From be1462d7bb840c13bc6c40a9d39dfd045bd7eff2 Mon Sep 17 00:00:00 2001 From: Silvana Ayala Date: Wed, 20 May 2020 13:54:14 -0600 Subject: [PATCH 1/3] Tutorials for single module and multiple row with irradiance distributions Tutorials for single module and multiple row with irradiance distributions --- ...1 - Beginner - Single Module Example.ipynb | 518 ++++++++++++++++++ .../1 - Beginner - Single Module Example.py | 211 +++++++ ...culation under different irradiances.ipynb | 276 ++++++++++ ...Calculation under different irradiances.py | 146 +++++ docs/tutorials/irr_1axis_11_06_13.pkl | Bin 0 -> 12101 bytes 5 files changed, 1151 insertions(+) create mode 100644 docs/tutorials/1 - Beginner - Single Module Example.ipynb create mode 100644 docs/tutorials/1 - Beginner - Single Module Example.py create mode 100644 docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.ipynb create mode 100644 docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.py create mode 100644 docs/tutorials/irr_1axis_11_06_13.pkl diff --git a/docs/tutorials/1 - Beginner - Single Module Example.ipynb b/docs/tutorials/1 - Beginner - Single Module Example.ipynb new file mode 100644 index 0000000..2cfed25 --- /dev/null +++ b/docs/tutorials/1 - Beginner - Single Module Example.ipynb @@ -0,0 +1,518 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1 - Beginner - Single Module Example\n", + "\n", + "This tutorial shows how to assign an array of irradiances as inputs to a module. It is assigning 12 values of irradiances Gpoat, 1 value to each row of six cells in a 12 x 6 module (72 cell module). " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pvmismatch # this imports everything we need\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Inputs" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "## Inputs:\n", + "numcells = 72\n", + "Gpoat = [0.9, 0.9, 0.8, 0.7, 0.7, 0.8, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9] # kW/m2 units\n", + "portraitorlandscape = 'portrait'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Select module type\n", + "\n", + "The stdpl matrix shows the placement of the cells in the module. The modules we are using are the standard PVMismatch modules, look at the references for the bypass diode groups, but because of this it does matter if the module is in ladscape or portrait." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# cell placement for 'portrait'.\n", + "if numcells == 72:\n", + " stdpl=np.array([[0,\t23,\t24,\t47,\t48,\t71],\n", + " [1,\t22,\t25,\t46,\t49,\t70],\n", + " [2,\t21,\t26,\t45,\t50,\t69],\n", + " [3,\t20,\t27,\t44,\t51,\t68],\n", + " [4,\t19,\t28,\t43,\t52,\t67],\n", + " [5,\t18,\t29,\t42,\t53,\t66],\n", + " [6,\t17,\t30,\t41,\t54,\t65],\n", + " [7,\t16,\t31,\t40,\t55,\t64],\n", + " [8,\t15,\t32,\t39,\t56,\t63],\n", + " [9,\t14,\t33,\t38,\t57,\t62],\n", + " [10,\t13,\t34,\t37,\t58,\t61],\n", + " [11,\t12,\t35,\t36,\t59,\t60]])\n", + "\n", + "elif numcells == 96:\n", + " stdpl=np.array([[0,\t23,\t24,\t47,\t48,\t71,\t72,\t95],\n", + " [1,\t22,\t25,\t46,\t49,\t70,\t73,\t94],\n", + " [2,\t21,\t26,\t45,\t50,\t69,\t74,\t93],\n", + " [3,\t20,\t27,\t44,\t51,\t68,\t75,\t92],\n", + " [4,\t19,\t28,\t43,\t52,\t67,\t76,\t91],\n", + " [5,\t18,\t29,\t42,\t53,\t66,\t77,\t90],\n", + " [6,\t17,\t30,\t41,\t54,\t65,\t78,\t89],\n", + " [7,\t16,\t31,\t40,\t55,\t64,\t79,\t88],\n", + " [8,\t15,\t32,\t39,\t56,\t63,\t80,\t87],\n", + " [9,\t14,\t33,\t38,\t57,\t62,\t81,\t86],\n", + " [10,\t13,\t34,\t37,\t58,\t61,\t82,\t85],\n", + " [11,\t12,\t35,\t36,\t59,\t60,\t83,\t84]])\n", + "\n", + "if portraitorlandscape == 'landscape':\n", + " stdpl = stdpl.transpose()\n", + "\n", + "cellsx = len(stdpl[1]); cellsy = len(stdpl)\n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Let's create the type of module we want" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "if cellsx*cellsy == 72:\n", + " cell_pos = pvmismatch.pvmismatch_lib.pvmodule.STD72\n", + "elif cellsx*cellsy == 96:\n", + " cell_pos = pvmismatch.pvmismatch_lib.pvmodule.STD96\n", + "\n", + "pvmod=pvmismatch.pvmismatch_lib.pvmodule.PVmodule(cell_pos=cell_pos)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Let's make the system be just 1 module" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "pvsys = pvmismatch.pvsystem.PVsystem(numberStrs=1, numberMods=1, pvmods=pvmod) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create the pattern of irradiance based on the Gpoat input. \n", + "\n", + "We are assigning the gradient across the module for this case. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how our irradiance gradient looks accross the module\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "G=np.array([Gpoat]).transpose()\n", + "H = np.ones([1,cellsx]) \n", + "array_det = np.dot(G,H) \n", + "sns.heatmap(array_det, square = True)\n", + "print(\"This is how our irradiance gradient looks accross the module\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Values under STC:\n", + "\n", + "This is under the default irradiance of 1000 W/m2" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pmp: 240.948027 [W], Eff: 21.825477 [%], FF: 78.726054 [%]\n", + "Imp: 5.915269 [A], Vmp: 40.733234 [V], Isc: 6.305600 [A], Voc: 48.537622 [V]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pvsys.plotSys() \n", + "print (\"Pmp: %f [W], Eff: %f [%%], FF: %f [%%]\" % (pvsys.Pmp, pvsys.eff * 100., pvsys.FF * 100.))\n", + "print (\"Imp: %f [A], Vmp: %f [V], Isc: %f [A], Voc: %f [V]\" % (pvsys.Imp, pvsys.Vmp, pvsys.Isc, pvsys.Voc))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Values with our irradiance profile:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pmp: 183.658038 [W], Eff: 19.571828 [%], FF: 80.869192 [%]\n", + "Imp: 4.310297 [A], Vmp: 42.609142 [V], Isc: 4.710711 [A], Voc: 48.210363 [V]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pvsys.setSuns({0: {0: [array_det, stdpl]}})\n", + "print (\"Pmp: %f [W], Eff: %f [%%], FF: %f [%%]\" % (pvsys.Pmp, pvsys.eff * 100., pvsys.FF * 100.))\n", + "print (\"Imp: %f [A], Vmp: %f [V], Isc: %f [A], Voc: %f [V]\" % (pvsys.Imp, pvsys.Vmp, pvsys.Isc, pvsys.Voc))\n", + "PowerDetailed=pvsys.Pmp \n", + "pvsys.plotSys() \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculating Mismatch\n", + "\n", + "The power derate, or Mismatch resulting from the module having a distribution of irradiances compared to just one single average irradiance value can be calculated by repeating the power calculation, now with the average irradiance assigned to the whole module, and then calculating the Mismatch: " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### First let's calculate the average irradiance value" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " The module's average irradiance is : %f [kW/m2] 0.8499999999999999\n", + " And each cell will see this value of irradiance: \n", + "[[0.85 0.85 0.85 0.85 0.85 0.85]\n", + " [0.85 0.85 0.85 0.85 0.85 0.85]\n", + " [0.85 0.85 0.85 0.85 0.85 0.85]\n", + " [0.85 0.85 0.85 0.85 0.85 0.85]\n", + " [0.85 0.85 0.85 0.85 0.85 0.85]\n", + " [0.85 0.85 0.85 0.85 0.85 0.85]\n", + " [0.85 0.85 0.85 0.85 0.85 0.85]\n", + " [0.85 0.85 0.85 0.85 0.85 0.85]\n", + " [0.85 0.85 0.85 0.85 0.85 0.85]\n", + " [0.85 0.85 0.85 0.85 0.85 0.85]\n", + " [0.85 0.85 0.85 0.85 0.85 0.85]\n", + " [0.85 0.85 0.85 0.85 0.85 0.85]]\n" + ] + } + ], + "source": [ + "array_avg = np.ones([cellsy,cellsx])*np.mean(Gpoat) \n", + "averageIrradiance = array_avg.mean()\n", + "print(\" The module's average irradiance is : %f [kW/m2]\", averageIrradiance)\n", + "print(\" And each cell will see this value of irradiance: \")\n", + "print (array_avg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Let's assign the averaged irradiance array to the cells and calculate power.\n", + "\n", + "There's various ways, but they all do the same.\n", + "\n", + "Setting each cell:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pmp: 204.012466 [W], Eff: 21.740932 [%], FF: 78.938784 [%]\n", + "Imp: 5.019300 [A], Vmp: 40.645602 [V], Isc: 5.359760 [A], Voc: 48.219306 [V]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pvsys.setSuns({0: {0: [array_avg, stdpl]}}) # Sets each cell\n", + "pvsys.plotSys() \n", + "print (\"Pmp: %f [W], Eff: %f [%%], FF: %f [%%]\" % (pvsys.Pmp, pvsys.eff * 100., pvsys.FF * 100.))