I am plotting color for geopandas shape with 2 array data:
Here's my first array.
newI =
array([ -467, -415, -414, -1001, -246, -147, -523, -327, -583,
-541, -290, -415, -453, -505, -791, -812, -672, -558,
-559, -1055, -327, -703, -419, -499, -273, -574, -802,
-450, -743, -221, -1282, -704, -352, -734, -430, -353,
-515, -1121, -664, -586, -171, -881, -402, -1024, -543,
-527, -384, -775, -931, -1380, -1662, -1069, -952, -435,
-1051, -921, -1211, -794, -547, -313, -511, -993, -430,
-262, -255, -675, -793, -1053, -702, -967, -1016, -230,
-405, -869, -689, -935, -190, -1473, -883, -1233, -240,
-607, -339, -1130, -909, -836, -667, -457, -847, -538,
-606, -457, -800, -322, -1339, -691, -627, -689, -365,
-600, -289, -810, -577, -187, -375, -574, -426, -662,
-695, -1003, -40, -1012, -279, -966, -587, -641, -753,
-461, -563, -604, -1013, -625, -506, -416, -1385, -459,
-760, -347, -308, -555, -325, -1588, -566, -533, -843,
-501, -448, -1022, -654, -602, -1201, -814, -754, -361,
-325, -1141, -725, -256, -601, -379, -496, -1099, -1101,
-598, -442, -773, -295, -1292, -558, -1234, -868, -1135,
-251, -1398, -453, -563, -1306, -693, -560, -512, -935,
-1168, -482, -431, -1199, -1249, -1199, -413, -1018, -194,
-375, -932, -1028, -436, -955, -463, -1303, -676, -554,
-601, -875, -661, -791, -443, -89, -879, -606, -577,
-475, -802, -734, -660, -684, -174, -902, -1241, -1320,
-575, -855, -222, -890, -701, -1082, -531, -693, -1008,
-1357, -433, -379, -192, -343, -477, -230, -938, -675,
-798, -259, -398, -778, -484, -817, -453, -564, -536,
-1599, -968, -547, -845, -1592, -256, -1139, -229, -926,
-474, -392, -990, -295, -558, -465, -497, -395, -468,
-310, -507, -1205, -705, -739, -609, -809, -610, -421,
-1057, -2023, -1105, -618, -466, -1291, -616, -620, -571,
-904, -383, -544, -688, -461, -769, -990, -664, -405,
-419, -852, -435, -298, -782, -758, -371, -813, -421,
-594, -259, -284, -215, -452, -430, -936, -994, -981,
-502, -510, -671, -721, -829, -523, -288, -653, -493,
-983, -1205, -722])
and Here's my second array:
array([-2407, -1992, -3400, -4826, -1544, -820, -3120, -1469, -2869,
-3622, -1738, -2122, -2773, -2939, -3558, -3575, -3082, -2494,
-3591, -5022, -1619, -2608, -3371, -3054, -1596, -2538, -3566,
-2035, -3490, -522, -5362, -3055, -1517, -4107, -2039, -2497,
-2302, -5513, -3876, -4303, -831, -4457, -2027, -5083, -2716,
-2284, -1288, -3781, -4707, -6903, -8592, -5763, -4644, -1999,
-4894, -3190, -6263, -3484, -3090, -1899, -2640, -3940, -2919,
-629, -2018, -4228, -4075, -5249, -2794, -4061, -4089, -1500,
-2434, -3867, -3359, -4070, -1472, -7334, -4367, -5422, -1563,
-3092, -1803, -4664, -4096, -3875, -3061, -1181, -4098, -2850,
-4356, -2239, -3102, -1498, -6458, -3495, -2863, -3568, -1752,
-3422, -1768, -3675, -2061, -919, -1452, -2512, -1924, -3668,
-3931, -4348, -284, -6232, -1065, -4261, -2739, -3392, -3962,
-2369, -2508, -3156, -4759, -3012, -3345, -2566, -7910, -2215,
-3581, -1357, -2155, -2643, -1420, -7449, -3023, -2982, -4913,
-2835, -1748, -4679, -2950, -2951, -5515, -4195, -3568, -1746,
-1437, -5429, -3246, -1556, -2635, -1534, -3553, -4451, -5655,
-2616, -2724, -4445, -1642, -6640, -2869, -5211, -5014, -4909,
-1103, -5658, -2096, -2427, -5719, -3152, -2717, -2544, -4226,
-4813, -2319, -2261, -4844, -5383, -5057, -2981, -5448, -1526,
-1749, -3550, -3736, -1893, -5812, -2686, -5923, -3145, -3569,
-2523, -4586, -2931, -4104, -2301, -666, -4402, -3201, -3171,
-2598, -4279, -3765, -3024, -3085, -468, -3732, -5899, -6464,
-3993, -4583, -1126, -4193, -4214, -3902, -2132, -3712, -4879,
-6907, -1524, -1987, -1444, -2086, -3229, -1316, -4331, -3150,
-4449, -1700, -1486, -3650, -2478, -4166, -2618, -3308, -2458,
-7441, -4452, -2438, -4722, -6949, -1712, -4727, -792, -4193,
-1610, -1951, -3965, -1410, -2958, -2167, -2050, -2035, -2152,
-2236, -3235, -5999, -4024, -3111, -3196, -3881, -2647, -2579,
-6387, -9912, -4677, -2983, -1913, -7547, -3166, -2990, -2183,
-3401, -2080, -3056, -2225, -2546, -4421, -3867, -2975, -1552,
-2090, -3871, -1768, -2032, -3564, -3273, -1579, -4338, -1371,
-3600, -1253, -2083, -1439, -2281, -2045, -4406, -4380, -4129,
-2520, -2529, -2108, -3081, -3561, -2601, -843, -3069, -1852,
-5888, -5730, -3386])
The code to plot those array data is as shown below.
