_images/toyplot.png

Matrix Visualization

It is often useful to render a two-dimensional matrix as a regular grid, colored by the matrix values, as a way to look for patterns in data. To facilitate this, Toyplot provides toyplot.canvas.Canvas.matrix() and toyplot.matrix() functions. To demonstrate, let’s begin with a visualization of a matrix containing values from a normal distribution centered around 1.0:

import numpy
numpy.random.seed(1234)
matrix = numpy.random.normal(loc=1.0, size=(20, 20))
import toyplot
toyplot.matrix(matrix, label="A matrix");
A matrix01234567891011121314151617181901.471435163732493-0.190975694706464472.4327069684260970.68734810390828720.27941126663498841.88716294030773861.85958841371741630.363476495582650941.015696372114429-1.24268495418540552.15003572471981831.9919460223426781.9533241281124303-1.02125482019497050.6659226341919031.00211836468348661.40545341157019091.28909194098003522.321158192129386-0.546905553229240210.79735367537081810.344030655861066071.19342137647035831.5534389109567422.31815155418013670.53069471529410041.6755540851223807-0.81702722659019680.81689145982100132.05896918757115040.60215977180000871.33743765361397232.0475785728927222.04593825562766531.86371729168483880.87790842515232571.1247129537682160.67720519439170431.84167471299614153.39096051546303321.07619958783723630.433554069535043161.0361419366840727-1.07497760069002931.24779219974854660.10284321556030130.86320516673865271.01828919134921941.75541398239813541.21526858096944351.841008794931391-0.4458100770443063-0.40197328150084390.89908180005108610.451757550813145060.85538049163061561.3540203321992380.96448697472185981.56573830606259512.545658804625557730.0257636662326845570.92965512289589761.30796885521603420.79150123689412022.033800732555499-1.40045363381229573.0306036208387996-0.142631289022763451.2118833867777011.70472062431710870.214564788236803031.46205973716204871.70422822546217431.52350796789380950.073745686469774093.00784295077800051.2269625418708954-0.152659109250952391.63197944580912951.039512686693366641.4643923250508961-2.56351666062473532.32110561547020571.15263055220453441.16452954293239850.56990430912351241.76736873575241151.9849198419098971.2708358488268042.39198619344640751.0798423130086290.6000354193034775-0.027850558681905780.415281788739211731.81659392654784170.91805294817333460.65523398574535571.528288145297394-0.068988783480132150.4881186908731849351.29120535974306351.56653369635357231.5035917591112031.2852956847818571.484288112749752.36348151242614570.2188947163746080.53198233366251452.2245743551261743-0.28110827514404261.8754755042743243-0.7107153240295290.54923489686372561.7491638059190650.79606713389874880.81782458833426591.6806560043814565-0.81849899039161421.0470716353257111.394844209327204460.75156794561915330.38229335200298330.317116003550666051.4362576043409168-0.70301277411323791.39371059913866510.52067599642450270.70098370703391951.69410328767876431.67862967370985651.23955599500389681.15122662929444971.8161272333600412.89353446759620071.63963276319370270.037971168094808605-1.08526564212010972.930246767465576-0.73534887447039272.210383704904514571.79743541942787340.6201892159526211.70256222400160030.1496537283448852.1768124501049290.4756638973675441.70090773091560471.98418807072241550.87827159133317983.3657686288400391.49614292624759471.79659486666495230.5259791098743150.94330428350907052.3577972581070580.19516627583329627-1.12362024909293010.66649755956675260.113280647514621971.334197930899008881.53678382490275030.25616963206379870.6797961177591210.083801138733459070.140331700018259341.22598548673375541.62877582653829431.18649434877307461.95247834510912191.98813758259330870.92739168603732450.449397076436580670.06184738590689576-0.239071562594792561.13968327403129480.7769810181175243.1236918885935531.122273434258829-0.40943173992276872.4229859527722439-1.1478550376436099-0.347532513457754931.36356455680664830.98524788819540512.27239507854621-0.4495666088633876-0.195523741666970840.4081370268527510.5854951564678105-0.425794733438021431.20939478753592390.4071139961610746-0.473116