\n", + "print (\"Imp: %f [A], Vmp: %f [V], Isc: %f [A], Voc: %f [V]\" % (pvsys.Imp, pvsys.Vmp, pvsys.Isc, pvsys.Voc))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Setting the Module: " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pmp: 204.012466 [W], Eff: 21.740932 [%], FF: 78.938784 [%]\n", + "Imp: 5.019300 [A], Vmp: 40.645602 [V], Isc: 5.359760 [A], Voc: 48.219306 [V]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pvsys.setSuns({0: {0: averageIrradiance}}) # Sets the module\n", + "pvsys.plotSys() \n", + "print (\"Pmp: %f [W], Eff: %f [%%], FF: %f [%%]\" % (pvsys.Pmp, pvsys.eff * 100., pvsys.FF * 100.))\n", + "print (\"Imp: %f [A], Vmp: %f [V], Isc: %f [A], Voc: %f [V]\" % (pvsys.Imp, pvsys.Vmp, pvsys.Isc, pvsys.Voc))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Setting the whole system (in this case is just 1 module):" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pmp: 204.012466 [W], Eff: 21.740932 [%], FF: 78.938784 [%]\n", + "Imp: 5.019300 [A], Vmp: 40.645602 [V], Isc: 5.359760 [A], Voc: 48.219306 [V]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pvsys.setSuns(averageIrradiance) # Sets the whole system\n", + "pvsys.plotSys() \n", + "print (\"Pmp: %f [W], Eff: %f [%%], FF: %f [%%]\" % (pvsys.Pmp, pvsys.eff * 100., pvsys.FF * 100.))\n", + "print (\"Imp: %f [A], Vmp: %f [V], Isc: %f [A], Voc: %f [V]\" % (pvsys.Imp, pvsys.Vmp, pvsys.Isc, pvsys.Voc))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, there are various ways to \"setSuns\". Some of them are described in the setSuns function:\n", + "(https://github.com/SunPower/PVMismatch/blob/master/pvmismatch/pvmismatch_lib/pvsystem.py)\n", + "\n", + "\n", + "This are the examples/options:\n", + "
    \n", + "
  • Ee={0: {0: {'cells': (0, 1, 2), 'Ee': (0.9, 0.3, 0.5)}}}
    • \n", + "
  • Ee=0.91 # set all modules in all strings to 0.91 suns
    • \n", + "
  • Ee={12: 0.77} # set all modules in string with index 12 to 0.77 suns
    • \n", + "
  • Ee={3: {8: 0.23, 7: 0.45}} # set module with index 8 to 0.23 suns and module with index 7 to 0.45 suns in string with index 3
    • \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Calculate the mismatch\n", + "\n", + "Now we have the power under average irradiance conditions, and we can calculate mismatch" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "PowerAveraged=pvsys.Pmp # This is the \"Ideal\"" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Electrical mismatch (power derate) is 9.977051 %\n" + ] + } + ], + "source": [ + "Mismatch = (1 - PowerDetailed/PowerAveraged)\n", + "print( \" Electrical mismatch (power derate) is %f %%\" % (Mismatch*100))" + ] + } + ], + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/tutorials/1 - Beginner - Single Module Example.py b/docs/tutorials/1 - Beginner - Single Module Example.py new file mode 100644 index 0000000..a820f32 --- /dev/null +++ b/docs/tutorials/1 - Beginner - Single Module Example.py @@ -0,0 +1,211 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # 1 - Beginner - Single Module Example +# +# This tutorial shows how to assign an array of irradiances as inputs to a module. It is assigning 12 values of irradiances Gpoat, 1 value to each row of six cells in a 12 x 6 module (72 cell module). + +# In[1]: + + +import pvmismatch # this imports everything we need +import numpy as np +import seaborn as sns +import pandas as pd + + +# #### Inputs + +# In[2]: + + +## Inputs: +numcells = 72 +Gpoat = [0.9, 0.9, 0.8, 0.7, 0.7, 0.8, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9] # kW/m2 units +portraitorlandscape = 'portrait' + + +# #### Select module type +# +# The stdpl matrix shows the placement of the cells in the module. The modules we are using are the standard PVMismatch modules, look at the references for the bypass diode groups, but because of this it does matter if the module is in ladscape or portrait. + +# In[3]: + + +# cell placement for 'portrait'. +if numcells == 72: + stdpl=np.array([[0, 23, 24, 47, 