area_gpd = gpd.read_file("....shp")
area_gpd['population'] = newI
plt.rcParams.update({'font.size':32})
west,south,east,north = area.unary_union.bounds
fig,ax = plt.subplots(figsize=(40,40))
cmap = LinearSegmentedColormap.from_list('mycmap', [ 'green','white'])
melbourne_gpd.plot(ax=ax, column='population',legend=False,cmap=cmap,zorder=3)
sm = plt.cm.ScalarMappable(cmap=cmap,\
norm=plt.Normalize(vmin=-9912,
vmax=-284))
It keeps normalizing things so the intensity shows now different.
Is there any function to normalize this data?
I want the map to be darker for those with a larger value. Can anyone give me some recommendations?
Thanks so much
I found a nice solution for the question from a guy on stackoverflow:
import scipy as sp
import matplotlib as mpl
import matplotlib.pyplot as plt
class MidpointNormalize(mpl.colors.Normalize):
def __init__(self, vmin, vmax, midpoint=0, clip=False):
self.midpoint = midpoint
mpl.colors.Normalize.__init__(self, vmin, vmax, clip)
def __call__(self, value, clip=None):
normalized_min = max(0, 1 / 2 * (1 - abs((self.midpoint - self.vmin) / (self.midpoint - self.vmax))))
normalized_max = min(1, 1 / 2 * (1 + abs((self.vmax - self.midpoint) / (self.midpoint - self.vmin))))
normalized_mid = 0.5
x, y = [self.vmin, self.midpoint, self.vmax], [normalized_min, normalized_mid, normalized_max]
return sp.ma.masked_array(sp.interp(value, x, y))
vals = sp.array([[-5., 0], [5, 10]])
vmin = -1225
vmax = 669
norm = MidpointNormalize(vmin=vmin, vmax=vmax, midpoint=0)
It will nicely complete the job for your color map.
Here is the link to the place I found the solution : Colorplot that distinguishes between positive and negative values
Related
I'm using the following bit of code to plot two arrays of the same length -
import matplotlib.pyplot as plt
from scipy import stats
from scipy.stats import linregress
G_mag_values = [11.436, 11.341, 11.822, 11.646, 11.924, 12.057, 11.884, 11.805, 12.347, 12.662, 12.362, 12.555, 12.794, 12.819, 12.945, 12.733, 12.789, 12.878, 12.963, 13.094, 13.031, 12.962, 13.018, 12.906, 13.016, 13.088, 13.04, 13.035, 13.094, 13.032, 13.216, 13.062, 13.083, 13.126, 13.101, 13.089, 13.073, 13.182, 13.116, 13.145, 13.235, 13.161, 13.154, 13.383, 13.315, 13.429, 13.461, 13.287, 13.494, 13.459, 13.478, 13.534, 13.536, 13.536, 13.483, 13.544, 13.564, 13.544, 13.608, 13.655, 13.665, 13.668, 13.697, 13.649, 13.742, 13.756, 13.671, 13.701, 13.788, 13.723, 13.697, 13.713, 13.708, 13.765, 13.847, 13.992, 13.706, 13.79, 13.783, 13.844, 13.945, 13.928, 13.936, 13.956, 13.898, 14.059, 13.945, 14.039, 13.999, 14.087, 14.05, 14.083, 14.136, 14.124, 14.189, 14.149, 14.182, 14.281, 14.177, 14.297, 14.268, 14.454, 14.295]
G_cal_values = [-8.553610547563503, -8.085853602272588, -7.98491731861732, -7.852060056526794, -7.550944423333883, -7.569289687795749, -7.547088847268468, -7.544445036682168, -7.480698829329534, -7.184407435874912, -7.382606680295108, -7.2231275160942054, -7.093385973539046, -7.0473097125206685, -6.775012624594927, -6.814667514017907, -6.719898703328146, -6.741699011193633, -6.483121454948265, -6.320533066162438, -6.216044707275117, -6.037365656714626, -6.058593802250578, -6.0203190345840865, -6.036176430437363, -5.817887798572345, -5.838439347527171, -5.864922270102037, -5.755152671040021, -5.7709095683554725, -5.729226240967218, -5.606533007538604, -5.5817719334376905, -5.578993138005095, -5.62616747769538, -5.648413591916503, -5.611676700504294, -5.557722166623976, -5.5584623064502825, -5.425878164810264, -5.582204334985258, -5.529395790688368, -5.560750195967957, -5.433224654816512, -5.4751198268734385, -5.4592032005417215, -5.514591770369543, -5.580278698184566, -5.520695348050357, -5.501615700174841, -5.578645415877418, -5.692203332547151, -5.569497861450115, -5.335209902666812, -5.470963349023013, -5.44265375533589, -5.538541653702721, -5.355732832969871, -5.318164588926453, -5.376154615199398, -5.372133774215322, -5.361