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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import xarray as xr\n",
"import numpy as np\n",
"from pvlib.tools import cosd\n",
"\n",
"%matplotlib notebook\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import os\n",
"import time"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 1: Combine tilted radiation with partly shading (*compute_all_PV.py*)\n",
"Merge tilted radiation with partly shading vectors according to the following formula:\n",
"\n",
"$G_t = S_{partly} (G_b R_b) + SVF (G_D R_d) + (G_h R_r)$\n",
"\n",
"where $(G_b R_b), (G_D R_d), (G_h R_r)$ are the three components given in *tilted_irradiance.nc* (poa_direct, poa_sky_diffuse, poa_ground_diffuse, respectively)\n",
"\n",
"**NOTE**: Best thing is to run the full code (*compute_all_PV.py*) on a large memory node (approx. 5min) - *compute_loop_PV.py* runs on small memory, but takes much longer.\n",
"\n",
"**FULL OUTPUT FILE**: */work/hyenergy/output/solar_potential/technical_potential/pv_potential.nc*\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Open datasets"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"fp = '/work/hyenergy/output/solar_potential/geographic_potential/available_area/shading_stats/CH_partly_shading_replaced.nc'\n",
"partly_shading = xr.open_mfdataset( fp, chunks={'DF_UID': 10000} )\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"fp = '/scratch/walch/workspace_tilted_irrad/tilted_irradiance_noALB.nc' # /work/hyenergy/output/solar_potential/geographic_potential/tilted_irradiance/tilted_irradiance_ELM50_1000_dhm25_200.nc'\n",
"tilted_irrad = xr.open_dataset( fp )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Define sky view factor and view variables"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"svf = 0.9\n",
"efficiency = 0.17\n",
"performance_factor = 0.8"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 9639231, timestamp: 146)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) float64 1.0 2.0 3.0 ... 9.89e+06 9.89e+06 9.89e+06\n",
"Data variables:\n",
" partly_shading (timestamp, DF_UID) float64 dask.array<shape=(146, 9639231), chunksize=(146, 10000)>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"partly_shading"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 9639231, timestamp: 149)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T07:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) int64 1 2 3 4 ... 9890021 9890022 9890023\n",
"Data variables:\n",
" poa_direct (DF_UID, timestamp) float64 ...\n",
" poa_sky_diffuse (DF_UID, timestamp) float64 ...\n",
" poa_ground_diffuse (DF_UID, timestamp) float64 ..."
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tilted_irrad"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Define subset"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"subset_shade = partly_shading.isel( DF_UID = range(100000) ).transpose()\n",
"subset_irrad = tilted_irrad.isel( DF_UID = range(100000) )"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# merge together and fill NaNs with 0 --> timestamps that are not in shading dataset mean that here the sun was not up\n",
"# --> beam component in that case is 0\n",
"tilted_irrad = xr.merge( [ subset_shade, subset_irrad ] ).fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"tilted_irrad['svf'] = svf"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 100000, timestamp: 149)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T07:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) float64 1.0 2.0 3.0 ... 1.013e+05 1.013e+05\n",
"Data variables:\n",
" partly_shading (DF_UID, timestamp) float64 0.0 0.0 0.0 ... 0.8 0.3 0.0\n",
" poa_direct (DF_UID, timestamp) float64 0.0 0.0 0.0 ... 18.81 1.049\n",
" poa_sky_diffuse (DF_UID, timestamp) float64 0.0 37.28 53.49 ... 37.4 5.2\n",
" poa_ground_diffuse (DF_UID, timestamp) float64 0.0 2.594 ... 0.0001567\n",
" svf float64 0.9"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tilted_irrad"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"tilted_irrad['tilted_irradiance'] = tilted_irrad.partly_shading * tilted_irrad.poa_direct + tilted_irrad.svf * tilted_irrad.poa_sky_diffuse + tilted_irrad.poa_ground_diffuse\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 100000, timestamp: 149)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T07:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) float64 1.0 2.0 3.0 ... 1.013e+05 1.013e+05\n",
"Data variables:\n",
" partly_shading (DF_UID, timestamp) float64 0.0 0.0 0.0 ... 0.8 0.3 0.0\n",
" poa_direct (DF_UID, timestamp) float64 0.0 0.0 0.0 ... 18.81 1.049\n",
" poa_sky_diffuse (DF_UID, timestamp) float64 0.0 37.28 53.49 ... 37.4 5.2\n",
" poa_ground_diffuse (DF_UID, timestamp) float64 0.0 2.594 ... 0.0001567\n",
" svf float64 0.9\n",
" tilted_irradiance (DF_UID, timestamp) float64 0.0 36.15 ... 39.3 4.68"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tilted_irrad"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Add days of each month"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"days_of_each_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n",
"month = range(1,13)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"days_of_month = pd.DataFrame( data = [ month, days_of_each_month ], index = ['month', 'n_days'] ).transpose()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"timestamps = tilted_irrad.timestamp.to_dataframe()\n",
"timestamps['month'] = timestamps.timestamp.dt.month"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"days_of_month = timestamps.merge( days_of_month, on = 'month', how = 'left' ).drop( columns = 'month' ).set_index('timestamp').to_xarray()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"pv_output = xr.merge( [tilted_irrad.tilted_irradiance, days_of_month ])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 100000, timestamp: 149)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T07:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) float64 1.0 2.0 3.0 ... 1.013e+05 1.013e+05\n",
"Data variables:\n",
" tilted_irradiance (DF_UID, timestamp) float64 0.0 36.15 52.25 ... 39.3 4.68\n",
" n_days (timestamp) int64 31 31 31 31 31 31 ... 31 31 31 31 31 31"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pv_output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Add available area"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"av_area_fp = '/work/hyenergy/output/solar_potential/geographic_potential/available_area/PRED_available_area_unc_filled.csv'\n",