413465647370.103419384698128952.10435156986031260.56845048448395530.83886309175594111.88915749407287371.2883768477152655-0.051538937570038135100.68043860015976850.38000691227879781.1569983764466890.42854464892021072.0576331832046140.208511190522114530.475372654071529841.07187803937036862.9107594272771831.78796468313761441.5130821444669920.45358371972640612.0439446554953233.107785152793372.4599274815852942.01540545781252071.74918460092419760.32447852156331181.44026639166872971.6889718563269422110.72335387272301272.924533320015291.41120402070722381.8907649561731171.2263632272517069-1.07861789370217530.6121137680328240.9128930300360012.1263858221372731.24711171615773631.1211718053315411.29898393554204670.84290086315334830.2595309772192821-0.247652923051232771.24945521515244941.58107332062060653.76384407984956631.39932543697957621.6684881692058056120.72422627134369491.50048290015437221.8630648605906246-0.0516278594652122-0.392054077493086962.15392224061222542.1819441059992321.3913714484219310.11895290779843061.295079959786242.863801062132504-0.7122739244712557-0.40708454574322551.12678118632697171.0037598477954959-0.26899425607339180.161157078579710071.553921251439720.495957281918036340.21155981548212133132.52940067448393121.20545485392597971.31301312898086041.86652071216829941.29907052959437632.0765406438461221.36317734754445222.89367982555184260.51424725571719171.38767445577759841.02355761039255370.33977049795572010.68131507042198570.27733780632562731.1773867193845991.98351272934316421.02350458588539461.55377697786336591.35376929399663970.7244063662323177140.50951640852131860.6255633998072194-1.3975039761388162.54102998327793861.06308453895730510.7155593160737073-0.26560053760121072.787979632455750.64549129193062570.89523887693289571.38625368918905251.82277537376537340.316210177568992172.05720333121412931.03187997356903162.34318245752894150.94946031045122760.6359900436928911-0.55334233943106080.6807021981798389151.52704645095496421.7111123993648230.78245451950952383.6377912107038637-0.74213763059496540.90556519225855582.4311837522545641.59275844655066881.170296895326288-0.75170594779202451.28858132234320790.45742048309322071.17160238198599491.98281782924310470.97465109793723130.71244755071589191.9244428673433660.93875370013368540.26896665741479386-0.022773704695142838161.99599297024575241.9555579326274551.71384077119504321.1333709621941220.192961691527186560.65798921812078492.90877965809118641.15592326145312671.75965253209296260.57699377158075161.18167274532368171.27449257074640341.06791235738075720.96301718512408050.82919896930237761.26697323153986792.38299724885292540.97753912648857341.13139536722591521.4344371851022324171.26453406831699881.56565834427382461.58508427950172460.8257018472479126-0.071368671455089630.95153949744761680.154709610433756531.41510055838158081.42553059139785840.019275642499414160.57217389002797952.4985699551385370.6398434291309720.5417596106625071-0.337968401950970550.95861346038512811.8210479561079153.09780078077169162.28293324687452341.2703379257393719182.00314036204493152.07867355132040641.34075268512976420.80192504037643183.48145842287565352.3852548761892063-0.15460096538966894-0.26806905037708151.6078621862094797-0.080096483157590990.388718325204237771.1020351122923313-0.436573936772118641.21071705250565520.10309582930248773-0.72439307899183312.7923391966469424-0.31271270659223151.5558774263856250.3181186945088693192.57274274575580450.8953484251522342.8503978016099010.66664957325059951.19346423381987240.5032550621350912.032723215735861-0.73980379752255930.24413782437709120.118888267352064481.39389226291267530.0499744652037167341.33250662216224921.5289444088560467-0.120520993992724981.04826420692565671.0619884505217776-0.027515673217867410.7616646345326742.9321781695813467

By default, the matrix is rendered using a Color Brewer diverging “BlueRed” linear map, mapped to the minimum and maximum values in the matrix. Thus, dark blue represents the minimum value and dark red is the maximum value. If you hover the mouse over the cells in the matrix you can see their values with a popup.