689907587619, -5.37608154921679, -5.412657572197508, -5.454613589602333, -5.339384591430104, -5.367511403407703, -5.258069473329993, -5.347580031901464, -4.9905279263992, -5.445096880253789, -5.192885553786512, -5.2983352094538505, -5.3930571447307365, -5.057910469318639, -5.32585105504838, -5.238649399637653, -5.122431894813153, -5.084559296025157, -5.139042420486851, -4.9919273140342915, -5.103619454431522, -5.017946144298159, -4.98136832081144, -5.084355565584671, -5.048634391386494, -4.887073481359435, -5.074683293771264, -5.050703776716202, -5.104772289705188, -4.9597601680524415, -4.971489935988787, -4.895283369236485, -4.9859511256778974, -4.840717539517584, -4.815665714699117, -4.937635861879118, -4.887219819695687, -4.813729758415283, -4.82667464608015, -4.865176481168528, -4.885105289124561, -4.887072278243732]
fig, ax = plt.subplots()
plt.scatter(G_mag_values,G_cal_values)
ax.minorticks_on()
ax.grid(which='major', linestyle='-', linewidth='0.5')
ax.grid(which='minor', linestyle='-', linewidth='0.5')
fig.set_size_inches(10,7)
best_fit_Y_G = []
slope_G, intercept_G, r_value_G, p_value_G, std_err_G = stats.linregress(G_mag_values,G_cal_values)
for value_G in G_mag_values:
best_fit_Y_G.append(intercept_G + slope_G*value_G)
plt.plot(G_mag_values, best_fit_Y_G, 'r', label = 'Best fit')
plt.title('M67 Calibration graph for G filter')
plt.xlabel('Real magnitude')
plt.ylabel('Measured magnitude')
plt.show()
curve_G = np.polyfit(G_mag_values,G_cal_values,1)
print('G filter polyfit line: slope {}; intercept = {}'.format(curve_G[0],curve_G[1]))
print('G filter linregress: slope {}; intercept = {}'.format(slope_G,intercept_G))
When I run this, it prints the values for slope and intercept from the best_fit_Y_G and curve_G, but it doesnt display the plot at all. Where am I going wrong?
I copy/pasted and run your code.
curve_G = np.polyfit(G_mag_values,G_cal_values,1)
That line gives me error. Then I imported numpy as np and problem solved.
output figure
I am attempting to make compressor and turbine maps colored by efficiency. I have achieved this, but the tricontourf I am attempting leads to level colors outside of where my data even exists. I need to make sure the contour ends at the bounds of my data. Is there a way to achieve this?
My code:
import numpy as np
import matplotlib.pyplot as plt
alphaMap = np.array([0.000, 90.000])
NcMap = np.array([0.300, 0.400, 0.500, 0.600, 0.700, 0.750, 0.800, 0.850, 0.900, 0.950, 1.000, 1.050, 1.100, 1.150])
RlineMap = np.array([1.000, 1.200, 1.400, 1.600, 1.800, 2.000, 2.200, 2.400, 2.600, 2.800, 3.000])
WCmap = np.array([[[17.907, 19.339, 20.749, 22.136, 23.498, 24.833, 26.141, 27.420, 28.669, 29.887, 31.011],
[24.951, 26.742, 28.485, 30.177, 31.815, 33.397, 34.921, 36.385, 37.788, 39.128, 40.405],
[32.682, 34.715, 36.662, 38.520, 40.286, 41.958, 43.533, 45.011, 46.390, 47.669, 48.848],
[40.927, 43.115, 45.168, 47.083, 48.858, 50.492, 51.983, 53.331, 54.539, 55.607, 56.537],
[49.850, 52.122, 54.195, 56.068, 57.741, 59.215, 60.494, 61.580, 62.479, 63.197, 63.739],
[54.798, 57.066, 59.099, 60.897, 62.463, 63.800, 64.913, 65.810, 66.497, 66.983, 67.278],
[60.051, 62.252, 64.185, 65.851, 67.255, 68.405, 69.307, 69.973, 70.413, 70.638, 70.675],
[65.313, 67.427, 69.262, 70.824, 72.118, 73.153, 73.938, 74.484, 74.803, 74.907, 74.907],
[70.995, 72.902, 74.542, 75.920, 77.043, 77.920, 78.560, 78.974, 79.174, 79.198, 79.198],
[77.441, 78.904, 80.155, 81.199, 82.042, 82.690, 83.151, 83.434, 83.545, 83.548, 83.548],
[84.344, 85.211, 85.952, 86.572, 87.074, 87.460, 87.735, 87.903, 87.967, 87.968, 87.968],
[89.305, 89.687, 90.025, 90.320, 90.572, 90.783, 90.953, 91.083, 91.174, 91.227, 91.243],
[93.626, 93.712, 93.793, 93.868, 93.939, 94.004, 94.064, 94.120, 94.170, 94.216, 94.257],
[95.978, 95.989, 96.000, 96.012, 96.022, 96.033, 96.044, 96.054, 96.064, 96.074, 96.084]],
[[17.907, 19.339, 20.749, 22.136, 23.498, 24.833, 26.141, 27.420, 28.669, 29.887, 31.011],
