/EZ0C/E8xtjjDet5bGngMWquiSAc4U1j3ldFSdKgqqdICILgXzAswodbu2We4FxqnpYRP4MpHocetL9t4HYvl5jTMfjM4+JyC+AHsAdTd+MZh6LZbJrLuQD7MYp5PMtXzur6lQf38oEjgPVIpKPc2Pjw/CGaowxXnnNYyJyG87iJZepu4AORDePxSy5q2q9iNyFU0kuEXhOVTeIs/bnT3AWelgrIgs8P+E8JOGs8rNORFYBG3AK7XwcpZdgjOnk/OSxNTgj/j5xilnyV1V9yMspIpbH2mVtGbd87DZguKpWxzoeY4wJVqTzWLuboeoO+F8NPGWJ3RjTHkUjj7XLlrsxxhj/4qLlLiJFIvKBiGxyB/Df7W7PEZF3RWSr+2/TYsFDROQTETkpIve2ONdZU4GNMaaziYuWu7u0WC9VXSkiGThjOa8FbgYqVfURN1Fnq+r97tJtfd19DqvqY+55EnGK21+OM0RpOXCDqm6M+osyxpgYiouWu6pWqOpK9/lRnGXZCoFrgOfd3Z7HSeao6n5VXY6zDJgnr1OBo/ASjDEmrsRFcvckIv2A0TjrNOars7gu7r95rRwezpIGxhjTbsVVcheRbsDrOAspHwnlFF62xb7fyRhjoixukrtbYOd1YK6qNtVR2Of2xzf1y+9v5TRBlTQwxpiOKi6SuzhTuOYAm1T1cY9vzQdmuc9nAW+2cqrmqcAi0gVnKvD8cMdrjDHxLl5Gy0wElgDrgKY6DP+B0+/+CtAH2AVMV9VKEekJrMCpx9AIHAOGquoREbkSeJLTU4F/HdUXY4wxcSAukrsxxpjwiotuGWOMMeFlyd0YYzogS+7GGNMBWXI3xpgOyJK7McZ0QJbcjTGmA7LkbowxHZAld2OM6YD+HxHzQUlr63LOAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -5505,7 +5510,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -5514,7 +5519,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -5719,7 +5724,7 @@ "[5 rows x 243 columns]" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -5730,27 +5735,29 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -5775,7 +5782,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.7rc2" } }, "nbformat": 4, diff --git a/prereise/gather/winddata/te_wind/te_wind.py b/prereise/gather/winddata/te_wind/te_wind.py index 6d3892555..6d07e99a5 100644 --- a/prereise/gather/winddata/te_wind/te_wind.py +++ b/prereise/gather/winddata/te_wind/te_wind.py @@ -12,6 +12,14 @@ def _greatCircleDistance(lat1, lon1, lat2, lon2): + """Calculates distance between two sites + + :param float lat1: latitude of first site (in rad.) + :param float lon1: longitude of first site (in rad.) + :param float lat2: latitude of second site (in rad.) + :param float lon2: longitude of second site (in rad.) + :return: (*float*) -- distance between two sites (in km.). + """ R = 6368 def haversin(x): @@ -24,9 +32,9 @@ def haversin(x): def get_all_NREL_siteID_for_states(states_list): """Retrieve ID's of wind farms in given states. - :param states_list: list object containing state abbreviation. - :return: pandas DataFrame with columns ['site_id', 'lat', 'lon', \ - 'capacity', 'capacity_factor'] + :param list states_list: list object containing state abbreviation. + :return: (*pandas*) -- dataframe with *'site_id'*, *'lat'*, *'lon'*, \ + *'capacity'* and *'capacity_factor'* as columns. """ nrel_sites = None @@ -62,13 +70,13 @@ def get_all_NREL_siteID_for_states(states_list): def find_NREL_siteID_closest_to_windfarm(nrel_sites, wind_farm_bus): """Find NREL site closest to wind farm. - :param nrel_sites: pandas DataFrame that needs the columns \ - ['site_id', 'lat', 'lon','capacity'] as returned by \ - :py:func:`get_all_NREL_siteID_for_states`. - :param wind_farm_bus: pandas DataFrame with the following structure: \ - ['plantID'(index), 'lat', 'lon']. The order of the columns \ - plantID(index), lat and lon is important. - :return: pandas Series with plantID as index, siteID and capacity as value. + :param pandas nrel_sites: data frame with *'site_id'*, *'lat'*, *'lon'* \ + and *'capacity'* as columns. Same strucutre as the one returned by \ + :func:`get_all_NREL_siteID_for_states`. + :param pandas wind_farm_bus: data frame with *'lat'*, *'lon'* as columns \ + and *'plantID'* as indices. The order of the columns is important. + :return: (*pandas*) -- data frame with *'siteID'*, '*capacity*' and \ + *'dist'* as columns and *'plantID'* as indices. """ closest_NREL_siteID = pd.DataFrame(index=wind_farm_bus.index, columns=['siteID', 'capacity', 'dist']) @@ -97,15 +105,16 @@ def find_NREL_siteID_closest_to_windfarm(nrel_sites, wind_farm_bus): def get_data_from_NREL_server(siteIDs, data_range): """Get power and wind speed data from NREL server. - :param siteIDs: pandas DataFrame with siteIDs and capacity as values, \ - as returned by :py:func:`find_NREL_siteID_closest_to_windfarm` - :param data_range: pandas data_range, freq needs to be 5min. - :return: 2D dict containing pandas DataFrame with index date \ - and columns power and wind_speed. \ - The data is normalized using the capacity. \ - The 1. key is the month like '2010-10' \ - The 2. key is the siteID like 121409 \ - So the class site_dict['2012-12'][121409] returns the DataFrame. + :param pandas siteIDs: data frame with *'siteID'* and '*capacity*' as \ + columns. Same structure as the one returned by \ + :func:`find_NREL_siteID_closest_to_windfarm`. + :param pandas data_range: data_range, freq needs to be 5min. + :return: (*dict*) -- dictionary of data frame with *'power'* \ + and *'wind_speed'* as columns and timestamp as indices. The data is \ + normalized using the capacity of the plant. The first key is a \ + month-like timestamp (e.g. *'2010-10'*). The second key is the site \ + id number (e.g. *'121409'*). Then, site_dict['2012-12'][121409] is \ + a data frame. """ wtk_url = "https://h2oq9ul559.execute-api.us-west-2.amazonaws.com/dev" @@ -116,7 +125,7 @@ def get_data_from_NREL_server(siteIDs, data_range): # We use a dict because the output is a tensor # 1: siteIDs 2: date(month) 3: attribute(power, wind_speed) sites_dict = {} - # Use helper DataFrame to specifie download interval + # Use helper DataFrame to specify download interval helper_df = pd.DataFrame(index=data_range) for y in tqdm(helper_df.index.year.unique()): @@ -149,20 +158,19 @@ def get_data_from_NREL_server(siteIDs, data_range): def dict_to_DataFrame(data, data_range, closest_NREL_siteID): - """Converts the dict into two DataFrames. One for power and one for wind - speed. - - :param data: 2D dict containing pandas DataFrame with index date \ - and columns power and wind_speed. The 1. key is the month like \ - '2010-10'. The 2. key is the siteID like 121409. \ - The dict structure is as returend by \ - :py:func:`get_data_from_NREL_server`. - :param data_range: pandas data_range of the data. - :param closest_NREL_siteID: pandas Series with plantID as index, \ - siteID and capacity as value. The structure is as returned by \ - :py:func:`find_NREL_siteID_closest_to_windfarm`. - :return: Two DataFrames, one for power and one for wind speed. Index is \ - data_range and columns are site_IDs + """Converts dictionary into two data frames. One is power, the other is \ + wind speed. + + :param dict data: dictionary of data frame with *'power'* \ + and *'wind_speed'* as columns and timestamp as indices. The first key \ + is a month-like timestamp (e.g. *'2010-10'*). The second key is the \ + site id number (e.g. *'121409'*). It is returend by \ + :func:`get_data_from_NREL_server`. + :param pandas data_range: date range for the data. + :param pandas closest_NREL_siteID: data frame with *'siteID'* as column \ + and *'plantID'* as indices. + :return: (*list*) -- Two data frames, one for power and one for wind \ + speed. Column is *'siteID'* and indices are timestamp. """ NREL_power = pd.DataFrame(index=data_range, columns=closest_NREL_siteID[ @@ -185,16 +193,18 @@ def dict_to_DataFrame(data, data_range, closest_NREL_siteID): def