48, 71], + [1, 22, 25, 46, 49, 70], + [2, 21, 26, 45, 50, 69], + [3, 20, 27, 44, 51, 68], + [4, 19, 28, 43, 52, 67], + [5, 18, 29, 42, 53, 66], + [6, 17, 30, 41, 54, 65], + [7, 16, 31, 40, 55, 64], + [8, 15, 32, 39, 56, 63], + [9, 14, 33, 38, 57, 62], + [10, 13, 34, 37, 58, 61], + [11, 12, 35, 36, 59, 60]]) + +elif numcells == 96: + stdpl=np.array([[0, 23, 24, 47, 48, 71, 72, 95], + [1, 22, 25, 46, 49, 70, 73, 94], + [2, 21, 26, 45, 50, 69, 74, 93], + [3, 20, 27, 44, 51, 68, 75, 92], + [4, 19, 28, 43, 52, 67, 76, 91], + [5, 18, 29, 42, 53, 66, 77, 90], + [6, 17, 30, 41, 54, 65, 78, 89], + [7, 16, 31, 40, 55, 64, 79, 88], + [8, 15, 32, 39, 56, 63, 80, 87], + [9, 14, 33, 38, 57, 62, 81, 86], + [10, 13, 34, 37, 58, 61, 82, 85], + [11, 12, 35, 36, 59, 60, 83, 84]]) + +if portraitorlandscape == 'landscape': + stdpl = stdpl.transpose() + +cellsx = len(stdpl[1]); cellsy = len(stdpl) + + + + +# #### Let's create the type of module we want + +# In[4]: + + +if cellsx*cellsy == 72: + cell_pos = pvmismatch.pvmismatch_lib.pvmodule.STD72 +elif cellsx*cellsy == 96: + cell_pos = pvmismatch.pvmismatch_lib.pvmodule.STD96 + +pvmod=pvmismatch.pvmismatch_lib.pvmodule.PVmodule(cell_pos=cell_pos) + + +# #### Let's make the system be just 1 module + +# In[5]: + + +pvsys = pvmismatch.pvsystem.PVsystem(numberStrs=1, numberMods=1, pvmods=pvmod) + + +# #### Create the pattern of irradiance based on the Gpoat input. +# +# We are assigning the gradient across the module for this case. + +# In[6]: + + +G=np.array([Gpoat]).transpose() +H = np.ones([1,cellsx]) +array_det = np.dot(G,H) +sns.heatmap(array_det, square = True) +print("This is how our irradiance gradient looks accross the module") + + +# #### Values under STC: +# +# This is under the default irradiance of 1000 W/m2 + +# In[7]: + + +pvsys.plotSys() +print ("Pmp: %f [W], Eff: %f [%%], FF: %f [%%]" % (pvsys.Pmp, pvsys.eff * 100., pvsys.FF * 100.)) +print ("Imp: %f [A], Vmp: %f [V], Isc: %f [A], Voc: %f [V]" % (pvsys.Imp, pvsys.Vmp, pvsys.Isc, pvsys.Voc)) + + +# #### Values with our irradiance profile: + +# In[8]: + + +pvsys.setSuns({0: {0: [array_det, stdpl]}}) +print ("Pmp: %f [W], Eff: %f [%%], FF: %f [%%]" % (pvsys.Pmp, pvsys.eff * 100., pvsys.FF * 100.)) +print ("Imp: %f [A], Vmp: %f [V], Isc: %f [A], Voc: %f [V]" % (pvsys.Imp, pvsys.Vmp, pvsys.Isc, pvsys.Voc)) +PowerDetailed=pvsys.Pmp +pvsys.plotSys() + + +# ### Calculating Mismatch +# +# The power derate, or Mismatch resulting from the module having a distribution of irradiances compared to just one single average irradiance value can be calculated by repeating the power calculation, now with the average irradiance assigned to the whole module, and then calculating the Mismatch: + +# #### First let's calculate the average irradiance value + +# In[9]: + + +array_avg = np.ones([cellsy,cellsx])*np.mean(Gpoat) +averageIrradiance = array_avg.mean() +print(" The module's average irradiance is : %f [kW/m2]", averageIrradiance) +print(" And each cell will see this value of irradiance: ") +print (array_avg) + + +# #### Let's assign the averaged irradiance array to the cells and calculate power. +# +# There's various ways, but they all do the same. +# +# Setting each cell: + +# In[10]: + + +pvsys.setSuns({0: {0: [array_avg, stdpl]}}) # Sets each cell +pvsys.plotSys() +print ("Pmp: %f [W], Eff: %f [%%], FF: %f [%%]" % (pvsys.Pmp, pvsys.eff * 100., pvsys.FF * 100.)) +print ("Imp: %f [A], Vmp: %f [V], Isc: %f [A], Voc: %f [V]" % (pvsys.Imp, pvsys.Vmp, pvsys.Isc, pvsys.Voc)) + + +# Setting the Module: + +# In[11]: + + +pvsys.setSuns({0: {0: averageIrradiance}}) # Sets the module +pvsys.plotSys() +print ("Pmp: %f [W], Eff: %f [%%], FF: %f [%%]" % (pvsys.Pmp, pvsys.eff * 100., pvsys.FF * 100.)) +print ("Imp: %f [A], Vmp: %f [V], Isc: %f [A], Voc: %f [V]" % (pvsys.Imp, pvsys.Vmp, pvsys.Isc, pvsys.Voc)) + + +# Setting the whole system (in this case is just 1 module): + +# In[12]: + + +pvsys.setSuns(averageIrradiance) # Sets the whole system +pvsys.plotSys() +print ("Pmp: %f [W], Eff: %f [%%], FF: %f [%%]" % (pvsys.Pmp, pvsys.eff * 100., pvsys.FF * 100.)) +print ("Imp: %f [A], Vmp: %f [V], Isc: %f [A], Voc: %f [V]" % (pvsys.Imp, pvsys.Vmp, pvsys.Isc, pvsys.Voc)) + + +# As you can see, there are various ways to "setSuns". Some of them are described in the setSuns function: +# (https://github.com/SunPower/PVMismatch/blob/master/pvmismatch/pvmismatch_lib/pvsystem.py) +# +# +# This are the examples/options: +#