You can also display an optional color scale that shows the mapping from matrix values to colors:

toyplot.matrix(matrix, label="A matrix", colorshow=True);
A matrix01234567891011121314151617181901.471435163732493-0.190975694706464472.4327069684260970.68734810390828720.27941126663498841.88716294030773861.85958841371741630.363476495582650941.015696372114429-1.24268495418540552.15003572471981831.9919460223426781.9533241281124303-1.02125482019497050.6659226341919031.00211836468348661.40545341157019091.28909194098003522.321158192129386-0.546905553229240210.79735367537081810.344030655861066071.19342137647035831.5534389109567422.31815155418013670.53069471529410041.6755540851223807-0.81702722659019680.81689145982100132.05896918757115040.60215977180000871.33743765361397232.0475785728927222.04593825562766531.86371729168483880.87790842515232571.1247129537682160.67720519439170431.84167471299614153.39096051546303321.07619958783723630.433554069535043161.0361419366840727-1.07497760069002931.24779219974854660.10284321556030130.86320516673865271.01828919134921941.75541398239813541.21526858096944351.841008794931391-0.4458100770443063-0.40197328150084390.89908180005108610.451757550813145060.85538049163061561.3540203321992380.96448697472185981.56573830606259512.545658804625557730.0257636662326845570.92965512289589761.30796885521603420.79150123689412022.033800732555499-1.40045363381229573.0306036208387996-0.142631289022763451.2118833867777011.70472062431710870.214564788236803031.46205973716204871.70422822546217431.52350796789380950.073745686469774093.00784295077800051.2269625418708954-0.152659109250952391.63197944580912951.039512686693366641.4643923250508961-2.56351666062473532.32110561547020571.15263055220453441.16452954293239850.56990430912351241.76736873575241151.9849198419098971.2708358488268042.39198619344640751.0798423130086290.6000354193034775-0.027850558681905780.415281788739211731.81659392654784170.91805294817333460.65523398574535571.528288145297394-0.068988783480132150.4881186908731849351.29120535974306351.56653369635357231.5035917591112031.2852956847818571.484288112749752.36348151242614570.2188947163746080.53198233366251452.2245743551261743-0.28110827514404261.8754755042743243-0.7107153240295290.54923489686372561.7491638059190650.79606713389874880.81782458833426591.6806560043814565-0.81849899039161421.0470716353257111.394844209327204460.75156794561915330.38229335200298330.317116003550666051.4362576043409168-0.70301277411323791.39371059913866510.52067599642450270.70098370703391951.69410328767876431.67862967370985651.23955599500389681.15122662929444971.8161272333600412.89353446759620071.63963276319370270.037971168094808605-1.08526564212010972.930246767465576-0.73534887447039272.210383704904514571.79743541942787340.6201892159526211.70256222400160030.1496537283448852.1768124501049290.4756638973675441.70090773091560471.98418807072241550.87827159133317983.3657686288400391.49614292624759471.79659486666495230.5259791098743150.94330428350907052.3577972581070580.19516627583329627-1.12362024909293010.66649755956675260.113280647514621971.334197930899008881.53678382490275030.25616963206379870.6797961177591210.083801138733459070.140331700018259341.22598548673375541.62877582653829431.18649434877307461.95247834510912191.98813758259330870.92739168603732450.449397076436580670.06184738590689576-0.239071562594792561.13968327403129480.7769810181175243.1236918885935531.122273434258829-0.40943173992276872.4229859527722439-1.1478550376436099-0.347532513457754931.36356455680664830.98524788819540512.27239507854621-0.4495666088633876-0.195523741666970840.4081370268527510.5854951564678105-0.425794733438021431.20939478753592390.4071139961610746-0.473116413465647370.103419384698128952.10435156986031260.56845048448395530.83886309175594111.88915749407287371.2883768477152655-0.051538937570038135100.68043860015976850.38000691227879781.1569983764466890.42854464892021072