"av_area = pd.read_csv( av_area_fp, usecols = ['DF_UID', 'available_area', 'tilted_area'] )\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"pv_output['available_area'] = av_area.set_index('DF_UID').to_xarray().available_area\n",
"pv_output['tilted_area'] = av_area.set_index('DF_UID').to_xarray().tilted_area"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Add computational factors"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"pv_output['efficiency'] = efficiency\n",
"pv_output['performance_factor'] = performance_factor"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"pv_output['pv_potential'] = pv_output.tilted_irradiance * pv_output.available_area * pv_output.efficiency * pv_output.performance_factor"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# pv_output['pv_all_area'] = pv_output.tilted_irradiance * pv_output.tilted_area * pv_output.efficiency * pv_output.performance_factor"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 100000, timestamp: 149)\n",
"Coordinates:\n",
" * DF_UID (DF_UID) float64 1.0 2.0 3.0 ... 1.013e+05 1.013e+05\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T07:00:00 ... 2001-12-15T15:00:00\n",
"Data variables:\n",
" tilted_irradiance (DF_UID, timestamp) float64 0.0 36.15 ... 39.3 4.68\n",
" n_days (timestamp) int64 31 31 31 31 31 31 ... 31 31 31 31 31\n",
" available_area (DF_UID) float64 1.437 0.0 29.41 ... 0.0 23.08 10.87\n",
" tilted_area (DF_UID) float64 15.95 13.73 63.44 ... 11.62 55.58 41.57\n",
" efficiency float64 0.17\n",
" performance_factor float64 0.8\n",
" pv_potential (DF_UID, timestamp) float64 0.0 7.066 ... 58.11 6.92"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pv_output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compute yearly sum"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"pv_output['yearly_kWh_m2_a'] = (pv_output.tilted_irradiance * pv_output.n_days).sum( dim = 'timestamp' ) / 1000"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"pv_output['yearly_PV_all_area'] = pv_output.yearly_kWh_m2_a * pv_output.tilted_area * pv_output.efficiency * pv_output.performance_factor"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 100000, timestamp: 149)\n",
"Coordinates:\n",
" * DF_UID (DF_UID) float64 1.0 2.0 3.0 ... 1.013e+05 1.013e+05\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T07:00:00 ... 2001-12-15T15:00:00\n",
"Data variables:\n",
" tilted_irradiance (DF_UID, timestamp) float64 0.0 36.15 ... 39.3 4.68\n",
" n_days (timestamp) int64 31 31 31 31 31 31 ... 31 31 31 31 31\n",
" available_area (DF_UID) float64 1.437 0.0 29.41 ... 0.0 23.08 10.87\n",
" efficiency float64 0.17\n",
" performance_factor float64 0.8\n",
" yearly_kWh_m2_a (DF_UID) float64 660.1 1.275e+03 ... 1.019e+03 1.088e+03\n",
" pv_potential (DF_UID, timestamp) float64 0.0 7.066 ... 58.11 6.92\n",
" tilted_area (DF_UID) float64 15.95 13.73 63.44 ... 11.62 55.58 41.57\n",
" pv_all_area (DF_UID, timestamp) float64 0.0 78.4 ... 222.2 26.46\n",
" yearly_PV_all_area (DF_UID) float64 1.432e+03 2.38e+03 ... 6.151e+03"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pv_output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Compare yearly sum with Sonnendach"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"SON_fp = '/work/hyenergy/raw/Sonnendach/Sonnendach_all_roofs/SOLKAT_CH_DACH_LV03.txt'\n",
"SON_cols = pd.read_csv( SON_fp, nrows = 0, index_col = 0 ).columns"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['OBJECTID', 'DF_UID', 'DF_NUMMER', 'DATUM_ERSTELLUNG',\n",
" 'DATUM_AENDERUNG', 'SB_UUID', 'SB_OBJEKTART', 'SB_DATUM_ERSTELLUNG',\n",
" 'SB_DATUM_AENDERUNG', 'KLASSE', 'FLAECHE', 'AUSRICHTUNG', 'NEIGUNG',\n",
" 'MSTRAHLUNG', 'GSTRAHLUNG', 'STROMERTRAG', 'WAERMEERTRAG',\n",
" 'DUSCHGAENGE', 'DG_HEIZUNG', 'DG_WAERMEBEDARF', 'BEDARF_WARMWASSER',\n",
" 'BEDARF_HEIZUNG', 'FLAECHE_KOLLEKTOREN', 'VOLUMEN_SPEICHER', 'GWR_EGID',\n",
" 'Shape_Length', 'Shape_Area', 'XCOORD', 'YCOORD', 'Estim_build_area'],\n",
" dtype='object')"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SON_cols"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"SON_radiation = pd.read_csv( SON_fp, nrows = 100000, usecols = [ 'DF_UID', 'MSTRAHLUNG', 'STROMERTRAG'] )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 2: Verify PV potential"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"ROOFS='/work/hyenergy/output/solar_potential/geographic_potential/CH_ROOFS_all_replaced.csv'"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"roofs = pd.read_csv(ROOFS, usecols = ['DF_UID', 'NEIGUNG', 'panel_direction', 'panel_tilt', 'FLAECHE'])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"DF_UID_flat = roofs[roofs.NEIGUNG == 0].DF_UID.values\n",
"DF_UID_tilt = roofs[roofs.NEIGUNG > 0].DF_UID.values"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"pv_potential_flat = xr.open_mfdataset('/work/hyenergy/output/solar_potential/technical_potential/pv_output_flat.nc',\n",
" chunks={'DF_UID': 10000})\n",
"pv_potential_tilt = xr.open_mfdataset('/work/hyenergy/output/solar_potential/technical_potential/pv_output_slanted.nc',\n",
" chunks={'DF_UID': 10000})"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 8484564, timestamp: 146)\n",
"Coordinates:\n",
" * DF_UID (DF_UID) int64 1 2 3 4 ... 9890021 9890022 9890023\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 2001-12-15T15:00:00\n",
"Data variables:\n",
" tilted_irradiance (DF_UID, timestamp) float64 dask.array<shape=(8484564, 146), chunksize=(10000, 146)>\n",
" available_area (DF_UID) float64 dask.array<shape=(8484564,), chunksize=(10000,)>\n",
" efficiency float64 ...\n",
" performance_factor float64 ...\n",
" pv_potential (DF_UID, timestamp) float64 dask.array<shape=(8484564, 146), chunksize=(10000, 146)>\n",
" yearly_kWh_m2 (DF_UID) float64 dask.array<shape=(8484564,), chunksize=(10000,)>\n",
" yearly_PV_kWh (DF_UID) float64 dask.array<shape=(8484564,), chunksize=(10000,)>\n",
" tilted_area (DF_UID) float64 dask.array<shape=(8484564,), chunksize=(10000,)>\n",
" yearly_PV_all_area (DF_UID) float64 dask.array<shape=(8484564,), chunksize=(10000,)>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pv_potential_tilt"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 1154667, timestamp: 146)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) int64 33 50 51 52 ... 9889995 9889996 9890002\n",
"Data variables:\n",
" tilted_irradiance (timestamp, DF_UID) float64 dask.array<shape=(146, 1154667), chunksize=(146, 10000)>\n",