[24.951, 26.742, 28.485, 30.177, 31.815, 33.397, 34.921, 36.385, 37.788, 39.128, 40.405],
[32.682, 34.715, 36.662, 38.520, 40.286, 41.958, 43.533, 45.011, 46.390, 47.669, 48.848],
[40.927, 43.115, 45.168, 47.083, 48.858, 50.492, 51.983, 53.331, 54.539, 55.607, 56.537],
[49.850, 52.122, 54.195, 56.068, 57.741, 59.215, 60.494, 61.580, 62.479, 63.197, 63.739],
[54.798, 57.066, 59.099, 60.897, 62.463, 63.800, 64.913, 65.810, 66.497, 66.983, 67.278],
[60.051, 62.252, 64.185, 65.851, 67.255, 68.405, 69.307, 69.973, 70.413, 70.638, 70.675],
[65.313, 67.427, 69.262, 70.824, 72.118, 73.153, 73.938, 74.484, 74.803, 74.907, 74.907],
[70.995, 72.902, 74.542, 75.920, 77.043, 77.920, 78.560, 78.974, 79.174, 79.198, 79.198],
[77.441, 78.904, 80.155, 81.199, 82.042, 82.690, 83.151, 83.434, 83.545, 83.548, 83.548],
[84.344, 85.211, 85.952, 86.572, 87.074, 87.460, 87.735, 87.903, 87.967, 87.968, 87.968],
[89.305, 89.687, 90.025, 90.320, 90.572, 90.783, 90.953, 91.083, 91.174, 91.227, 91.243],
[93.626, 93.712, 93.793, 93.868, 93.939, 94.004, 94.064, 94.120, 94.170, 94.216, 94.257],
[96.084, 96.074, 96.064, 96.054, 96.044, 96.033, 96.022, 96.012, 96.000, 95.989, 95.978]]])
effMap = np.array([[[.8070, .8291, .8461, .8566, .8586, .8497, .8170, .7410, .6022, .3674, .0000],
[.8230, .8454, .8628, .8741, .8775, .8708, .8419, .7732, .6477, .4372, .0916],
[.8411, .8631, .8805, .8921, .8966, .8918, .8671, .8065, .6959, .5124, .2168],
[.8565, .8783, .8957, .9077, .9131, .9099, .8883, .8338, .7340, .5696, .3083],
[.8662, .8879, .9055, .9179, .9239, .9219, .9024, .8520, .7600, .6096, .3739],
[.8699, .8917, .9093, .9218, .9281, .9265, .9080, .8598, .7721, .6297, .4089],
[.8743, .8957, .9130, .9253, .9316, .9304, .9131, .8678, .7858, .6538, .4519],
[.8836, .9026, .9179, .9287, .9342, .9331, .9183, .8804, .8128, .7065, .5485],
[.8943, .9103, .9230, .9319, .9362, .9351, .9231, .8930, .8406, .7602, .6442],
[.9060, .9169, .9253, .9310, .9334, .9321, .9236, .9036, .8703, .8211, .7529],
[.9170, .9224, .9264, .9288, .9293, .9280, .9231, .9127, .8962, .8730, .8423],
[.9159, .9171, .9176, .9177, .9171, .9159, .9136, .9097, .9042, .8968, .8876],
[.9061, .9059, .9055, .9052, .9047, .9042, .9036, .9028, .9018, .9007, .8994],
[.8962, .8964, .8965, .8966, .8967, .8968, .8969, .8970, .8971, .8972, .8973]],
[[.8070, .8291, .8461, .8566, .8586, .8497, .8170, .7410, .6022, .3674, .0714],
[.8230, .8454, .8628, .8741, .8775, .8708, .8419, .7732, .6477, .4372, .0916],
[.8411, .8631, .8805, .8921, .8966, .8918, .8671, .8065, .6959, .5124, .2168],
[.8565, .8783, .8957, .9077, .9131, .9099, .8883, .8338, .7340, .5696, .3083],
[.8662, .8879, .9055, .9179, .9239, .9219, .9024, .8520, .7600, .6096, .3739],
[.8699, .8917, .9093, .9218, .9281, .9265, .9080, .8598, .7721, .6297, .4089],
[.8743, .8957, .9130, .9253, .9316, .9304, .9131, .8678, .7858, .6538, .4519],
[.8836, .9026, .9179, .9287, .9342, .9331, .9183, .8804, .8128, .7065, .5485],
[.8943, .9103, .9230, .9319, .9362, .9351, .9231, .8930, .8406, .7602, .6442],
[.9060, .9169, .9253, .9310, .9334, .9321, .9236, .9036, .8703, .8211, .7529],
[.9170, .9224, .9264, .9288, .9293, .9280, .9231, .9127, .8962, .8730, .8423],
[.9159, .9171, .9176, .9177, .9171, .9159, .9136, .9097, .9042, .8968, .8876],
[.9061, .9059, .9055, .9052, .9047, .9042, .9036, .9028, .9018, .9007, .8994],
[.8962, .8964, .8965, .8966, .8967, .8968, .8969, .8970, .8971, .8972, .8973]]])
PRmap = np.array([[[1.0678, 1.0649, 1.0613, 1.0571, 1.0522, 1.0468, 1.0402, 1.0322, 1.0227, 1.0117, 1.0000],
[1.1239, 1.1186, 1.1122, 1.1047, 1.0962, 1.0865, 1.0751, 1.0611, 1.0445, 1.0257, 1.0045],
[1.1994, 1.1910, 1.1809, 1.1691, 1.1558, 1.1409, 1.1233, 1.1020, 1.0771, 1.0488, 1.0173],
[1.2981, 1.2855, 1.2706, 1.2533, 1.2339, 1.2122, 1.1869, 1.1563, 1.1210, 1.0811, 1.0370],
[1.4289, 1.4111, 1.3899, 1.3655, 1.3380, 1.3076, 1.2720, 1.2295, 1.1804, 1.1254, 1.0654],