.0576331832046140.208511190522114530.475372654071529841.07187803937036862.9107594272771831.78796468313761441.5130821444669920.45358371972640612.0439446554953233.107785152793372.4599274815852942.01540545781252071.74918460092419760.32447852156331181.44026639166872971.6889718563269422110.72335387272301272.924533320015291.41120402070722381.8907649561731171.2263632272517069-1.07861789370217530.6121137680328240.9128930300360012.1263858221372731.24711171615773631.1211718053315411.29898393554204670.84290086315334830.2595309772192821-0.247652923051232771.24945521515244941.58107332062060653.76384407984956631.39932543697957621.6684881692058056120.72422627134369491.50048290015437221.8630648605906246-0.0516278594652122-0.392054077493086962.15392224061222542.1819441059992321.3913714484219310.11895290779843061.295079959786242.863801062132504-0.7122739244712557-0.40708454574322551.12678118632697171.0037598477954959-0.26899425607339180.161157078579710071.553921251439720.495957281918036340.21155981548212133132.52940067448393121.20545485392597971.31301312898086041.86652071216829941.29907052959437632.0765406438461221.36317734754445222.89367982555184260.51424725571719171.38767445577759841.02355761039255370.33977049795572010.68131507042198570.27733780632562731.1773867193845991.98351272934316421.02350458588539461.55377697786336591.35376929399663970.7244063662323177140.50951640852131860.6255633998072194-1.3975039761388162.54102998327793861.06308453895730510.7155593160737073-0.26560053760121072.787979632455750.64549129193062570.89523887693289571.38625368918905251.82277537376537340.316210177568992172.05720333121412931.03187997356903162.34318245752894150.94946031045122760.6359900436928911-0.55334233943106080.6807021981798389151.52704645095496421.7111123993648230.78245451950952383.6377912107038637-0.74213763059496540.90556519225855582.4311837522545641.59275844655066881.170296895326288-0.75170594779202451.28858132234320790.45742048309322071.17160238198599491.98281782924310470.97465109793723130.71244755071589191.9244428673433660.93875370013368540.26896665741479386-0.022773704695142838161.99599297024575241.9555579326274551.71384077119504321.1333709621941220.192961691527186560.65798921812078492.90877965809118641.15592326145312671.75965253209296260.57699377158075161.18167274532368171.27449257074640341.06791235738075720.96301718512408050.82919896930237761.26697323153986792.38299724885292540.97753912648857341.13139536722591521.4344371851022324171.26453406831699881.56565834427382461.58508427950172460.8257018472479126-0.071368671455089630.95153949744761680.154709610433756531.41510055838158081.42553059139785840.019275642499414160.57217389002797952.4985699551385370.6398434291309720.5417596106625071-0.337968401950970550.95861346038512811.8210479561079153.09780078077169162.28293324687452341.2703379257393719182.00314036204493152.07867355132040641.34075268512976420.80192504037643183.48145842287565352.3852548761892063-0.15460096538966894-0.26806905037708151.6078621862094797-0.080096483157590990.388718325204237771.1020351122923313-0.436573936772118641.21071705250565520.10309582930248773-0.72439307899183312.7923391966469424-0.31271270659223151.5558774263856250.3181186945088693192.57274274575580450.8953484251522342.8503978016099010.66664957325059951.19346423381987240.5032550621350912.032723215735861-0.73980379752255930.24413782437709120.118888267352064481.39389226291267530.0499744652037167341.33250662216224921.5289444088560467-0.120520993992724981.04826420692565671.0619884505217776-0.027515673217867410.7616646345326742.9321781695813467-2024

Note that, because our minimum and maximum data values aren’t symmetric around the origin, the white section of the color map doesn’t map to zero, and some values greater than zero are mapped to the cool blue portion of the spectrum. Let’s fix this and force our our colormap to be symmetric around zero so all blue colors are negative and all red colors are positive. To do so, we simply create a custom toyplot.color.LinearMap, specifying explicit minimum and maximum domain values and using it in the call to create the matrix visualization:

colormap = toyplot.color.brewer.map("BlueRed", domain_min=-4, domain_max=4)
toyplot.matrix((matrix, colormap), label="A matrix", colorshow=True);
A matrix01234567891011121314151617181901.471435163732493-0.190975694706464472.4327069684260970.68734810390828720.27941126663498841.88716294030773861.85958841371741630.363476495582650941.015696372114429-1.24268495418540552.15003572471981831.9919460223426781.9533241281124303-1.02125482019497050.6659226341919031.00211836468348661.40545341157019091.28909194098003522.321158192129386-0.546905553229240210.79735367537081810.344030655861066071.19342137647035831.5534389109567422.31815155418013670.53069471529410041.6755540851223807-0.81702722659019680.81689145982100132.05896918757115040.60215977180000871.33743765361397232.0475785728927222.04593825562766531.86371729168483880.87790842515232571.1247129537682160.67720519439170431.84167471299614153.39096051546303321.07619958783723630.433554069535043161.0361419366840727-1.07497760069002931.24779219974854660.10284321556030130.86320516673865271.01828919134921941.75541398239813541.21526858096944351.841008794931391-0.4458100770443063-0.40197328150084390.89908180005108610.451757550813145060.85538049163061561.3540203321992380.96448697472185981.56573830606259512.545658804625557730.0257636662326845570.92965512289589761.30796885521603420.79150123689412022.033800732555499-1.40045363381229573.0306036208387996-0.142631289022763451.2118833867777011.70472062431710870.214564788236803031.46205973716204871.70422822546217431.52350796789380950.073745686469774093.00784295077800051.2269625418708954-0.152659109250952391.63197944580912951.039512686693366641.4643923250508961-2.56351666062473532.32110561547020571.15263055220453441.16452954293239850.56990430912351241.76736873575241151.9849198419098971.2708358488268042.39198619344640751.0798423130086290.6000354193034775-0.027850558681905780.415281788739211731.81659392654784170.91805294817333460.65523398574535571.528288145297394-0.068988783480132150.4881186908731849351.29120535974306351.56653369635357231.5035917591112031.2852956847818571.484288112749752.36348151242614570.2188947163746080.53198233366251452.2245743551261743-0.28110827514404261.8754755042743243-0.7107153240295290.54923489686372561.7491638059190650.79606713389874880.81782458833426591.6806560043814565-0.81849899039161421.0470716353257111.394844209327204460.75156794561915330.38229335200298330.317116003550666051.4362576043409168-0.70301277411323791.39371059913866510.52067599642450270.70098370703391951.69410328767876431.67862967370985651.23955599500389681.15122662929444971.8161272333600412.89353446759620071.63963276319370270.037971168094808605-1.08526564212010972.930246767465576-0.73534887447039272.210383704904514571.79743541942787340.6201892159526211.70256222400160030.1496537283448852.1768124501049290.4756638973675441.70090773091560471.98418807072241550.87827159133317983.3657686288400391.49614292624759471.79659486666495230.5259791098743150.94330428350907052.3577972581070580.19516627583329627-1.12362024909293010.66649755956675260.113280647514621971.334197930899008881.53678382490275030.25616963206379870.6797961177591210.083801138733459070.140331700018259341.22598548673375541.62877582653829431.18649434877307461.95247834510912191.98813758259330870.92739168603732450.449397076436580670.06184738590689576-0.239071562594792561.13968327403129480.7769810181175243.1236918885935531.122273434258829-0.40943173992276872.4229859527722439-1.1478550376436099-0.347532513457754931.36356455680664830.98524788819540512.27239507854621-0.4495666088633876-0.195523741666970840.4081370268527510.5854951564678105-0.425794733438021431.20939478753592390.4071139961610746-0.473116413465647370.103