" available_area (DF_UID) float64 dask.array<shape=(1154667,), chunksize=(10000,)>\n",
" efficiency float64 ...\n",
" performance_factor float64 ...\n",
" pv_potential (timestamp, DF_UID) float64 dask.array<shape=(146, 1154667), chunksize=(146, 10000)>\n",
" yearly_kWh_m2 (DF_UID) float64 dask.array<shape=(1154667,), chunksize=(10000,)>\n",
" yearly_PV_kWh (DF_UID) float64 dask.array<shape=(1154667,), chunksize=(10000,)>\n",
" tilted_area (DF_UID) float64 dask.array<shape=(1154667,), chunksize=(10000,)>\n",
" yearly_PV_all_area (DF_UID) float64 dask.array<shape=(1154667,), chunksize=(10000,)>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pv_potential_flat = pv_potential_flat.sel( timestamp = pv_potential_tilt.timestamp.values )\n",
"pv_potential_flat"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"pv_potential_all = xr.open_mfdataset('/scratch/walch/workspace_tilted_irrad/PV_all_interp.nc', chunks = {'DF_UID' : 10000})"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 9639231, timestamp: 146)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) int64 1 2 3 4 ... 9890021 9890022 9890023\n",
"Data variables:\n",
" tilted_irradiance (DF_UID, timestamp) float64 dask.array<shape=(9639231, 146), chunksize=(10000, 146)>\n",
" panelled_area (DF_UID) float64 dask.array<shape=(9639231,), chunksize=(10000,)>\n",
" tilted_area (DF_UID) float64 dask.array<shape=(9639231,), chunksize=(10000,)>\n",
" available_area (DF_UID) float64 dask.array<shape=(9639231,), chunksize=(10000,)>\n",
" efficiency float64 ...\n",
" performance_factor float64 ...\n",
" pv_potential (DF_UID, timestamp) float64 dask.array<shape=(9639231, 146), chunksize=(10000, 146)>\n",
" yearly_kWh_m2 (DF_UID) float64 dask.array<shape=(9639231,), chunksize=(10000,)>\n",
" yearly_PV_kWh (DF_UID) float64 dask.array<shape=(9639231,), chunksize=(10000,)>\n",
" yearly_PV_all_area (DF_UID) float64 dask.array<shape=(9639231,), chunksize=(10000,)>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pv_potential_all"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"pv_potential_all_flat = pv_potential_all.where(pv_potential_all.DF_UID.isin(DF_UID_flat), drop = True)\n",
"pv_potential_all_tilt = pv_potential_all.where(pv_potential_all.DF_UID.isin(DF_UID_tilt), drop = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get yearly dataframe"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"yearly_df = pv_potential_all.drop(['tilted_irradiance', 'pv_potential', 'efficiency', 'performance_factor']).drop('timestamp').to_dataframe()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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],
"text/plain": [
" panelled_area tilted_area available_area yearly_kWh_m2 \\\n",
"DF_UID \n",
"1 1.437423 15.948816 1.437423 1347.372656 \n",
"2 0.000000 13.730555 0.000000 564.570217 \n",
"3 29.414390 63.437476 29.414390 1140.713599 \n",
"4 14.224941 38.368910 14.224941 1140.718178 \n",
"5 10.472945 36.403273 10.472945 881.565997 \n",
"\n",
" yearly_PV_kWh yearly_PV_all_area \n",
"DF_UID \n",
"1 263.397304 2922.503751 \n",
"2 0.000000 1054.253283 \n",
"3 4563.261613 9841.502922 \n",
"4 2206.824277 5952.463436 \n",
"5 1255.632561 4364.496713 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yearly_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"yearly_df.to_csv('/scratch/walch/workspace_tilted_irrad/PV_all_interp_yearly.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analyse yearly dataframe"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"pv_yearly_all = pd.read_csv('/work/hyenergy/output/solar_potential/technical_potential/pv_potential_annual_shared_v2.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9639231\n"
]
},
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"data": {
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" <th></th>\n",
" <th>DF_UID</th>\n",
" <th>pv_potential_kWh_a</th>\n",
" <th>pv_potential_kWh_a_std</th>\n",
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],
"text/plain": [
" DF_UID pv_potential_kWh_a pv_potential_kWh_a_std radiation_kWh_m2_a \\\n",
"0 1 239.8001 324.268393 1210.184846 \n",
"1 2 0.0000 0.000000 474.558362 \n",
"2 3 3891.6055 2071.079104 951.934814 \n",
"3 4 2022.1940 1044.604313 1029.797436 \n",
"4 5 1030.4568 702.281552 711.799309 \n",
"\n",
" radiation_kWh_m2_a_std available_area \n",
"0 309.039122 1.437423 \n",
"1 85.539804 0.000000 \n",
"2 239.814424 29.414390 \n",
"3 259.121816 14.224941 \n",
"4 188.438262 10.472945 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(len(pv_yearly_all))\n",
"pv_yearly_all.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"pv_yearly_flat = pd.read_csv('/work/hyenergy/output/solar_potential/technical_potential/flat_roofs/pv_output_mutual_tiltedPanels_yearly.csv')\n",
"pv_yearly_tilt = pd.read_csv('/work/hyenergy/output/solar_potential/technical_potential/slanted_roofs/pv_output_slanted_yearly.csv')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# for consistency with pv_yearly_all:\n",
"# pv_yearly_flat.rename(columns = {'available_area' : 'panelled_area'}, inplace = True)\n",
"pv_yearly_tilt.rename(columns = {'available_area' : 'panelled_area'}, inplace = True)\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"pv_yearly_all_flat = pv_yearly_all[ pv_yearly_all.DF_UID.isin( DF_UID_flat ) ]\n",
"pv_yearly_all_tilt = pv_yearly_all[ pv_yearly_all.DF_UID.isin( DF_UID_tilt ) ]"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
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],
"text/plain": [
" DF_UID panelled_area yearly_kWh_m2 yearly_PV_kWh tilted_area \\\n",
"0 33 6.984947 1123.602111 1067.369008 27.568645 \n",
"1 50 225.933379 1293.889179 39757.254536 681.609660 \n",
"2 51 193.567950 1264.346952 33284.238437 609.169499 \n",
"3 52 588.427224 1288.958288 103150.308061 1523.273993 \n",
"4 53 499.533235 1290.708202 87686.223539 1332.294940 \n",
"\n",
" yearly_PV_all_area \n",
"0 4212.761480 \n",
"1 119942.121482 \n",
"2 104747.417511 \n",
"3 267027.382779 \n",
"4 233866.144921 "
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pv_yearly_flat.head()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
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],
"text/plain": [
" DF_UID panelled_area tilted_area available_area yearly_kWh_m2 \\\n",
"31 33 6.984947 27.568645 6.049142 1123.602111 \n",
"48 50 225.933379 681.609660 195.664046 1293.889179 \n",
"49 51 193.567950 609.169499 167.634762 1264.346952 \n",
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"51 53 499.533235 1332.294940 432.608472 1290.708202 \n",