    +#
  • Ee={0: {0: {'cells': (0, 1, 2), 'Ee': (0.9, 0.3, 0.5)}}}
    • +#
  • Ee=0.91 # set all modules in all strings to 0.91 suns
    • +#
  • Ee={12: 0.77} # set all modules in string with index 12 to 0.77 suns
    • +#
  • Ee={3: {8: 0.23, 7: 0.45}} # set module with index 8 to 0.23 suns and module with index 7 to 0.45 suns in string with index 3
    • +#
+ +# ##### Calculate the mismatch +# +# Now we have the power under average irradiance conditions, and we can calculate mismatch + +# In[13]: + + +PowerAveraged=pvsys.Pmp # This is the "Ideal" + + +# In[14]: + + +Mismatch = (1 - PowerDetailed/PowerAveraged) +print( " Electrical mismatch (power derate) is %f %%" % (Mismatch*100)) + diff --git a/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.ipynb b/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.ipynb new file mode 100644 index 0000000..82d036a --- /dev/null +++ b/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.ipynb @@ -0,0 +1,276 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2 - Intermediate - Row Power Calculation under different irradiances\n", + "\n", + "Example for calculating power of a row, assigning values from a pickle where each pickle cell is the irradiance value in a cell in the module.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pvmismatch # this imports everything we need\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Inputs\n", + "\n", + "This is a 20 modules single row, with 72 cells in portrait\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "Nmods=20\n", + "portraitorlandscape = 'portrait'\n", + "numcells = 72" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Input Irradiance" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "testfile = r'irr_1axis_11_06_13.pkl'\n", + "df = pd.read_pickle(testfile)\n", + "sns.heatmap(df, square=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Select module type\n", + "\n", + "The stdpl matrix shows the placement of the cells in the module. The modules we are using are the standard PVMismatch modules, look at the references for the bypass diode groups, but because of this it does matter if the module is in ladscape or portrait.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# cell placement for 'portrait'.\n", + "if numcells == 72:\n", + " stdpl=np.array([[0,\t23,\t24,\t47,\t48,\t71],\n", + " [1,\t22,\t25,\t46,\t49,\t70],\n", + " [2,\t21,\t26,\t45,\t50,\t69],\n", + " [3,\t20,\t27,\t44,\t51,\t68],\n", + " [4,\t19,\t28,\t43,\t52,\t67],\n", + " [5,\t18,\t29,\t42,\t53,\t66],\n", + " [6,\t17,\t30,\t41,\t54,\t65],\n", + " [7,\t16,\t31,\t40,\t55,\t64],\n", + " [8,\t15,\t32,\t39,\t56,\t63],\n", + " [9,\t14,\t33,\t38,\t57,\t62],\n", + " [10,\t13,\t34,\t37,\t58,\t61],\n", + " [11,\t12,\t35,\t36,\t59,\t60]])\n", + "\n", + "elif numcells == 96:\n", + " stdpl=np.array([[0,\t23,\t24,\t47,\t48,\t71,\t72,\t95],\n", + " [1,\t22,\t25,\t46,\t49,\t70,\t73,\t94],\n", + " [2,\t21,\t26,\t45,\t50,\t69,\t74,\t93],\n", + " [3,\t20,\t27,\t44,\t51,\t68,\t75,\t92],\n", + " [4,\t19,\t28,\t43,\t52,\t67,\t76,\t91],\n", + " [5,\t18,\t29,\t42,\t53,\t66,\t77,\t90],\n", + " [6,\t17,\t30,\t41,\t54,\t65,\t78,\t89],\n", + " [7,\t16,\t31,\t40,\t55,\t64,\t79,\t88],\n", + " [8,\t15,\t32,\t39,\t56,\t63,\t80,\t87],\n", + " [9,\t14,\t33,\t38,\t57,\t62,\t81,\t86],\n", + " [10,\t13,\t34,\t37,\t58,\t61,\t82,\t85],\n", + " [11,\t12,\t35,\t36,\t59,\t60,\t83,\t84]])\n", + "\n", + "if portraitorlandscape == 'landscape':\n", + " stdpl = stdpl.transpose()\n", + "\n", + "cellsx = len(stdpl[1]); cellsy = len(stdpl)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Let's create the type of module we want" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "if cellsx*cellsy == 72:\n", + " cell_pos = pvmismatch.pvmismatch_lib.pvmodule.STD72\n", + "elif cellsx*cellsy == 96:\n", + " cell_pos = pvmismatch.pvmismatch_lib.pvmodule.STD96\n", + "\n", + "pvmod=pvmismatch.pvmismatch_lib.pvmodule.PVmodule(cell_pos=cell_pos)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Let's make the system be just 1 module" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "#### Let's create the type of module we want\n", + "\n", + "if cellsx*cellsy == 72:\n", + " cell_pos = pvmismatch.pvmismatch_lib.pvmodule.STD72\n", + "elif cellsx*cellsy == 96:\n", + " cell_pos = pvmismatch.pvmismatch_lib.pvmodule.STD96\n", + "\n", + "pvmod=pvmismatch.pvmismatch_lib.pvmodule.PVmodule(cell_pos=cell_pos)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Let's make the 1 row, with 20 modules" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results at STC, no modifications yet\n", + " Pmp: 4818.961 [W], Eff: 21.825 [%], FF: 78.726 [%]\n", + " Imp: 5.915 [A], Vmp: 814.665 [V], Isc: 6.306 [A], Voc: 970.752 [V]\n" + ] + } + ], + "source": [ + "pvsys = pvmismatch.pvsystem.PVsystem(numberStrs=1, numberMods=Nmods, pvmods=pvmod) \n", + "\n", + "print(\"Results at STC, no modifications yet\")\n", + "print (\" Pmp: %1.3f [W], Eff: %1.3f [%%], FF: %1.3F [%%]\" % (pvsys.Pmp, pvsys.eff*100, pvsys.FF*100))\n", + "print (\" Imp: %1.3f [A], Vmp: %1.3f [V], Isc: %1.3f [A], Voc: %1.3f [V]\" % (pvsys.Imp, pvsys.Vmp, pvsys.Isc, pvsys.Voc))\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Assign the dataframe irradiances to each cell in each module\n", + "\n", + "We do this by creating a dictionary. Remember that the dataframe is in W/m2 and PVMismatch uses kW/m2 as irradiance inputs, so we have to divide by a 1000. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "modDict = {}\n", + "for i in range (0, Nmods):\n", + " modDict[i] = [df[i*cellsx:(i+1)*cellsx].T/1000, stdpl]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "System results with the irradiance profile\n", + " Pmp: 2879.027 [W], Eff: 21.366 [%], FF: 75.367 [%]\n", + " Imp: 3.551 [A], Vmp: 810.869 [V], Isc: 4.016 [A], Voc: 951.246 [V]\n" + ] + } + ], + "source": [ + "pvsys.setSuns({0: modDict}) \n", + "\n", + "print(\"System results with the irradiance profile\")\n", + "print (\" Pmp: %1.3f [W], Eff: %1.3f [%%], FF: %1.3F [%%]\" % (pvsys.Pmp, pvsys.eff*100, pvsys.FF*100))\n", + "print (\" Imp: %1.3f [A], Vmp: %1.3f [V], Isc: %1.3f [A], Voc: %1.3f [V]\" % (pvsys.Imp, pvsys.Vmp, pvsys.Isc, pvsys.Voc))\n" + ] + } + ], + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.py b/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.py new file mode 100644 index 0000000..cad94ba --- /dev/null +++ b/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.py @@ -0,0 +1,146 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # 2 - Intermediate - Row Power Calculation under different irradiances +# +# Example for calculating power of a row, assigning values from a pickle where each pickle cell is the irradiance value in a cell in the module. +# + +# In[1]: + + +import pvmismatch # this imports everything we need +import numpy as np +import seaborn as sns +import pandas as pd + + +# #### Inputs +# +# This is a 20 modules single row, with 72 cells in portrait +# + +# In[2]: + + +Nmods=20 +portraitorlandscape = 'portrait' +numcells = 72 + + +# #### Input Irradiance + +# In[3]: + + +testfile = r'irr_1axis_11_06_13.pkl' +df = pd.read_pickle(testfile) +sns.heatmap(df, square=True) + + +# #### Select module type +# +# The stdpl matrix shows the placement of the cells in the module. The modules we are using are the standard PVMismatch modules, look at the references for the bypass diode groups, but because of this it does matter if the module is in ladscape or portrait. +# + +# In[4]: + + +# cell placement for 'portrait'. +if numcells == 72: + stdpl=np.array([[0, 23, 24, 47, 48, 71], + [1, 22, 25, 46, 49, 70], + [2, 21, 26, 45, 50, 69], + [3, 20, 27, 44, 51, 68], + [4, 19, 28, 43, 52, 67], + [5, 18, 29, 42, 53, 66], + [6, 17, 30, 41, 54, 65], + [7, 16, 31, 40, 55, 64], + [8, 15, 32, 39, 56, 63], + [9, 14, 33, 38, 57, 62], + [10, 