419384698128952.10435156986031260.56845048448395530.83886309175594111.88915749407287371.2883768477152655-0.051538937570038135100.68043860015976850.38000691227879781.1569983764466890.42854464892021072.0576331832046140.208511190522114530.475372654071529841.07187803937036862.9107594272771831.78796468313761441.5130821444669920.45358371972640612.0439446554953233.107785152793372.4599274815852942.01540545781252071.74918460092419760.32447852156331181.44026639166872971.6889718563269422110.72335387272301272.924533320015291.41120402070722381.8907649561731171.2263632272517069-1.07861789370217530.6121137680328240.9128930300360012.1263858221372731.24711171615773631.1211718053315411.29898393554204670.84290086315334830.2595309772192821-0.247652923051232771.24945521515244941.58107332062060653.76384407984956631.39932543697957621.6684881692058056120.72422627134369491.50048290015437221.8630648605906246-0.0516278594652122-0.392054077493086962.15392224061222542.1819441059992321.3913714484219310.11895290779843061.295079959786242.863801062132504-0.7122739244712557-0.40708454574322551.12678118632697171.0037598477954959-0.26899425607339180.161157078579710071.553921251439720.495957281918036340.21155981548212133132.52940067448393121.20545485392597971.31301312898086041.86652071216829941.29907052959437632.0765406438461221.36317734754445222.89367982555184260.51424725571719171.38767445577759841.02355761039255370.33977049795572010.68131507042198570.27733780632562731.1773867193845991.98351272934316421.02350458588539461.55377697786336591.35376929399663970.7244063662323177140.50951640852131860.6255633998072194-1.3975039761388162.54102998327793861.06308453895730510.7155593160737073-0.26560053760121072.787979632455750.64549129193062570.89523887693289571.38625368918905251.82277537376537340.316210177568992172.05720333121412931.03187997356903162.34318245752894150.94946031045122760.6359900436928911-0.55334233943106080.6807021981798389151.52704645095496421.7111123993648230.78245451950952383.6377912107038637-0.74213763059496540.90556519225855582.4311837522545641.59275844655066881.170296895326288-0.75170594779202451.28858132234320790.45742048309322071.17160238198599491.98281782924310470.97465109793723130.71244755071589191.9244428673433660.93875370013368540.26896665741479386-0.022773704695142838161.99599297024575241.9555579326274551.71384077119504321.1333709621941220.192961691527186560.65798921812078492.90877965809118641.15592326145312671.75965253209296260.57699377158075161.18167274532368171.27449257074640341.06791235738075720.96301718512408050.82919896930237761.26697323153986792.38299724885292540.97753912648857341.13139536722591521.4344371851022324171.26453406831699881.56565834427382461.58508427950172460.8257018472479126-0.071368671455089630.95153949744761680.154709610433756531.41510055838158081.42553059139785840.019275642499414160.57217389002797952.4985699551385370.6398434291309720.5417596106625071-0.337968401950970550.95861346038512811.8210479561079153.09780078077169162.28293324687452341.2703379257393719182.00314036204493152.07867355132040641.34075268512976420.80192504037643183.48145842287565352.3852548761892063-0.15460096538966894-0.26806905037708151.6078621862094797-0.080096483157590990.388718325204237771.1020351122923313-0.436573936772118641.21071705250565520.10309582930248773-0.72439307899183312.7923391966469424-0.31271270659223151.5558774263856250.3181186945088693192.57274274575580450.8953484251522342.8503978016099010.66664957325059951.19346423381987240.5032550621350912.032723215735861-0.73980379752255930.24413782437709120.118888267352064481.39389226291267530.0499744652037167341.33250662216224921.5289444088560467-0.120520993992724981.04826420692565671.0619884505217776-0.027515673217867410.7616646345326742.9321781695813467-4-2024

Now we see that the color map is nicely symmetric around the origin, making it clear which values are positive and which are negative.