"\n",
" yearly_PV_kWh yearly_PV_all_area \n",
"31 1067.369008 4212.761480 \n",
"48 39757.254536 119942.121482 \n",
"49 33284.238437 104747.417511 \n",
"50 103150.308061 267027.382779 \n",
"51 87686.223539 233866.144921 "
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pv_yearly_all_flat.head()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"scrolled": false
},
"outputs": [
{
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" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"pv_yearly_flat.yearly_kWh_m2.plot.hist(bins = 50, label = 'Flat only', range = (500,2000), density = True)\n",
"pv_yearly_all_flat.yearly_kWh_m2.plot.hist(bins = 50, facecolor = 'None', edgecolor = 'C1', label = 'Flat of all', range = (500,2000), density = True)\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"pv_yearly_tilt.yearly_kWh_m2.plot.hist(bins = 50, label = 'Tilted only', range = (500,2000)) # \n",
"pv_yearly_all_tilt.yearly_kWh_m2.plot.hist(bins = 50, \n",
" facecolor = 'None', edgecolor = 'C1', label = 'Tilted of all', \n",
" range = (500,2000))\n",
"plt.legend()\n",
"plt.xlabel('Rooftop PV potential (kWh)')\n",
"plt.tight_layout()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Compare with Sonnendach"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"SON_fp = '/work/hyenergy/raw/Sonnendach/Sonnendach_all_roofs/SOLKAT_CH_DACH_LV03.txt'\n",
"SON_radiation = pd.read_csv( SON_fp, usecols = [ 'DF_UID', 'MSTRAHLUNG', 'GSTRAHLUNG', 'STROMERTRAG'] )"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"ROOF_fp = '/work/hyenergy/output/solar_potential/geographic_potential/CH_ROOFS_all_replaced.csv'\n",
"ROOFS = pd.read_csv( ROOF_fp, usecols = ['DF_UID', 'AUSRICHTUNG', 'NEIGUNG', 'panel_tilt', 'panel_direction'])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"yearly_df_merged = pv_yearly_all.merge( SON_radiation, on = 'DF_UID' )\n",
"yearly_df_merged = yearly_df_merged.merge( ROOFS, on = 'DF_UID' )"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DF_UID</th>\n",
" <th>pv_potential_kWh_a</th>\n",
" <th>pv_potential_kWh_a_std</th>\n",
" <th>radiation_kWh_m2_a</th>\n",
" <th>radiation_kWh_m2_a_std</th>\n",
" <th>available_area</th>\n",
" <th>MSTRAHLUNG</th>\n",
" <th>GSTRAHLUNG</th>\n",
" <th>STROMERTRAG</th>\n",
" <th>NEIGUNG</th>\n",
" <th>AUSRICHTUNG</th>\n",
" <th>panel_direction</th>\n",
" <th>panel_tilt</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>239.8001</td>\n",
" <td>324.268393</td>\n",
" <td>1210.184846</td>\n",
" <td>309.039122</td>\n",
" <td>1.437423</td>\n",
" <td>1320</td>\n",
" <td>21052</td>\n",
" <td>2863</td>\n",
" <td>37</td>\n",
" <td>10.0</td>\n",
" <td>190.0</td>\n",
" <td>37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>474.558362</td>\n",
" <td>85.539804</td>\n",
" <td>0.000000</td>\n",
" <td>593</td>\n",
" <td>8142</td>\n",
" <td>1107</td>\n",
" <td>44</td>\n",
" <td>-170.0</td>\n",
" <td>10.0</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>3891.6055</td>\n",
" <td>2071.079104</td>\n",
" <td>951.934814</td>\n",
" <td>239.814424</td>\n",
" <td>29.414390</td>\n",
" <td>1146</td>\n",
" <td>72699</td>\n",
" <td>9887</td>\n",
" <td>12</td>\n",
" <td>-80.0</td>\n",
" <td>100.0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>2022.1940</td>\n",
" <td>1044.604313</td>\n",
" <td>1029.797436</td>\n",
" <td>259.121816</td>\n",
" <td>14.224941</td>\n",
" <td>1128</td>\n",
" <td>43280</td>\n",
" <td>5886</td>\n",
" <td>33</td>\n",
" <td>-80.0</td>\n",
" <td>100.0</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>1030.4568</td>\n",
" <td>702.281552</td>\n",
" <td>711.799309</td>\n",
" <td>188.438262</td>\n",
" <td>10.472945</td>\n",
" <td>721</td>\n",
" <td>26247</td>\n",
" <td>3570</td>\n",
" <td>35</td>\n",
" <td>100.0</td>\n",
" <td>280.0</td>\n",
" <td>35</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DF_UID pv_potential_kWh_a pv_potential_kWh_a_std radiation_kWh_m2_a \\\n",
"0 1 239.8001 324.268393 1210.184846 \n",
"1 2 0.0000 0.000000 474.558362 \n",
"2 3 3891.6055 2071.079104 951.934814 \n",
"3 4 2022.1940 1044.604313 1029.797436 \n",
"4 5 1030.4568 702.281552 711.799309 \n",
"\n",
" radiation_kWh_m2_a_std available_area MSTRAHLUNG GSTRAHLUNG \\\n",
"0 309.039122 1.437423 1320 21052 \n",
"1 85.539804 0.000000 593 8142 \n",
"2 239.814424 29.414390 1146 72699 \n",
"3 259.121816 14.224941 1128 43280 \n",
"4 188.438262 10.472945 721 26247 \n",
"\n",
" STROMERTRAG NEIGUNG AUSRICHTUNG panel_direction panel_tilt \n",
"0 2863 37 10.0 190.0 37 \n",
"1 1107 44 -170.0 10.0 44 \n",
"2 9887 12 -80.0 100.0 12 \n",
"3 5886 33 -80.0 100.0 33 \n",
"4 3570 35 100.0 280.0 35 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yearly_df_merged.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"yearly_df_merged['yearly_radiation'] = yearly_df_merged.radiation_kWh_m2_a * yearly_df_merged.available_area\n",
"# yearly_df_merged['yearly_radiation_allArea'] = yearly_df_merged.radiation_kWh_m2_a * yearly_df_merged.tilted_area"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"flat_roofs = yearly_df_merged.NEIGUNG == 0\n",
"flat_roof_UIDs = yearly_df_merged.DF_UID[flat_roofs].values"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"suitable_surfaces = yearly_df_merged[ (yearly_df_merged.panelled_area > 8) & (abs(yearly_df_merged.AUSRICHTUNG) < 90) ]\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DF_UID 47981.471946\n",
"panelled_area 0.272633\n",
"tilted_area 0.644587\n",
"available_area 0.267230\n",
"yearly_kWh_m2 10.677593\n",
"yearly_PV_kWh 42.663887\n",
"yearly_PV_all_area 100.216033\n",
"MSTRAHLUNG 10.437668\n",
"GSTRAHLUNG 722.380284\n",
"STROMERTRAG 98.243718\n",
"NEIGUNG 0.240198\n",
"AUSRICHTUNG 0.017595\n",
"panel_direction 1.752657\n",
"panel_tilt 0.274838\n",
"yearly_radiation 313.705054\n",
"yearly_radiation_allArea 736.882594\n",
"dtype: float64"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pv_TWh = yearly_df_merged.sum()/1e9\n",
"pv_TWh"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DF_UID 12875.993021\n",
"panelled_area 0.154640\n",
"tilted_area 0.344324\n",
"available_area 0.149404\n",
"yearly_kWh_m2 3.483231\n",
"yearly_PV_kWh 27.227543\n",
"yearly_PV_all_area 60.563253\n",
"MSTRAHLUNG 3.415678\n",
"GSTRAHLUNG 429.683996\n",
"STROMERTRAG 58.437024\n",
"NEIGUNG 0.061288\n",
"AUSRICHTUNG 0.005646\n",
"panel_direction 0.490881\n",
"panel_tilt 0.074360\n",
"yearly_radiation 200.202521\n",
"yearly_radiation_allArea 445.318039\n",
