1 Hello friends. Welcome to this spoken tutorial on Multiple plots. |
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2 |
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3 {{{ Show the slide containing the title }}} |
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4 |
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5 {{{ Show the slide containing the outline }}} |
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6 |
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7 In this tutorial, we will learn how to draw more than one plot, how to |
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8 add legends to each plot to indicate what each plot represents. We |
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9 will also learn how to switch between the plots and create multiple |
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10 plots with different regular axes which are also called as subplots. |
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11 |
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12 .. #[Nishanth]: See diff - edited a grammatical mistake |
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13 .. #[Madhu: Done] |
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14 |
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15 {{{ Shift to terminal and start ipython -pylab }}} |
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16 |
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17 To begin with let us start ipython with pylab, by typing:: |
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18 |
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19 ipython -pylab |
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20 |
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21 on the terminal |
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22 |
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23 Let us first create set of points for our plot. For this we will use |
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24 the command called linspace:: |
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25 |
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26 x = linspace(0, 50, 10) |
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27 |
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28 linspace command creates 10 points in the interval between 0 and 50 |
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29 both inclusive. We assign these values to a variable called x. |
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30 |
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31 .. #[Nishanth]: pre requisite for this LO is basic plotting which |
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32 covers linspace and plot. So you may not need to |
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33 specify all that again. But not a problem if it is |
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34 there also. |
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35 .. #[Madhu: Since I thought the LOs are disconnected, I thought it is |
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36 better to give a very short intro to it] |
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37 |
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38 Now let us draw a plot simple sine plot using these points:: |
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39 |
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40 plot(x, sin(x)) |
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41 |
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42 This should give us a nice sine plot. |
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43 |
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44 {{{ Switch to the plot window }}} |
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45 |
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46 Oh! wait! Is that a nice sine plot? Does a sine plot actually look |
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47 like that? We know that a sine plot is a smooth curve. Is it not? What |
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48 really caused this? |
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49 |
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50 .. #[Nishanth]: See diff |
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51 .. #[Madhu: Done] |
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52 |
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53 {{{ pause for a while }}} |
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54 |
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55 A small investigation on linspace tells us that we chose too few |
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56 points in a large interval between 0 and 50 for the curve to be |
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57 smooth. This should also indicate that the plot command actually plots |
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58 the set of points given by x and sin(x) and it doesn't plot the |
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59 analytical function itself i.e. it plots the points given by |
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60 Analytical functions. So now let us use linspace again to get 500 |
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61 points between 0 and 100 and draw the sine plot |
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62 |
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63 .. #[Nishanth]: Here specify that when we do plot(x, sin(x) |
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64 it is actually plotting two sets of points |
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65 and not analytical functions. Hence the sharp |
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66 curve. |
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67 .. #[Madhu: Incorporated] |
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68 |
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69 {{{ Switch to ipython andtype }}} :: |
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70 |
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71 y = linspace(0, 50, 500) |
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72 plot(y, sin(y)) |
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73 |
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74 {{{ Change to the plot window }}} |
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75 |
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76 Now we see what we remember as a sine plot. A smooth curve. If we |
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77 carefully notice we also have two plots now one overlaid upon |
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78 another. In pylab, by default all the plots are overlaid. |
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79 |
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80 Since we have two plots now overlaid upon each other we would like to |
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81 have a way to indicate what each plot represents to distinguish |
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82 between them. This is accomplished using legends. Equivalently, the |
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83 legend command does this for us |
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84 |
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85 {{{ Switch to ipython }}}:: |
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86 |
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87 legend(['sin(x)', 'cos(x)']) |
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88 |
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89 .. #[Nishanth]: This legend may go up in the script. May be before |
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90 introducing the figure command itself. |
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91 .. #[Madhu: brought up] |
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92 |
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93 The legend command takes a single list of parameters where each |
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94 parameter is the text indicating the plots in the order of their |
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95 serial number. |
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96 |
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97 {{{ Switch to plot window }}} |
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98 |
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99 Now we can see the legends being displayed for the respective sine and |
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100 cosine plots on the plot area. |
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101 |
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102 We have learnt quite a lot of things now, so let us take up an |
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103 exercise problem. |
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104 |
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105 %% 1 %% Draw two plots overlaid upon each other, with the first plot |
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106 being a parabola of the form y = 4(x ^ 2) and the second being a |
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107 straight line of the form y = 2x + 3 in the interval -5 to 5. Use |
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108 colors to differentiate between the plots and use legends to |
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109 indicate what each plot is doing. |
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110 |
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111 {{{ pause for a while and continue from paused state }}} |
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112 |
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113 We can obtain the two plots in different colors using the following |
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114 commands:: |
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115 |
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116 x = linspace(-5, 5, 100) |
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117 plot(x, 4 * (x * x), 'b') |
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118 plot(x, (2 * x) + 3, 'g') |
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119 |
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120 Now we can use the legend command as:: |
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121 |
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122 legend(['Parabola', 'Straight Line']) |
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123 |