[1.5118, 1.4909, 1.4661, 1.4375, 1.4052, 1.3695, 1.3278, 1.2779, 1.2205, 1.1565, 1.0868],
[1.6070, 1.5827, 1.5538, 1.5205, 1.4831, 1.4417, 1.3934, 1.3358, 1.2697, 1.1962, 1.1165],
[1.7160, 1.6881, 1.6555, 1.6183, 1.5767, 1.5312, 1.4785, 1.4160, 1.3448, 1.2660, 1.1808],
[1.8402, 1.8086, 1.7724, 1.7318, 1.6869, 1.6381, 1.5824, 1.5170, 1.4430, 1.3615, 1.2736],
[1.9930, 1.9587, 1.9206, 1.8788, 1.8336, 1.7852, 1.7309, 1.6685, 1.5988, 1.5225, 1.4405],
[2.1593, 2.1257, 2.0899, 2.0518, 2.0117, 1.9695, 1.9235, 1.8724, 1.8163, 1.7557, 1.6909],
[2.2764, 2.2510, 2.2248, 2.1978, 2.1701, 2.1416, 2.1118, 2.0801, 2.0464, 2.0108, 1.9735],
[2.3771, 2.3664, 2.3557, 2.3448, 2.3339, 2.3229, 2.3118, 2.3004, 2.2887, 2.2768, 2.2646],
[2.4559, 2.4538, 2.4516, 2.4495, 2.4473, 2.4452, 2.443, 2.4409, 2.4387, 2.4365, 2.4343]],
[[1.0678, 1.0649, 1.0613, 1.0571, 1.0522, 1.0468, 1.0402, 1.0322, 1.0227, 1.0117, 1.0000],
[1.1239, 1.1186, 1.1122, 1.1047, 1.0962, 1.0865, 1.0751, 1.0611, 1.0445, 1.0257, 1.0045],
[1.1994, 1.1910, 1.1809, 1.1691, 1.1558, 1.1409, 1.1233, 1.1020, 1.0771, 1.0488, 1.0173],
[1.2981, 1.2855, 1.2706, 1.2533, 1.2339, 1.2122, 1.1869, 1.1563, 1.1210, 1.0811, 1.0370],
[1.4289, 1.4111, 1.3899, 1.3655, 1.3380, 1.3076, 1.2720, 1.2295, 1.1804, 1.1254, 1.0654],
[1.5118, 1.4909, 1.4661, 1.4375, 1.4052, 1.3695, 1.3278, 1.2779, 1.2205, 1.1565, 1.0868],
[1.6070, 1.5827, 1.5538, 1.5205, 1.4831, 1.4417, 1.3934, 1.3358, 1.2697, 1.1962, 1.1165],
[1.7160, 1.6881, 1.6555, 1.6183, 1.5767, 1.5312, 1.4785, 1.4160, 1.3448, 1.2660, 1.1808],
[1.8402, 1.8086, 1.7724, 1.7318, 1.6869, 1.6381, 1.5824, 1.5170, 1.4430, 1.3615, 1.2736],
[1.9930, 1.9587, 1.9206, 1.8788, 1.8336, 1.7852, 1.7309, 1.6685, 1.5988, 1.5225, 1.4405],
[2.1593, 2.1257, 2.0899, 2.0518, 2.0117, 1.9695, 1.9235, 1.8724, 1.8163, 1.7557, 1.6909],
[2.2764, 2.2510, 2.2248, 2.1978, 2.1701, 2.1416, 2.1118, 2.0801, 2.0464, 2.0108, 1.9735],
[2.3771, 2.3664, 2.3557, 2.3448, 2.3339, 2.3229, 2.3118, 2.3004, 2.2887, 2.2768, 2.2646],
[2.4343, 2.4365, 2.4387, 2.4409, 2.4430, 2.4452, 2.4473, 2.4495, 2.4516, 2.4538, 2.4559]]])
label = []
for x in NcMap:
label.append(x*100)
for i in range(0,14):
plt.annotate('{0}%'.format(round(label[i],2)),xy = ((flowmax[i],PRmax[i])), textcoords='offset points', xytext=(0,6), ha = 'center', color = 'b')
plt.xlim(0,1)
plt.ylim(1,8)
plt.ylabel(r'$PR_{off}$', fontsize=16)
plt.xlabel(r'$\.m_{c,off} [kg/s]$', fontsize=16)
x = WCmap[0,:14,:]
x = x.flatten().tolist()
y = PRmap[0,:14,:]
y = y.flatten().tolist()
z = effMap[0,:14,:]
z = z.flatten().tolist()
plt.tricontourf(x,y,z, cmap = 'jet')
cbar = plt.colorbar()
cbar.set_label(r'$\eta_{off}$', fontsize=16)
plt.show()
Compressor Map Plot
I'm working with PyGame and attempting to create a zoomable/scaleable Mandelbrot Set. I have this set up for square windows and coordinates that only from -1 to 1 on both axes in the complex plane. The way I do this is for every pixel on the screen I call this function:
#Import pygame and initialize
xSize = 50
ySize = 50
scale = 20
size = width, height = (xSize * scale), (ySize * scale)
screen = pygame.display.set_mode(size)
def getCoords(x, y):
complexX = (x/((xSize * scale)/2)) - 1
complexY = (y/((ySize * scale)/2)) - 1
return complexX, complexY
And here is the loop where I actually plot the pixels:
for y in range(0, (ySize * scale)):
for x in range(0, (xSize * scale)):
i = 0
z = getCoords(x, y)
complexNum = complex(z[0], z[1])
zOld = 0
blowsUp = False
#Check to see if (z^2 + c) "blows up"
if blowsUp:
screen.set_at((x, y), color1)
else:
screen.set_at((x, y), color0)
Essentially what I want to be able to do is to have two tuples (one for x and one for y) that contain the maximum and minimum values that get plotted from the complex plane (i.e. here I'm just plotting 1 to -1 on both the real and imaginary axes). I imagine that this would be done by editing the getCoords() function, but after much tinkering with the expression there I can't seem to find a way to do this properly.