"dtype: float64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"suitable_surfaces.sum()/(1e9)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"floor_area = suitable_surfaces.tilted_area * cosd(suitable_surfaces.NEIGUNG)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"317.55869855185148"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"floor_area.sum()/1e6"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [],
"source": [
"# FURIES AREA:\n",
"furies_area = 48536 / 100 # hectars to km2\n",
"furies_PV = 53 # TWh"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.10919729685182132"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"furies_PV / furies_area "
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [],
"source": [
"our_area = pv_TWh.panelled_area * 1000\n",
"our_PV = pv_TWh.yearly_PV_kWh"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.15648821640048841"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"our_PV / our_area"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualise"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/numpy/lib/arraysetops.py:569: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" mask |= (ar1 == a)\n"
]
}
],
"source": [
"# COMPARE WITH NEW DATA WHERE ONLY THE ISOTROPIC COMPONENT IS MULTIPLIED WITH THE SVF\n",
"new_data = pd.read_csv( '/scratch/walch/workspace_tilted_irrad/irrad_diffuse_split_compare.csv', index_col = 0)\n",
"yearly_df_merged = yearly_df_merged.merge( new_data, on = 'DF_UID' )"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"yearly_df_tilt = yearly_df_merged[ yearly_df_merged.NEIGUNG > 0 ] \n",
"yearly_df_flat = yearly_df_merged[ yearly_df_merged.NEIGUNG == 0 ] "
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
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" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
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" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
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"\n",
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"\n",
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" }\n",
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"\n",
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"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
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"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
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" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
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" canvas_div.focus();\n",
" }\n",
"\n",
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"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"\n",
"N_BINS = 30\n",
"yearly_df_flat['yearly_kWh_m2'].plot.hist( bins = N_BINS, label = 'BIG_DATA', range = [0, 2000], density = False, alpha = 0.8, edgecolor = 'C0' )\n",
"yearly_df_flat.MSTRAHLUNG.hist( bins = N_BINS, label = 'SONNENDACH', facecolor = 'None', edgecolor = 'C1', range = [0, 2000], density = False )\n",
"plt.title('Yearly mean irradiance (flat roofs)')\n",
"plt.xlabel('$kWh/m^2/a$')\n",
"plt.ylabel('Density')\n",
"plt.legend()\n",
"plt.show()\n",
"plt.savefig('yearly_mean_irrad_flat.png', dpi = 300)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"\n",
"N_BINS = 30\n",
"yearly_df_tilt['yearly_kWh_m2'].plot.hist( bins = N_BINS, label = 'BIG_DATA', range = [0, 2000], density = False, alpha = 0.8, edgecolor = 'C0' )\n",
"yearly_df_tilt.MSTRAHLUNG.hist( bins = N_BINS, label = 'SONNENDACH', facecolor = 'None', edgecolor = 'C1', range = [0, 2000], density = False )\n",
"plt.title('Yearly mean irradiance (tilted roofs)')\n",
"plt.xlabel('$kWh/m^2/a$')\n",
"plt.ylabel('Density')\n",
"plt.legend()\n",
"plt.show()\n",
"plt.savefig('yearly_mean_irrad_tilt.png', dpi = 300)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DF_UID</th>\n",
" <th>pv_potential_kWh_a</th>\n",
" <th>pv_potential_kWh_a_std</th>\n",
" <th>radiation_kWh_m2_a</th>\n",
" <th>radiation_kWh_m2_a_std</th>\n",
" <th>available_area</th>\n",
" <th>MSTRAHLUNG</th>\n",
" <th>GSTRAHLUNG</th>\n",
" <th>STROMERTRAG</th>\n",
" <th>NEIGUNG</th>\n",
" <th>AUSRICHTUNG</th>\n",
" <th>panel_direction</th>\n",
" <th>panel_tilt</th>\n",
" <th>kWh_m2_old</th>\n",
" <th>kWh_m2_new</th>\n",
" <th>kWh_m2_new_CshReduced</th>\n",
" </tr>\n",
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" <td>85.539804</td>\n",
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" <td>8142</td>\n",
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" <td>2071.079104</td>\n",
" <td>951.934814</td>\n",
" <td>239.814424</td>\n",
" <td>29.414390</td>\n",
" <td>1146</td>\n",
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" <td>9887</td>\n",
" <td>12</td>\n",
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" <td>2022.1940</td>\n",
" <td>1044.604313</td>\n",
" <td>1029.797436</td>\n",
" <td>259.121816</td>\n",
" <td>14.224941</td>\n",
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" <td>5886</td>\n",
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" <td>1029.797436</td>\n",
" <td>1090.741274</td>\n",
" <td>981.406106</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>1030.4568</td>\n",
" <td>702.281552</td>\n",
" <td>711.799309</td>\n",
" <td>188.438262</td>\n",
" <td>10.472945</td>\n",
" <td>721</td>\n",
" <td>26247</td>\n",
" <td>3570</td>\n",
" <td>35</td>\n",
" <td>100.0</td>\n",
" <td>280.0</td>\n",
" <td>35</td>\n",
" <td>711.799309</td>\n",
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" <td>532.651360</td>\n",
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],
"text/plain": [
" DF_UID pv_potential_kWh_a pv_potential_kWh_a_std radiation_kWh_m2_a \\\n",
"0 1 239.8001 324.268393 1210.184846 \n",
"1 2 0.0000 0.000000 474.558362 \n",
"2 3 3891.6055 2071.079104 951.934814 \n",
"3 4 2022.1940 1044.604313 1029.797436 \n",
"4 5 1030.4568 702.281552 711.799309 \n",
"\n",
" radiation_kWh_m2_a_std available_area MSTRAHLUNG GSTRAHLUNG \\\n",
"0 309.039122 1.437423 1320 21052 \n",
"1 85.539804 0.000000 593 8142 \n",
"2 239.814424 29.414390 1146 72699 \n",
"3 259.121816 14.224941 1128 43280 \n",
"4 188.438262 10.472945 721 26247 \n",
"\n",
" STROMERTRAG NEIGUNG AUSRICHTUNG panel_direction panel_tilt \\\n",
"0 2863 37 10.0 190.0 37 \n",
"1 1107 44 -170.0 10.0 44 \n",
"2 9887 12 -80.0 100.0 12 \n",
"3 5886 33 -80.0 100.0 33 \n",
"4 3570 35 100.0 280.0 35 \n",
"\n",
" kWh_m2_old kWh_m2_new kWh_m2_new_CshReduced \n",
"0 1210.184846 1281.830912 1101.451997 \n",