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124 Or we can also just give the equations of the plot:: |
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125 |
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126 legend(['y = 4(x ^ 2)', 'y = 2x + 3']) |
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127 |
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128 We now know how to draw multiple plots and use legends to indicate |
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129 which plot represents what function, but we would like to have more |
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130 control over the plots we draw. Like switch between them, perform some |
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131 operations or labelling on them individually and so on. Let us see how |
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132 to accomplish this. Before we move on, let us clear our screen. |
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133 |
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134 {{{ Switch to ipython }}}:: |
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135 |
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136 clf() |
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137 |
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138 To accomplishing more control over individual plots we use the figure |
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139 command:: |
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140 |
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141 x = linspace(0, 50, 500) |
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142 figure(1) |
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143 plot(x, sin(x), 'b') |
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144 figure(2) |
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145 plot(x, cos(x), 'g') |
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146 |
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147 {{{ Switch to plot window }}} |
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148 |
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149 Now we have two plots, a sine plot and a cosine plot in two different |
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150 figures. |
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151 |
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152 .. #[Nishanth]: figure(1) and figure(2) give two different plots. |
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153 The remaining script moves on the fact that they |
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154 give overlaid plots which is not the case. |
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155 So clear the figure and plot cos and sin without |
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156 introducing figure command. Then introduce legend |
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157 and finish off the everything on legend. |
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158 Then introduce figure command. |
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159 |
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160 .. #[Madhu: I have just moved up the text about legend command. I |
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161 think that should take care of what you suggested. If there is |
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162 some mistake with it, Punch please let me know in your next |
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163 review.] |
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164 |
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165 {{{ Have both plot window and ipython side by side }}} |
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166 |
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167 The figure command takes an integer as an argument which is the serial |
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168 number of the plot. This selects the corresponding plot. All the plot |
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169 commands we run after this are applied to the selected plot. In this |
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170 example figure 1 is the sine plot and figure 2 is the cosine plot. We |
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171 can, for example, save each plot separately |
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172 |
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173 {{{ Switch to ipython }}}:: |
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174 |
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175 savefig('/home/user/cosine.png') |
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176 figure(1) |
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177 title('sin(y)') |
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178 savefig('/home/user/sine.png') |
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179 |
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180 {{{ Have both plot window and ipython side by side }}} |
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181 |
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182 We also titled the our first plot as 'sin(y)' which we did not do for |
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183 the second plot. |
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184 |
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185 Let us attempt another exercise problem |
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186 |
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187 %% 2 %% Draw a line of the form y = x as one figure and another line |
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188 of the form y = 2x + 3. Switch back to the first figure, annotate |
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189 the x and y intercepts. Now switch to the second figure and |
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190 annotate its x and y intercepts. Save each of them. |
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191 |
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192 {{{ Pause for a while and continue from the paused state }}} |
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193 |
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194 To solve this problem we should first create the first figure using |
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195 the figure command. Before that, let us first run clf command to make |
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196 sure all the previous plots are cleared:: |
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197 |
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198 clf() |
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199 figure(1) |
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200 x = linspace(-5, 5, 100) |
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201 plot(x, x) |
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202 |
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203 Now we can use figure command to create second plotting area and plot |
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204 the figure:: |
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205 |
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206 figure(2) |
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207 plot(x, ((2 * x) + 3)) |
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208 |
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209 Now to switch between the figures we can use figure command. So let us |
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210 switch to figure 1. We are asked to annotate x and y intercepts of the |
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211 figure 1 but since figure 1 passes through origin we will have to |
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212 annotate the origin. We will annotate the intercepts for the second |
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213 figure and save them as follows:: |
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214 |
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215 figure(1) |
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216 annotate('Origin', xy=(0.0, 0.0) |
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217 figure(2) |
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218 annotate('x-intercept', xy=(0, 3)) |
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219 annotate('y-intercept', xy=(0, -1.5)) |
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220 savefig('/home/fossee/plot2.png') |
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221 figure(1) |
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222 savefig('/home/fossee/plot1.png') |
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223 |
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224 At times we run into situations where we want to compare two plots and |
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225 in such cases we want to draw both the plots in the same plotting |
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226 area. The situation is such that the two plots have different regular |
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227 axes which means we cannot draw overlaid plots. In such cases we can |
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228 draw subplots. |
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229 |
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230 We use subplot command to accomplish this |
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231 |
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232 {{{ Switch to ipython }}}:: |
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233 |
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234 subplot(2, 1, 1) |
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235 |
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236 subplot command takes three arguments, the first being the number of |
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237 rows of subplots that must be created, |
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238 |
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239 {{{ Have both plot window and ipython side by side }}} |
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240 |
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241 in this case we have 2 so it spilts the plotting area horizontally for |
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242 two subplots. The second argument specifies the number of coloumns of |
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243 subplots that must be created. We passed 1 as the argument so the |