I think your question is only marginally related to pygame programming, and is really mostly a math problem.
If I've understood what you're trying to do correctly, essentially it amounts to mapping an integer range of 0..scale to a specified subrange within ±1.0 in both the x and y dimensions. Visualize it as the transformation of the x and y coordinates in one rectangular area or box so they fit within the boundaries of another.
Here's code showing the essence of the math involved.
(Note that the code shown (largely) follows the PEP 8 - Style Guide for Python Code, which I strongly suggest you read and start following.)
scale = 2
size_x, size_y = 15, 15
subrange_x, subrange_y = (-.20, .20), (-.20, .20)
delta_x, delta_y = (subrange_x[1] - subrange_x[0]), (subrange_y[1] - subrange_y[0])
scale_x, scale_y = (size_x * scale), (size_y * scale)
def get_coords(x, y):
real = (x/scale_x * delta_x) + subrange_x[0]
imag = (y/scale_y * delta_y) + subrange_y[0]
return real, imag
for y in range(scale_y):
z_values = []
for x in range(scale_x):
z = get_coords(x, y)
complex_num = complex(z[0], z[1])
z_values.append(f'{complex_num:.2f}')
print(f'y={y:02}:', ' '.join(z_values))
Results printed:
y=00: -0.20-0.20j -0.19-0.20j -0.17-0.20j -0.16-0.20j -0.15-0.20j -0.13-0.20j -0.12-0.20j -0.11-0.20j -0.09-0.20j -0.08-0.20j -0.07-0.20j -0.05-0.20j -0.04-0.20j -0.03-0.20j -0.01-0.20j 0.00-0.20j 0.01-0.20j 0.03-0.20j 0.04-0.20j 0.05-0.20j 0.07-0.20j 0.08-0.20j 0.09-0.20j 0.11-0.20j 0.12-0.20j 0.13-0.20j 0.15-0.20j 0.16-0.20j 0.17-0.20j 0.19-0.20j
y=01: -0.20-0.19j -0.19-0.19j -0.17-0.19j -0.16-0.19j -0.15-0.19j -0.13-0.19j -0.12-0.19j -0.11-0.19j -0.09-0.19j -0.08-0.19j -0.07-0.19j -0.05-0.19j -0.04-0.19j -0.03-0.19j -0.01-0.19j 0.00-0.19j 0.01-0.19j 0.03-0.19j 0.04-0.19j 0.05-0.19j 0.07-0.19j 0.08-0.19j 0.09-0.19j 0.11-0.19j 0.12-0.19j 0.13-0.19j 0.15-0.19j 0.16-0.19j 0.17-0.19j 0.19-0.19j
y=02: -0.20-0.17j -0.19-0.17j -0.17-0.17j -0.16-0.17j -0.15-0.17j -0.13-0.17j -0.12-0.17j -0.11-0.17j -0.09-0.17j -0.08-0.17j -0.07-0.17j -0.05-0.17j -0.04-0.17j -0.03-0.17j -0.01-0.17j 0.00-0.17j 0.01-0.17j 0.03-0.17j 0.04-0.17j 0.05-0.17j 0.07-0.17j 0.08-0.17j 0.09-0.17j 0.11-0.17j 0.12-0.17j 0.13-0.17j 0.15-0.17j 0.16-0.17j 0.17-0.17j 0.19-0.17j
y=03: -0.20-0.16j -0.19-0.16j -0.17-0.16j -0.16-0.16j -0.15-0.16j -0.13-0.16j -0.12-0.16j -0.11-0.16j -0.09-0.16j -0.08-0.16j -0.07-0.16j -0.05-0.16j -0.04-0.16j -0.03-0.16j -0.01-0.16j 0.00-0.16j 0.01-0.16j 0.03-0.16j 0.04-0.16j 0.05-0.16j 0.07-0.16j 0.08-0.16j 0.09-0.16j 0.11-0.16j 0.12-0.16j 0.13-0.16j 0.15-0.16j 0.16-0.16j 0.17-0.16j 0.19-0.16j