scale_power_to_plant_capacity(NREL_power, wind_farm_bus, closest_NREL_siteID): - """ Scales power to plant capacity - - :param NREL_power: pandas DataFrame index data_range and columns siteID. \ - The structure the same as the one of the first variable returned by \ - :py:func:`dict_to_DataFrame`. - :param wind_farm_bus: pandas DataFrame containing plantID and GenMWMax. \ - :param closest_NREL_siteID: pandas DataFrame containing siteID as \ - as returned by :py:func`find_NREL_siteID_closest_to_windfarm`. \ - :return: pandas DataFrame with data_range as well as plantID containing \ - the power. + """Scales power to plant capacity. + + :param pandas NREL_power: data frame with *'siteID'* as columns and \ + timestamp as indices. Same structure as the one returned by \ + :func:`dict_to_DataFrame`. + :param pandas wind_farm_bus: data frame with *'GenMWMax'* as column and \ + *'plantID'* as indices. + :param pandas closest_NREL_siteID: data frame with *'siteID'*, \ + '*capacity*' and *'dist'* as columns and *'plantID'* as indices. It \ + is returned by :func`find_NREL_siteID_closest_to_windfarm`. + :return: (*pandas*) -- data frame of the power generated with \ + *'plantID'* and timestamp as indices. """ wind_farm_power = pd.DataFrame(index=NREL_power.index, columns=wind_farm_bus.index.values) diff --git a/prereise/gather/winddata/te_wind/test/__init__.py b/prereise/gather/winddata/te_wind/test/__init__.py index c527a74bc..d5e165455 100644 --- a/prereise/gather/winddata/te_wind/test/__init__.py +++ b/prereise/gather/winddata/te_wind/test/__init__.py @@ -1 +1 @@ -__all__ = ["context", "te_wind_test"] +__all__ = ["te_wind_test"] diff --git a/prereise/gather/winddata/te_wind/test/context.py b/prereise/gather/winddata/te_wind/test/context.py deleted file mode 100644 index 35cfc30ab..000000000 --- a/prereise/gather/winddata/te_wind/test/context.py +++ /dev/null @@ -1,6 +0,0 @@ -import os -import sys - -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - -import te_wind diff --git a/prereise/gather/winddata/te_wind/test/te_wind_test.py b/prereise/gather/winddata/te_wind/test/te_wind_test.py index c2be2f4eb..f23730bf7 100644 --- a/prereise/gather/winddata/te_wind/test/te_wind_test.py +++ b/prereise/gather/winddata/te_wind/test/te_wind_test.py @@ -1,16 +1,12 @@ -# Import western interconnect data -from westernintnet.westernintnet import WesternIntNet - -from .context import * - -win = WesternIntNet() +from westernintnet.westernintnet import win_data +from prereise.gather.winddata.te_wind import te_wind def test(): all_siteID_NREL = te_wind.get_all_NREL_siteID_for_states(['WA']) - wind_farm_bus = win.genbus.groupby('type').get_group('wind') + wind_farm_bus = win_data.genbus.groupby('type').get_group('wind') closest_NREL_siteID = te_wind.find_NREL_siteID_closest_to_windfarm( all_siteID_NREL, wind_farm_bus[['lat', 'lon']] diff --git a/requirements.txt b/requirements.txt index 31421eca8..7f78b5900 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,24 +1,14 @@ -###### Requirements without Version Specifiers ###### -collections -datetime -dateutil -math -os -time -sys -bokeh -h5py -h5pyd +bokeh==0.13.0 +h5py==2.8.0 +h5pyd==0.3.2 matlab -matplotlib -multiprocessing -netCDF4 -numpy -pandas >= 0.23.0 -pyproj -pywtk -requests -timeit -tqdm -westernintnet -xlrd +matplotlib==3.0.0 +netCDF4==1.4.1 +numpy==1.15.2 +pandas==0.23.0 +-e git://github.com/sjschneider/py3samsdk.git#egg=py3samsdk +pyproj==1.9.5.1 +-e git://github.com/NREL/pywtk.git#egg=pywtk +requests==2.19.1 +tqdm==4.27.0 +xlrd=1.2.0