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244 plotting area won't be split vertically and the last argument |
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245 specifies what subplot must be created now in the order of the serial |
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246 number. In this case we passed 1 as the argument, so the first subplot |
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247 that is top half is created. If we execute the subplot command as |
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248 |
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249 {{{ Switch to ipython }}}:: |
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250 |
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251 subplot(2, 1, 2) |
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252 |
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253 {{{ Switch to plot window }}} |
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254 |
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255 The lower subplot is created. Now we can draw plots in each of the |
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256 subplot area using the plot command. |
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257 |
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258 {{{ Switch to ipython }}}:: |
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259 |
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260 x = linspace(0, 50, 500) |
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261 plot(x, cos(x)) |
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262 subplot(2, 1, 1) |
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263 y = linspace(0, 5, 100) |
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264 plot(y, y ** 2) |
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265 |
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266 {{{ Have both plot window and ipython side by side }}} |
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267 |
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268 This created two plots one in each of the subplot area. The top |
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269 subplot holds a parabola and the bottom subplot holds a cosine |
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270 curve. |
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271 |
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272 As seen here we can use subplot command to switch between the subplot |
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273 as well, but we have to use the same arguments as we used to create |
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274 that subplot, otherwise the previous subplot at that place will be |
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275 automatically erased. It is clear from the two subplots that both have |
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276 different regular axes. For the cosine plot x-axis varies from 0 to |
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277 100 and y-axis varies from 0 to 1 where as for the parabolic plot the |
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278 x-axis varies from 0 to 10 and y-axis varies from 0 to 100 |
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279 |
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280 .. #[Nishanth]: stress on the similarity between subplot and figure |
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281 commands |
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282 |
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283 .. #[Madhu: I think they are not really similar. Trying to bring in |
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284 the similarity will confuse people I think.] |
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285 |
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286 %% 3 %% We know that the Pressure, Volume and Temperatures are held by |
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287 the equation PV = nRT where nR is a constant. Let us assume nR = .01 |
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288 Joules/Kelvin and T = 200K. V can be in the range from 21cc to |
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289 100cc. Draw two different plots as subplots, one being the Pressure |
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290 versus Volume plot and the other being Pressure versus Temparature |
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291 plot. |
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292 |
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293 {{{ Pause for a while and continue }}} |
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294 |
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295 To start with, we have been given the range of Volume using which we |
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296 can define the variable V:: |
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297 |
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298 V = linspace(21, 100, 500) |
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299 |
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300 Now we can create first subplot and draw Pressure versus Volume graph |
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301 using this V. We know that nRT is a constant which is equal to 2.0 |
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302 since nR = 0.01 Joules/Kelvin and T = 200 Kelvin:: |
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303 |
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304 subplot(2, 1, 1) |
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305 plot(V, 2.0/V) |
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306 |
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307 Now we can create the second subplot and draw the Pressure versus |
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308 Temparature plot as follows:: |
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309 |
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310 subplot(2, 1, 2) |
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311 plot(200, 2.0/V) |
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312 |
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313 Unfortunately we have an error now, telling x and y dimensions don't |
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314 match. This is because our V contains a set of values as returned by |
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315 linspace and hence 2.0/V which is the pressure also contains a set of |
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316 values. But the first argument to the plot command is a single |
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317 value. So to plot this data we need to create as many points as there |
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318 are in Pressure or Volume data for Temperature too, all having the |
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319 same value. This can be accomplished using:: |
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320 |
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321 T = linspace(200, 200, 500) |
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322 |
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323 We now have 500 values in T each with the value 200 Kelvin. Plotting |
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324 this data we get the required plot:: |
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325 |
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326 plot(T, 2.0/V) |
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327 |
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328 It is left as a homework to label both X and Y axes for each of the |
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329 two subplots. |
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330 |
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331 {{{ Show summary slide }}} |
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332 |
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333 .. #[Nishanth]: Exercises are missing in the script |
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334 one exercise for overlaid plot and legend |
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335 one for figure command |
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336 one for subplot must do |
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337 |
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338 This brings us to the end of another session. In this tutorial session |
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339 we learnt |
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340 |
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341 * How to draw multiple plots which are overlaid |
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342 * the figure command |
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343 * the legend command |
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344 * how to switch between the plots and perform some operations on each |
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345 of them like saving the plots and |
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346 * creating and switching between subplots |
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347 |
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348 .. #[Nishanth]: legend command can be told right after overlaid plots |
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349 .. #[Madhu: Incorporated] |
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350 |
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351 {{{ Show the "sponsored by FOSSEE" slide }}} |
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352 |
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353 This tutorial was created as a part of FOSSEE project, NME ICT, MHRD India |
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354 |
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355 Hope you have enjoyed and found it useful. |
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356 Thankyou |
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357 |
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358 .. Author : Madhu |
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359 Internal Reviewer 1 : [potential reviewer: Puneeth] |
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360 Internal Reviewer 2 : Nishanth |
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361 External Reviewer : |
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362 |
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