y=04: -0.20-0.15j -0.19-0.15j -0.17-0.15j -0.16-0.15j -0.15-0.15j -0.13-0.15j -0.12-0.15j -0.11-0.15j -0.09-0.15j -0.08-0.15j -0.07-0.15j -0.05-0.15j -0.04-0.15j -0.03-0.15j -0.01-0.15j 0.00-0.15j 0.01-0.15j 0.03-0.15j 0.04-0.15j 0.05-0.15j 0.07-0.15j 0.08-0.15j 0.09-0.15j 0.11-0.15j 0.12-0.15j 0.13-0.15j 0.15-0.15j 0.16-0.15j 0.17-0.15j 0.19-0.15j
y=05: -0.20-0.13j -0.19-0.13j -0.17-0.13j -0.16-0.13j -0.15-0.13j -0.13-0.13j -0.12-0.13j -0.11-0.13j -0.09-0.13j -0.08-0.13j -0.07-0.13j -0.05-0.13j -0.04-0.13j -0.03-0.13j -0.01-0.13j 0.00-0.13j 0.01-0.13j 0.03-0.13j 0.04-0.13j 0.05-0.13j 0.07-0.13j 0.08-0.13j 0.09-0.13j 0.11-0.13j 0.12-0.13j 0.13-0.13j 0.15-0.13j 0.16-0.13j 0.17-0.13j 0.19-0.13j
y=06: -0.20-0.12j -0.19-0.12j -0.17-0.12j -0.16-0.12j -0.15-0.12j -0.13-0.12j -0.12-0.12j -0.11-0.12j -0.09-0.12j -0.08-0.12j -0.07-0.12j -0.05-0.12j -0.04-0.12j -0.03-0.12j -0.01-0.12j 0.00-0.12j 0.01-0.12j 0.03-0.12j 0.04-0.12j 0.05-0.12j 0.07-0.12j 0.08-0.12j 0.09-0.12j 0.11-0.12j 0.12-0.12j 0.13-0.12j 0.15-0.12j 0.16-0.12j 0.17-0.12j 0.19-0.12j
y=07: -0.20-0.11j -0.19-0.11j -0.17-0.11j -0.16-0.11j -0.15-0.11j -0.13-0.11j -0.12-0.11j -0.11-0.11j -0.09-0.11j -0.08-0.11j -0.07-0.11j -0.05-0.11j -0.04-0.11j -0.03-0.11j -0.01-0.11j 0.00-0.11j 0.01-0.11j 0.03-0.11j 0.04-0.11j 0.05-0.11j 0.07-0.11j 0.08-0.11j 0.09-0.11j 0.11-0.11j 0.12-0.11j 0.13-0.11j 0.15-0.11j 0.16-0.11j 0.17-0.11j 0.19-0.11j
y=08: -0.20-0.09j -0.19-0.09j -0.17-0.09j -0.16-0.09j -0.15-0.09j -0.13-0.09j -0.12-0.09j -0.11-0.09j -0.09-0.09j -0.08-0.09j -0.07-0.09j -0.05-0.09j -0.04-0.09j -0.03-0.09j -0.01-0.09j 0.00-0.09j 0.01-0.09j 0.03-0.09j 0.04-0.09j 0.05-0.09j 0.07-0.09j 0.08-0.09j 0.09-0.09j 0.11-0.09j 0.12-0.09j 0.13-0.09j 0.15-0.09j 0.16-0.09j 0.17-0.09j 0.19-0.09j
y=09: -0.20-0.08j -0.19-0.08j -0.17-0.08j -0.16-0.08j -0.15-0.08j -0.13-0.08j -0.12-0.08j -0.11-0.08j -0.09-0.08j -0.08-0.08j -0.07-0.08j -0.05-0.08j -0.04-0.08j -0.03-0.08j -0.01-0.08j 0.00-0.08j 0.01-0.08j 0.03-0.08j 0.04-0.08j 0.05-0.08j 0.07-0.08j 0.08-0.08j 0.09-0.08j 0.11-0.08j 0.12-0.08j 0.13-0.08j 0.15-0.08j 0.16-0.08j 0.17-0.08j 0.19-0.08j
y=10: -0.20-0.07j -0.19-0.07j -0.17-0.07j -0.16-0.07j -0.15-0.07j -0.13-0.07j -0.12-0.07j -0.11-0.07j -0.09-0.07j -0.08-0.07j -0.07-0.07j -0.05-0.07j -0.04-0.07j -0.03-0.07j -0.01-0.07j 0.00-0.07j 0.01-0.07j 0.03-0.07j 0.04-0.07j 0.05-0.07j 0.07-0.07j 0.08-0.07j 0.09-0.07j 0.11-0.07j 0.12-0.07j 0.13-0.07j 0.15-0.07j 0.16-0.07j 0.17-0.07j 0.19-0.07j
y=11: -0.20-0.05j -0.19-0.05j -0.17-0.05j -0.16-0.05j -0.15-0.05j -0.13-0.05j -0.12-0.05j -0.11-0.05j -0.09-0.05j -0.08-0.05j -0.07-0.05j -0.05-0.05j -0.04-0.05j -0.03-0.05j -0.01-0.05j 0.00-0.05j 0.01-0.05j 0.03-0.05j 0.04-0.05j 0.05-0.05j 0.07-0.05j 0.08-0.05j 0.09-0.05j 0.11-0.05j 0.12-0.05j 0.13-0.05j 0.15-0.05j 0.16-0.05j 0.17-0.05j 0.19-0.05j