In addition to the top-level label, you can specify labels on any side of the matrix. Note the convention in the parameter names: t for “top”, l for “left”, r for “right”, and b for “bottom”:

toyplot.matrix((matrix, colormap), label="A matrix", tlabel="Top", llabel="Left", rlabel="Right", blabel="Bottom");
A matrix01234567891011121314151617181901.471435163732493-0.190975694706464472.4327069684260970.68734810390828720.27941126663498841.88716294030773861.85958841371741630.363476495582650941.015696372114429-1.24268495418540552.15003572471981831.9919460223426781.9533241281124303-1.02125482019497050.6659226341919031.00211836468348661.40545341157019091.28909194098003522.321158192129386-0.546905553229240210.79735367537081810.344030655861066071.19342137647035831.5534389109567422.31815155418013670.53069471529410041.6755540851223807-0.81702722659019680.81689145982100132.05896918757115040.60215977180000871.33743765361397232.0475785728927222.04593825562766531.86371729168483880.87790842515232571.1247129537682160.67720519439170431.84167471299614153.39096051546303321.07619958783723630.433554069535043161.0361419366840727-1.07497760069002931.24779219974854660.10284321556030130.86320516673865271.01828919134921941.75541398239813541.21526858096944351.841008794931391-0.4458100770443063-0.40197328150084390.89908180005108610.451757550813145060.85538049163061561.3540203321992380.96448697472185981.56573830606259512.545658804625557730.0257636662326845570.92965512289589761.30796885521603420.79150123689412022.033800732555499-1.40045363381229573.0306036208387996-0.142631289022763451.2118833867777011.70472062431710870.214564788236803031.46205973716204871.70422822546217431.52350796789380950.073745686469774093.00784295077800051.2269625418708954-0.152659109250952391.63197944580912951.039512686693366641.4643923250508961-2.56351666062473532.32110561547020571.15263055220453441.16452954293239850.56990430912351241.76736873575241151.9849198419098971.2708358488268042.39198619344640751.0798423130086290.6000354193034775-0.027850558681905780.415281788739211731.81659392654784170.91805294817333460.65523398574535571.528288145297394-0.068988783480132150.4881186908731849351.29120535974306351.56653369635357231.5035917591112031.2852956847818571.484288112749752.36348151242614570.2188947163746080.53198233366251452.2245743551261743-0.28110827514404261.8754755042743243-0.7107153240295290.54923489686372561.7491638059190650.79606713389874880.81782458833426591.6806560043814565-0.81849899039161421.0470716353257111.394844209327204460.75156794561915330.38229335200298330.317116003550666051.4362576043409168-0.70301277411323791.39371059913866510.52067599642450270.70098370703391951.69410328767876431.67862967370985651.23955599500389681.15122662929444971.8161272333600412.89353446759620071.63963276319370270.037971168094808605-1.08526564212010972.930246767465576-0.73534887447039272.210383704904514571.79743541942787340.6201892159526211.70256222400160030.1496537283448852.1768124501049290.4756638973675441.70090773091560471.98418807072241550.87827159133317983.3657686288400391.49614292624759471.79659486666495230.5259791098743150.94330428350907052.3577972581070580.19516627583329627-1.12362024909293010.66649755956675260.113280647514621971.334197930899008881.53678382490275030.25616963206379870.6797961177591210.083801138733459070.140331700018259341.22598548673375541.62877582653829431.18649434877307461.95247834510912191.98813758259330870.92739168603732450.449397076436580670.06184738590689576-0.239071562594792561.13968327403129480.7769810181175243.1236918885935531.122273434258829-0.40943173992276872.4229859527722439-1.1478550376436099-0.347532513457754931.36356455680664830.98524788819540512.27239507854621-0.4495666088633876-0.195523741666970840.4081370268527510.5854951564678105-0.425794733438021431.20939478753592390.4071139961610746-0.473116413465647370.103419384698128952.10435156986031260.56845048448395530.83886309175594111.88915749407287371.2883768477152655-0.051538937570038135100.68043860015976850.38000691227879781.1569983764466890.42854464892021072.0576331832046140.208511190522114530.475372