"1 474.558362 498.795119 498.795119 \n",
"2 951.934814 1043.480604 967.400815 \n",
"3 1029.797436 1090.741274 981.406106 \n",
"4 711.799309 751.227645 532.651360 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yearly_df_merged.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"\n",
"df = yearly_df_tilt\n",
"\n",
"min_val = max(df.kWh_m2_new.min(), df.MSTRAHLUNG.min())\n",
"max_val = min(df.kWh_m2_new.max(), df.MSTRAHLUNG.max())\n",
"\n",
"X = df.kWh_m2_new.values.reshape(-1, 1)\n",
"Y = df.MSTRAHLUNG.values.reshape(-1, 1)\n",
"\n",
"LIN = LinearRegression( n_jobs = -1 )\n",
"LIN.fit( X, Y )\n",
"\n",
"x = np.asarray([min_val, max_val]).reshape(-1, 1)\n",
"y = LIN.predict( x )\n",
"\n",
"R2 = LIN.score(X, Y)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"scrolled": false
},
"outputs": [
{
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" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"\n",
"plt.hist2d( df.MSTRAHLUNG, df.kWh_m2_new, bins=100, cmap = 'Oranges')\n",
"plt.plot([min_val,max_val],[min_val,max_val], c = 'grey', alpha= 0.7, lw = 0.7)\n",
"plt.plot(y, x, c = 'k', lw = 0.7, label = 'Regression line ($R^2 = %.2f$)' %R2)\n",
"# plt.plot([], [], c = 'grew', ls = '--', lw = 0.7, alpha = 0.7)\n",
"\n",
"plt.ylabel('Our study ($kWh/m^2$)')\n",
"plt.xlabel('Toitsolaire ($kWh/m^2$)')\n",
"plt.title('Annual tilted radiation')\n",
"\n",
"plt.axis('equal')\n",
"plt.xlim((min_val, max_val))\n",
"plt.ylim((min_val, max_val))\n",
"\n",
"cbar = plt.colorbar()\n",
"cbar.set_label('# of values in bin')\n",
"\n",
"plt.legend()\n",
"plt.show()\n",
"plt.tight_layout()\n",
"plt.savefig('Regression_tilt_splitDiffuse.pdf', dpi = 300)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WITHOUT REDUCING BY Csh:\n",
"Our estimate lies on average 0.86% below Sonnendach\n",
"The mean abolute deviation of our data is 12.29% wrt Sonnendach\n"
]
}
],
"source": [
"print('WITHOUT REDUCING BY Csh:')\n",
"# Average difference:\n",
"# MBE:\n",
"mbe = (yearly_df_merged.MSTRAHLUNG - yearly_df_merged.kWh_m2_new) / yearly_df_merged.MSTRAHLUNG\n",
"print('Our estimate lies on average %.2f%% below Sonnendach' %(mbe.mean()*100))\n",
"\n",
"# MAE:\n",
"mae = np.abs(yearly_df_merged.MSTRAHLUNG - yearly_df_merged.kWh_m2_new) / yearly_df_merged.MSTRAHLUNG\n",
"print('The mean abolute deviation of our data is %.2f%% wrt Sonnendach' %(mae.mean()*100))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WITH REDUCING BY Csh:\n",
"Our estimate lies on average 12.76% below Sonnendach\n",
"The mean abolute deviation of our data is 18.85% wrt Sonnendach\n"
]
}
],
"source": [
"print('WITH REDUCING BY Csh:')\n",
"# Average difference:\n",
"# MBE:\n",
"mbe = (yearly_df_merged.MSTRAHLUNG - yearly_df_merged.kWh_m2_new_CshReduced) / yearly_df_merged.MSTRAHLUNG\n",
"print('Our estimate lies on average %.2f%% below Sonnendach' %(mbe.mean()*100))\n",
"\n",
"# MAE:\n",
"mae = np.abs(yearly_df_merged.MSTRAHLUNG - yearly_df_merged.kWh_m2_new_CshReduced) / yearly_df_merged.MSTRAHLUNG\n",
"print('The mean abolute deviation of our data is %.2f%% wrt Sonnendach' %(mae.mean()*100))"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
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"output_type": "display_data"
},
{
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plotted_subset = pv_output.assign_coords( timestamp = range(149) ).isel( DF_UID = range(72000, 72100) ).transpose()\n",
"plotted_subset.pv_potential.plot(vmax = 3000)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Verify tilted irradiance"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"tilted_irrad = xr.open_mfdataset('/scratch/walch/workspace_tilted_irrad/tilted_irradiance.nc', chunks={'DF_UID': 10000} )"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 9639231, timestamp: 149)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T07:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) int64 1 2 3 4 ... 9890021 9890022 9890023\n",
"Data variables:\n",
" poa_direct (DF_UID, timestamp) float64 dask.array<shape=(9639231, 149), chunksize=(10000, 149)>\n",
" poa_sky_diffuse (DF_UID, timestamp) float64 dask.array<shape=(9639231, 149), chunksize=(10000, 149)>\n",
" poa_ground_diffuse (DF_UID, timestamp) float64 dask.array<shape=(9639231, 149), chunksize=(10000, 149)>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tilted_irrad"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"tilted_irrad_old = xr.open_mfdataset('/work/hyenergy/output/solar_potential/geographic_potential/tilted_irradiance/tilted_irradiance_ELM50_1000_dhm25_200.nc', chunks={'DF_UID': 10000} )"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 9639231, timestamp: 149)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T07:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) int64 1 2 3 4 ... 9890021 9890022 9890023\n",
"Data variables:\n",
" poa_direct (DF_UID, timestamp) float64 dask.array<shape=(9639231, 149), chunksize=(10000, 149)>\n",
" poa_sky_diffuse (DF_UID, timestamp) float64 dask.array<shape=(9639231, 149), chunksize=(10000, 149)>\n",
" poa_ground_diffuse (DF_UID, timestamp) float64 dask.array<shape=(9639231, 149), chunksize=(10000, 149)>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tilted_irrad_old"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# WE DON'T EXPECT THESE TO MATCH EXACTLY, AS IN OLD ALL PANELS WERE ORIENTED OPPOSITE (+ 180°)\n",
"plt.figure()\n",
"\n",
"tilted_irrad.poa_direct.sel( DF_UID = DF_UID_tilt, timestamp = '2001-07-15 12:00' ).plot.hist(bins = 60, range = (0,600), label = 'new irradiance')\n",
"tilted_irrad_old.poa_direct.sel( DF_UID = DF_UID_tilt, timestamp = '2001-07-15 12:00' ).plot.hist(bins = 60, range = (0,600), fc = 'None',\n",
" edgecolor = 'C1', label = 'old irradiance')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# NEW IRRADIANCE OF FLAT ROOFS SHOULD BE LARGER, AS PANELS ARE ORIENTED SOUTH AT 30°\n",
"plt.figure()\n",
"\n",
"tilted_irrad.poa_direct.sel( DF_UID = DF_UID_flat, timestamp = '2001-07-15 12:00' ).plot.hist(bins = 60, range = (0,600), label = 'new irradiance')\n",
"tilted_irrad_old.poa_direct.sel( DF_UID = DF_UID_flat, timestamp = '2001-07-15 12:00' ).plot.hist(bins = 60, range = (0,600), fc = 'None',\n",