13, 34, 37, 58, 61], + [11, 12, 35, 36, 59, 60]]) + +elif numcells == 96: + stdpl=np.array([[0, 23, 24, 47, 48, 71, 72, 95], + [1, 22, 25, 46, 49, 70, 73, 94], + [2, 21, 26, 45, 50, 69, 74, 93], + [3, 20, 27, 44, 51, 68, 75, 92], + [4, 19, 28, 43, 52, 67, 76, 91], + [5, 18, 29, 42, 53, 66, 77, 90], + [6, 17, 30, 41, 54, 65, 78, 89], + [7, 16, 31, 40, 55, 64, 79, 88], + [8, 15, 32, 39, 56, 63, 80, 87], + [9, 14, 33, 38, 57, 62, 81, 86], + [10, 13, 34, 37, 58, 61, 82, 85], + [11, 12, 35, 36, 59, 60, 83, 84]]) + +if portraitorlandscape == 'landscape': + stdpl = stdpl.transpose() + +cellsx = len(stdpl[1]); cellsy = len(stdpl) + + + +# #### Let's create the type of module we want + +# In[5]: + + +if cellsx*cellsy == 72: + cell_pos = pvmismatch.pvmismatch_lib.pvmodule.STD72 +elif cellsx*cellsy == 96: + cell_pos = pvmismatch.pvmismatch_lib.pvmodule.STD96 + +pvmod=pvmismatch.pvmismatch_lib.pvmodule.PVmodule(cell_pos=cell_pos) + + +# #### Let's make the system be just 1 module + +# In[6]: + + +#### Let's create the type of module we want + +if cellsx*cellsy == 72: + cell_pos = pvmismatch.pvmismatch_lib.pvmodule.STD72 +elif cellsx*cellsy == 96: + cell_pos = pvmismatch.pvmismatch_lib.pvmodule.STD96 + +pvmod=pvmismatch.pvmismatch_lib.pvmodule.PVmodule(cell_pos=cell_pos) + + +# #### Let's make the 1 row, with 20 modules + +# In[7]: + + +pvsys = pvmismatch.pvsystem.PVsystem(numberStrs=1, numberMods=Nmods, pvmods=pvmod) + +print("Results at STC, no modifications yet") +print (" Pmp: %1.3f [W], Eff: %1.3f [%%], FF: %1.3F [%%]" % (pvsys.Pmp, pvsys.eff*100, pvsys.FF*100)) +print (" Imp: %1.3f [A], Vmp: %1.3f [V], Isc: %1.3f [A], Voc: %1.3f [V]" % (pvsys.Imp, pvsys.Vmp, pvsys.Isc, pvsys.Voc)) + + + +# #### Assign the dataframe irradiances to each cell in each module +# +# We do this by creating a dictionary. Remember that the dataframe is in W/m2 and PVMismatch uses kW/m2 as irradiance inputs, so we have to divide by a 1000. + +# In[8]: + + +modDict = {} +for i in range (0, Nmods): + modDict[i] = [df[i*cellsx:(i+1)*cellsx].T/1000, stdpl] + + +# In[9]: + + +pvsys.setSuns({0: modDict}) + +print("System results with the irradiance profile") +print (" Pmp: %1.3f [W], Eff: %1.3f [%%], FF: %1.3F [%%]" % (pvsys.Pmp, pvsys.eff*100, pvsys.FF*100)) +print (" Imp: %1.3f [A], Vmp: %1.3f [V], Isc: %1.3f [A], Voc: %1.3f [V]" % (pvsys.Imp, pvsys.Vmp, pvsys.Isc, pvsys.Voc)) + diff --git a/docs/tutorials/irr_1axis_11_06_13.pkl b/docs/tutorials/irr_1axis_11_06_13.pkl new file mode 100644 index 0000000000000000000000000000000000000000..4daf170911840480e477cb38310701331f98c357 GIT binary patch literal 12101 zcmZvid0dZA^yrI9C5j4JLr9UeWFH|VMUgE@DMd<~B};gok8B~7ec$&bgh)ilzGRo| zWG_P2`<{>Q@4oJR-P<3X&&+e?%$b=pXPs$!sqLESX#bKbMFmC%2gW)K9}^SeFd`-} zA|xfrs9RuMpsV)x;*`eIQ>LWYCg}$R)0vV~`~Sor85tK66B#%<)*&J=GB7kGCN?F> 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zl>|!2RW5arVC2u9#MX)i|KNQGRbXBdo+GEn;}%!bYYM*13pnyl27Bh-k$s!GHo7kG z3F@;<=YMv=$%8sjPL1W|d{i-(hYj|_$s1EvObDUgp6u@GH6H!Qd&5n0k@FhqO^sJm zFX}YFujtUf`m6C3;#&NxM$eEx^0Og7RH0U#qF;#J%^!NBT2mYR;*t*w+O*XOo^iak z=iaW!ZS!CidF8XdAqBoc8nNTBLHe9fjkZ3XIZ-3>>_e=AW{{t@pIK!P?>ElMhhGb* zGZE1B?~mR{n^fGKx^zakQR%wgyc?qMogbbr3i zv&5hHp-z7_v;+3Kb%wf(d%woUzg1h>XHwiY+;QT&@r|VJ^w)}M_3$L^ngsl3fN4BL zyU6}NoOac8V>8}EHY@qu5d9$IgHx@^%W=&U(F=Y&Na4kR_I*MD`%kp#_!F2c6M&3Q z%eWOQ-yIR&`|h(tuEuF@x_}?<_HcM(@_bh}AKB3axbI2}>tmeb8u`wPHtB*n_t7(2 zJ7bTWP}nu+{L{C@pBo9x;(#85FYEg~X}_skJ--X%x)LZzJ=H-9D)J-sm^*wnK%B=T zH`Bh>@BSP`Ar@%`hj{Bi4PA0_WM5`HQXFGjcer|;Te4`sfxOv``yo?gKI zsHYO>G%_uJXoNF*oWzkQc1G6Sx0BBuh<}?3Jc)mOpd0q%tVf$%Ig$IReV69VxgfCh`f(qr_YWKP-+RyzyeF3a9`{xH?YR@UpJk@h>ph3}4l7n1YZA5Pirqgz}&8r->IE>T} Date: Tue, 2 Jun 2020 15:44:26 -0600 Subject: [PATCH 2/3] Journal 2 small cleanup fix repetition of module creation --- ...culation under different irradiances.ipynb | 23 ------------------- ...Calculation under different irradiances.py | 15 ------------ 2 files changed, 38 deletions(-) diff --git a/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.ipynb b/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.ipynb index 82d036a..444c0b5 100644 --- a/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.ipynb +++ b/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.ipynb @@ -154,29 +154,6 @@ "pvmod=pvmismatch.pvmismatch_lib.pvmodule.PVmodule(cell_pos=cell_pos)\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Let's make the system be just 1 module" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "#### Let's create the type of module we want\n", - "\n", - "if cellsx*cellsy == 72:\n", - " cell_pos = pvmismatch.pvmismatch_lib.pvmodule.STD72\n", - "elif cellsx*cellsy == 96:\n", - " cell_pos = pvmismatch.pvmismatch_lib.pvmodule.STD96\n", - "\n", - "pvmod=pvmismatch.pvmismatch_lib.pvmodule.PVmodule(cell_pos=cell_pos)" - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.py b/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.py index cad94ba..a5cd865 100644 --- a/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.py +++ b/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.py @@ -95,21 +95,6 @@ pvmod=pvmismatch.pvmismatch_lib.pvmodule.PVmodule(cell_pos=cell_pos) -# #### Let's make the system be just 1 module - -# In[6]: - - -#### Let's create the type of module we want - -if cellsx*cellsy == 72: - cell_pos = pvmismatch.pvmismatch_lib.pvmodule.STD72 -elif cellsx*cellsy == 96: - cell_pos = pvmismatch.pvmismatch_lib.pvmodule.STD96 - -pvmod=pvmismatch.pvmismatch_lib.pvmodule.PVmodule(cell_pos=cell_pos) - - # #### Let's make the 1 row, with 20 modules # In[7]: From b32fb1f5649b9e03e1a030b0c8e9dfe632c2099a Mon Sep 17 00:00:00 2001 From: Silvana Ayala Date: Thu, 11 Jun 2020 10:49:19 -0600 Subject: [PATCH 3/3] csv instead of pickle replaced pickle with csv and updated journals --- ...culation under different irradiances.ipynb | 4 +- ...Calculation under different irradiances.py | 4 +- docs/tutorials/irr_1axis_11_06_13.csv | 121 ++++++++++++++++++ docs/tutorials/irr_1axis_11_06_13.pkl | Bin 12101 -> 0 bytes 4 files changed, 125 insertions(+), 4 deletions(-) create mode 100644 docs/tutorials/irr_1axis_11_06_13.csv delete mode 100644 docs/tutorials/irr_1axis_11_06_13.pkl diff --git a/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.ipynb b/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.ipynb index 444c0b5..83dd6e4 100644 --- a/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.ipynb +++ b/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.ipynb @@ -77,8 +77,8 @@ } ], "source": [ - "testfile = r'irr_1axis_11_06_13.pkl'\n", - "df = pd.read_pickle(testfile)\n", + "testfile = r'irr_1axis_11_06_13.csv'\n", + "df = pd.read_csv(testfile)\n", "sns.heatmap(df, square=True)" ] }, diff --git a/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.py b/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.py index a5cd865..da241fd 100644 --- a/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.py +++ b/docs/tutorials/2 - Intermediate - Row Power Calculation under different irradiances.py @@ -33,8 +33,8 @@ # In[3]: -testfile = r'irr_1axis_11_06_13.pkl' -df = pd.read_pickle(testfile) +testfile = r'irr_1axis_11_06_13.csv' +df = pd.read_csv(testfile) sns.heatmap(df, square=True) diff --git a/docs/tutorials/irr_1axis_11_06_13.csv b/docs/tutorials/irr_1axis_11_06_13.csv new file mode 100644 index 0000000..4dd4ce1 --- /dev/null +++ b/docs/tutorials/irr_1axis_11_06_13.csv @@ -0,0 +1,121 @@ +0,1,2,3,4,5,6,7,8,9,10,11 +622.03771,618.7740699999999,614.78195,612.4252,607.1510599999999,592.34128,597.73401,605.46429,610.7762299999999,612.90477,616.13903,618.79207 +621.56858,618.25651,613.13983,610.60051,605.02961,589.9008600000001,596.14293,603.39864,609.50406,613.6173099999999,616.25847,618.8989 +621.12951,617.85046,612.78004,609.91119,604.51441,589.46234,595.67398,601.98557,610.02363,612.09467,615.24491,617.9536 +620.25627,616.84083,611.8029899999999,609.2660199999999,603.1510000000001,588.52185,595.50689,601.3339199999999,609.29991,611.52104,614.8851199999999,617.53558 +619.74321,616.46357,611.59952,608.58419,602.7408899999999,588.0358100000001,595.08473,601.61541,608.7338,610.8064400000001,614.3287399999999,616.89389 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