654071529841.07187803937036862.9107594272771831.78796468313761441.5130821444669920.45358371972640612.0439446554953233.107785152793372.4599274815852942.01540545781252071.74918460092419760.32447852156331181.44026639166872971.6889718563269422110.72335387272301272.924533320015291.41120402070722381.8907649561731171.2263632272517069-1.07861789370217530.6121137680328240.9128930300360012.1263858221372731.24711171615773631.1211718053315411.29898393554204670.84290086315334830.2595309772192821-0.247652923051232771.24945521515244941.58107332062060653.76384407984956631.39932543697957621.6684881692058056120.72422627134369491.50048290015437221.8630648605906246-0.0516278594652122-0.392054077493086962.15392224061222542.1819441059992321.3913714484219310.11895290779843061.295079959786242.863801062132504-0.7122739244712557-0.40708454574322551.12678118632697171.0037598477954959-0.26899425607339180.161157078579710071.553921251439720.495957281918036340.21155981548212133132.52940067448393121.20545485392597971.31301312898086041.86652071216829941.29907052959437632.0765406438461221.36317734754445222.89367982555184260.51424725571719171.38767445577759841.02355761039255370.33977049795572010.68131507042198570.27733780632562731.1773867193845991.98351272934316421.02350458588539461.55377697786336591.35376929399663970.7244063662323177140.50951640852131860.6255633998072194-1.3975039761388162.54102998327793861.06308453895730510.7155593160737073-0.26560053760121072.787979632455750.64549129193062570.89523887693289571.38625368918905251.82277537376537340.316210177568992172.05720333121412931.03187997356903162.34318245752894150.94946031045122760.6359900436928911-0.55334233943106080.6807021981798389151.52704645095496421.7111123993648230.78245451950952383.6377912107038637-0.74213763059496540.90556519225855582.4311837522545641.59275844655066881.170296895326288-0.75170594779202451.28858132234320790.45742048309322071.17160238198599491.98281782924310470.97465109793723130.71244755071589191.9244428673433660.93875370013368540.26896665741479386-0.022773704695142838161.99599297024575241.9555579326274551.71384077119504321.1333709621941220.192961691527186560.65798921812078492.90877965809118641.15592326145312671.75965253209296260.57699377158075161.18167274532368171.27449257074640341.06791235738075720.96301718512408050.82919896930237761.26697323153986792.38299724885292540.97753912648857341.13139536722591521.4344371851022324171.26453406831699881.56565834427382461.58508427950172460.8257018472479126-0.071368671455089630.95153949744761680.154709610433756531.41510055838158081.42553059139785840.019275642499414160.57217389002797952.4985699551385370.6398434291309720.5417596106625071-0.337968401950970550.95861346038512811.8210479561079153.09780078077169162.28293324687452341.2703379257393719182.00314036204493152.07867355132040641.34075268512976420.80192504037643183.48145842287565352.3852548761892063-0.15460096538966894-0.26806905037708151.6078621862094797-0.080096483157590990.388718325204237771.1020351122923313-0.436573936772118641.21071705250565520.10309582930248773-0.72439307899183312.7923391966469424-0.31271270659223151.5558774263856250.3181186945088693192.57274274575580450.8953484251522342.8503978016099010.66664957325059951.19346423381987240.5032550621350912.032723215735861-0.73980379752255930.24413782437709120.118888267352064481.39389226291267530.0499744652037167341.33250662216224921.5289444088560467-0.120520993992724981.04826420692565671.0619884505217776-0.027515673217867410.7616646345326742.9321781695813467TopLeftRightBottom

Note that by default, Toyplot provides row and column indices for the matrix, along the top and left sides. As your matrix sizes grow, you may need to thin-out the indices to avoid overlap:

big_matrix = numpy.random.normal(loc=1, size=(50, 50))
toyplot.matrix((big_matrix, colormap), step=5, label="A matrix");
A 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Or, you may wish to leave off the indices altogether:

toyplot.matrix((big_matrix, colormap), tshow=False, lshow=False, label="A matrix");
A 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