" edgecolor = 'C1', label = 'old irradiance')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f9c15f324a8>]], dtype=object)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"roofs[roofs.NEIGUNG > 0].hist('panel_direction', bins = 72)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prepare available area"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load predicted available area data and Sonnendach reference data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"SON_area = pd.read_csv( SON_fp, usecols = ['DF_UID', 'FLAECHE'] )"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/walch/miniconda3/envs/py3/lib/python3.6/site-packages/numpy/lib/arraysetops.py:463: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" mask |= (ar1 == a)\n"
]
}
],
"source": [
"fp_roofs = '/work/hyenergy/output/solar_potential/geographic_potential/available_area/PRED_available_area_unc.csv'\n",
"av_area = pd.read_csv(fp_roofs, index_col = 0)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DF_UID</th>\n",
" <th>prediction_panels</th>\n",
" <th>model_std_panels</th>\n",
" <th>data_std_panels</th>\n",
" <th>total_std_panels</th>\n",
" <th>prediction_shade</th>\n",
" <th>model_std_shade</th>\n",
" <th>data_std_shade</th>\n",
" <th>total_std_shade</th>\n",
" <th>predicted_area_ratio</th>\n",
" <th>total_uncertainty_all</th>\n",
" <th>model_uncertainty_all</th>\n",
" <th>data_uncertainty_all</th>\n",
" <th>tilted_area</th>\n",
" <th>available_area</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>0.104887</td>\n",
" <td>0.103332</td>\n",
" <td>0.074716</td>\n",
" <td>0.127515</td>\n",
" <td>0.140720</td>\n",
" <td>0.224326</td>\n",
" <td>0.056937</td>\n",
" <td>0.231439</td>\n",
" <td>0.090127</td>\n",
" <td>0.264242</td>\n",
" <td>0.246981</td>\n",
" <td>0.093937</td>\n",
" <td>15.948816</td>\n",
" <td>1.437423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3.0</td>\n",
" <td>0.500140</td>\n",
" <td>0.189558</td>\n",
" <td>0.110180</td>\n",
" <td>0.219253</td>\n",
" <td>0.072910</td>\n",
" <td>0.093728</td>\n",
" <td>0.033942</td>\n",
" <td>0.099684</td>\n",
" <td>0.463675</td>\n",
" <td>0.240851</td>\n",
" <td>0.211464</td>\n",
" <td>0.115290</td>\n",
" <td>63.437476</td>\n",
" <td>29.414390</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.0</td>\n",
" <td>0.412044</td>\n",
" <td>0.139281</td>\n",
" <td>0.081699</td>\n",
" <td>0.161474</td>\n",
" <td>0.100239</td>\n",
" <td>0.134644</td>\n",
" <td>0.048521</td>\n",
" <td>0.143120</td>\n",
" <td>0.370741</td>\n",
" <td>0.215771</td>\n",
" <td>0.193722</td>\n",
" <td>0.095021</td>\n",
" <td>38.368910</td>\n",
" <td>14.224941</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5.0</td>\n",
" <td>0.405748</td>\n",
" <td>0.156295</td>\n",
" <td>0.090131</td>\n",
" <td>0.180421</td>\n",
" <td>0.290959</td>\n",
" <td>0.200610</td>\n",
" <td>0.144141</td>\n",
" <td>0.247024</td>\n",
" <td>0.287692</td>\n",
" <td>0.305897</td>\n",
" <td>0.254308</td>\n",
" <td>0.170001</td>\n",
" <td>36.403273</td>\n",
" <td>10.472945</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>6.0</td>\n",
" <td>0.481832</td>\n",
" <td>0.188802</td>\n",
" <td>0.104113</td>\n",
" <td>0.215606</td>\n",
" <td>0.247234</td>\n",
" <td>0.202474</td>\n",
" <td>0.141214</td>\n",
" <td>0.246854</td>\n",
" <td>0.362707</td>\n",
" <td>0.327754</td>\n",
" <td>0.276843</td>\n",
" <td>0.175445</td>\n",
" <td>46.031679</td>\n",
" <td>16.696005</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DF_UID prediction_panels model_std_panels data_std_panels \\\n",
"0 1.0 0.104887 0.103332 0.074716 \n",
"1 3.0 0.500140 0.189558 0.110180 \n",
"2 4.0 0.412044 0.139281 0.081699 \n",
"3 5.0 0.405748 0.156295 0.090131 \n",
"4 6.0 0.481832 0.188802 0.104113 \n",
"\n",
" total_std_panels prediction_shade model_std_shade data_std_shade \\\n",
"0 0.127515 0.140720 0.224326 0.056937 \n",
"1 0.219253 0.072910 0.093728 0.033942 \n",
"2 0.161474 0.100239 0.134644 0.048521 \n",
"3 0.180421 0.290959 0.200610 0.144141 \n",
"4 0.215606 0.247234 0.202474 0.141214 \n",
"\n",
" total_std_shade predicted_area_ratio total_uncertainty_all \\\n",
"0 0.231439 0.090127 0.264242 \n",
"1 0.099684 0.463675 0.240851 \n",
"2 0.143120 0.370741 0.215771 \n",
"3 0.247024 0.287692 0.305897 \n",
"4 0.246854 0.362707 0.327754 \n",
"\n",
" model_uncertainty_all data_uncertainty_all tilted_area available_area \n",
"0 0.246981 0.093937 15.948816 1.437423 \n",
"1 0.211464 0.115290 63.437476 29.414390 \n",
"2 0.193722 0.095021 38.368910 14.224941 \n",
"3 0.254308 0.170001 36.403273 10.472945 \n",
"4 0.276843 0.175445 46.031679 16.696005 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"av_area.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Expand predicted available area to all DF_UIDs"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"av_area_filled = SON_area[['DF_UID']].merge( av_area, on = 'DF_UID', how = 'left' )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Find DF_UIDs with missing prediction and replace tilted_area with Sonnendach FLAECHE"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"null_areas = av_area_filled.available_area.isnull()\n",
"missing_DF_UIDs = av_area_filled.loc[ null_areas ].DF_UID.values"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2098699"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(missing_DF_UIDs)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"tilted_areas_to_fill = SON_area[ SON_area.DF_UID.isin( missing_DF_UIDs ) ].FLAECHE.values"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"av_area_filled.loc[ null_areas, 'tilted_area' ] = tilted_areas_to_fill"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fill all remaining NaNs with 0"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"av_area_filled = av_area_filled.fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DF_UID</th>\n",
" <th>prediction_panels</th>\n",
" <th>model_std_panels</th>\n",
" <th>data_std_panels</th>\n",
" <th>total_std_panels</th>\n",
" <th>prediction_shade</th>\n",
" <th>model_std_shade</th>\n",
" <th>data_std_shade</th>\n",
" <th>total_std_shade</th>\n",
" <th>predicted_area_ratio</th>\n",
" <th>total_uncertainty_all</th>\n",
" <th>model_uncertainty_all</th>\n",
" <th>data_uncertainty_all</th>\n",
" <th>tilted_area</th>\n",
" <th>available_area</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0.104887</td>\n",
" <td>0.103332</td>\n",
" <td>0.074716</td>\n",
" <td>0.127515</td>\n",
" <td>0.140720</td>\n",
" <td>0.224326</td>\n",
" <td>0.056937</td>\n",
" <td>0.231439</td>\n",
" <td>0.090127</td>\n",
" <td>0.264242</td>\n",
" <td>0.246981</td>\n",
" <td>0.093937</td>\n",
" <td>15.948816</td>\n",
" <td>1.437423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>13.730555</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>0.500140</td>\n",
" <td>0.189558</td>\n",
" <td>0.110180</td>\n",
" <td>0.219253</td>\n",
" <td>0.072910</td>\n",
" <td>0.093728</td>\n",
" <td>0.033942</td>\n",
" <td>0.099684</td>\n",
" <td>0.463675</td>\n",
" <td>0.240851</td>\n",
" <td>0.211464</td>\n",
" <td>0.115290</td>\n",
" <td>63.437476</td>\n",
" <td>29.414390</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>0.412044</td>\n",
" <td>0.139281</td>\n",
" <td>0.081699</td>\n",
" <td>0.161474</td>\n",
" <td>0.100239</td>\n",
" <td>0.134644</td>\n",
" <td>0.048521</td>\n",
" <td>0.143120</td>\n",
" <td>0.370741</td>\n",
" <td>0.215771</td>\n",
" <td>0.193722</td>\n",
" <td>0.095021</td>\n",
" <td>38.368910</td>\n",
" <td>14.224941</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0.405748</td>\n",
" <td>0.156295</td>\n",
" <td>0.090131</td>\n",
" <td>0.180421</td>\n",
" <td>0.290959</td>\n",
" <td>0.200610</td>\n",
" <td>0.144141</td>\n",
" <td>0.247024</td>\n",
" <td>0.287692</td>\n",
" <td>0.305897</td>\n",
" <td>0.254308</td>\n",
" <td>0.170001</td>\n",
" <td>36.403273</td>\n",
" <td>10.472945</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DF_UID prediction_panels model_std_panels data_std_panels \\\n",
"0 1 0.104887 0.103332 0.074716 \n",
"1 2 0.000000 0.000000 0.000000 \n",
"2 3 0.500140 0.189558 0.110180 \n",
"3 4 0.412044 0.139281 0.081699 \n",
"4 5 0.405748 0.156295 0.090131 \n",
"\n",
" total_std_panels prediction_shade model_std_shade data_std_shade \\\n",
"0 0.127515 0.140720 0.224326 0.056937 \n",
"1 0.000000 0.000000 0.000000 0.000000 \n",
"2 0.219253 0.072910 0.093728 0.033942 \n",
"3 0.161474 0.100239 0.134644 0.048521 \n",
"4 0.180421 0.290959 0.200610 0.144141 \n",
"\n",
" total_std_shade predicted_area_ratio total_uncertainty_all \\\n",
"0 0.231439 0.090127 0.264242 \n",
"1 0.000000 0.000000 0.000000 \n",
"2 0.099684 0.463675 0.240851 \n",
"3 0.143120 0.370741 0.215771 \n",
"4 0.247024 0.287692 0.305897 \n",
"\n",
" model_uncertainty_all data_uncertainty_all tilted_area available_area \n",
"0 0.246981 0.093937 15.948816 1.437423 \n",
"1 0.000000 0.000000 13.730555 0.000000 \n",
"2 0.211464 0.115290 63.437476 29.414390 \n",
"3 0.193722 0.095021 38.368910 14.224941 \n",
"4 0.254308 0.170001 36.403273 10.472945 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"av_area_filled.head()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"av_area_filled.to_csv('/work/hyenergy/output/solar_potential/geographic_potential/available_area/PRED_available_area_unc_filled.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SPIELWIESE"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"roof_subset = pd.read_csv('/scratch/walch/workspace_tilted_irrad_change/files/roof_subset_1M.csv', index_col = 0)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DF_UID</th>\n",
" <th>FLAECHE</th>\n",
" <th>AUSRICHTUNG</th>\n",
" <th>NEIGUNG</th>\n",
" <th>XCOORD</th>\n",
" <th>YCOORD</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>16.457870</td>\n",
" <td>-81</td>\n",
" <td>9</td>\n",
" <td>621706.188280</td>\n",
" <td>254171.306654</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>13</td>\n",
" <td>19.496845</td>\n",
" <td>-170</td>\n",
" <td>42</td>\n",
" <td>621684.301885</td>\n",
" <td>254242.707828</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>31</td>\n",
" <td>16.954926</td>\n",
" <td>-180</td>\n",
" <td>32</td>\n",
" <td>622443.599020</td>\n",
" <td>254084.364048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>37</td>\n",
" <td>14.423623</td>\n",
" <td>-180</td>\n",
" <td>44</td>\n",
" <td>622433.685770</td>\n",
" <td>254090.141980</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>47</td>\n",
" <td>0.422576</td>\n",
" <td>41</td>\n",
" <td>39</td>\n",
" <td>623002.078837</td>\n",
" <td>254135.067752</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DF_UID FLAECHE AUSRICHTUNG NEIGUNG XCOORD YCOORD\n",
"6 7 16.457870 -81 9 621706.188280 254171.306654\n",
"11 13 19.496845 -170 42 621684.301885 254242.707828\n",
"29 31 16.954926 -180 32 622443.599020 254084.364048\n",
"35 37 14.423623 -180 44 622433.685770 254090.141980\n",
"45 47 0.422576 41 39 623002.078837 254135.067752"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"roof_subset.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"partly_shading_sel = partly_shading.sel( DF_UID = roof_subset.DF_UID.values )"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 1000000, timestamp: 146)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) float64 7.0 13.0 31.0 ... 9.89e+06 9.89e+06\n",
"Data variables:\n",
" partly_shading (timestamp, DF_UID) float64 dask.array<shape=(146, 1000000), chunksize=(146, 1034)>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"partly_shading_sel"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"partly_shading_sel.to_netcdf('/scratch/walch/workspace_tilted_irrad_change/files/partly_shading_subset_1M.nc')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"partly_shading_sel = xr.open_mfdataset('/scratch/walch/workspace_tilted_irrad_change/files/partly_shading_subset_1M.nc')\n",
"tilted_irrad = xr.open_mfdataset('/scratch/walch/workspace_tilted_irrad_change/files/tilted_irrad_change_0.nc')\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 1000000, timestamp: 146)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T08:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) float64 7.0 13.0 31.0 ... 9.89e+06 9.89e+06\n",
"Data variables:\n",
" partly_shading (timestamp, DF_UID) float64 dask.array<shape=(146, 1000000), chunksize=(146, 1000000)>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"partly_shading_sel"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (DF_UID: 1000000, timestamp: 149)\n",
"Coordinates:\n",
" * timestamp (timestamp) datetime64[ns] 2001-01-15T07:00:00 ... 2001-12-15T15:00:00\n",
" * DF_UID (DF_UID) int64 7 13 31 37 ... 9890009 9890010 9890022\n",
"Data variables:\n",
" poa_direct (DF_UID, timestamp) float64 dask.array<shape=(1000000, 149), chunksize=(1000000, 149)>\n",
" poa_sky_diffuse (DF_UID, timestamp) float64 dask.array<shape=(1000000, 149), chunksize=(1000000, 149)>\n",
" poa_ground_diffuse (DF_UID, timestamp) float64 dask.array<shape=(1000000, 149), chunksize=(1000000, 149)>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tilted_irrad"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.all( partly_shading_sel.DF_UID.values == tilted_irrad.DF_UID.values )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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