y=12: -0.20-0.04j -0.19-0.04j -0.17-0.04j -0.16-0.04j -0.15-0.04j -0.13-0.04j -0.12-0.04j -0.11-0.04j -0.09-0.04j -0.08-0.04j -0.07-0.04j -0.05-0.04j -0.04-0.04j -0.03-0.04j -0.01-0.04j 0.00-0.04j 0.01-0.04j 0.03-0.04j 0.04-0.04j 0.05-0.04j 0.07-0.04j 0.08-0.04j 0.09-0.04j 0.11-0.04j 0.12-0.04j 0.13-0.04j 0.15-0.04j 0.16-0.04j 0.17-0.04j 0.19-0.04j
y=13: -0.20-0.03j -0.19-0.03j -0.17-0.03j -0.16-0.03j -0.15-0.03j -0.13-0.03j -0.12-0.03j -0.11-0.03j -0.09-0.03j -0.08-0.03j -0.07-0.03j -0.05-0.03j -0.04-0.03j -0.03-0.03j -0.01-0.03j 0.00-0.03j 0.01-0.03j 0.03-0.03j 0.04-0.03j 0.05-0.03j 0.07-0.03j 0.08-0.03j 0.09-0.03j 0.11-0.03j 0.12-0.03j 0.13-0.03j 0.15-0.03j 0.16-0.03j 0.17-0.03j 0.19-0.03j
y=14: -0.20-0.01j -0.19-0.01j -0.17-0.01j -0.16-0.01j -0.15-0.01j -0.13-0.01j -0.12-0.01j -0.11-0.01j -0.09-0.01j -0.08-0.01j -0.07-0.01j -0.05-0.01j -0.04-0.01j -0.03-0.01j -0.01-0.01j 0.00-0.01j 0.01-0.01j 0.03-0.01j 0.04-0.01j 0.05-0.01j 0.07-0.01j 0.08-0.01j 0.09-0.01j 0.11-0.01j 0.12-0.01j 0.13-0.01j 0.15-0.01j 0.16-0.01j 0.17-0.01j 0.19-0.01j
y=15: -0.20+0.00j -0.19+0.00j -0.17+0.00j -0.16+0.00j -0.15+0.00j -0.13+0.00j -0.12+0.00j -0.11+0.00j -0.09+0.00j -0.08+0.00j -0.07+0.00j -0.05+0.00j -0.04+0.00j -0.03+0.00j -0.01+0.00j 0.00+0.00j 0.01+0.00j 0.03+0.00j .04+0.00j 0.05+0.00j 0.07+0.00j 0.08+0.00j 0.09+0.00j 0.11+0.00j 0.12+0.00j 0.13+0.00j 0.15+0.00j 0.16+0.00j 0.17+0.00j 0.19+0.00j
y=16: -0.20+0.01j -0.19+0.01j -0.17+0.01j -0.16+0.01j -0.15+0.01j -0.13+0.01j -0.12+0.01j -0.11+0.01j -0.09+0.01j -0.08+0.01j -0.07+0.01j -0.05+0.01j -0.04+0.01j -0.03+0.01j -0.01+0.01j 0.00+0.01j 0.01+0.01j 0.03+0.01j 0.04+0.01j 0.05+0.01j 0.07+0.01j 0.08+0.01j 0.09+0.01j 0.11+0.01j 0.12+0.01j 0.13+0.01j 0.15+0.01j 0.16+0.01j 0.17+0.01j 0.19+0.01j
y=17: -0.20+0.03j -0.19+0.03j -0.17+0.03j -0.16+0.03j -0.15+0.03j -0.13+0.03j -0.12+0.03j -0.11+0.03j -0.09+0.03j -0.08+0.03j -0.07+0.03j -0.05+0.03j -0.04+0.03j -0.03+0.03j -0.01+0.03j 0.00+0.03j 0.01+0.03j 0.03+0.03j 0.04+0.03j 0.05+0.03j 0.07+0.03j 0.08+0.03j 0.09+0.03j 0.11+0.03j 0.12+0.03j 0.13+0.03j 0.15+0.03j 0.16+0.03j 0.17+0.03j 0.19+0.03j
y=18: -0.20+0.04j -0.19+0.04j -0.17+0.04j -0.16+0.04j -0.15+0.04j -0.13+0.04j -0.12+0.04j -0.11+0.04j -0.09+0.04j -0.08+0.04j -0.07+0.04j -0.05+0.04j -0.04+0.04j -0.03+0.04j -0.01+0.04j 0.00+0.04j 0.01+0.04j 0.03+0.04j 0.04+0.04j 0.05+0.04j 0.07+0.04j 0.08+0.04j 0.09+0.04j 0.11+0.04j 0.12+0.04j 0.13+0.04j 0.15+0.04j 0.16+0.04j 0.17+0.04j 0.19+0.04j
y=19: -0.20+0.05j -0.19+0.05j -0.17+0.05j -0.16+0.05j -0.15+0.05j -0.13+0.05j -0.12+0.05j -0.11+0.05j -0.09+0.05j -0.08+0.05j -0.07+0.05j -0.05+0.05j -0.04+0.05j -0.03+0.05j -0.01+0.05j 0.00+0.05j 0.01+0.05j 0.03+0.05j 0.04+0.05j 0.05+0.05j 0.07+0.05j 0.08+0.05j 0.09+0.05j 0.11+0.05j 0.12+0.05j 0.13+0.05j 0.15+0.05j 0.16+0.05j 0.17+0.05j 0.19+0.05j
y=20: -0.20+0.07j -0.19+0.07j -0.17+0.07j -0.16+0.07j -0.15+0.07j -0.13+0.07j -0.12+0.07j -0.11+0.07j -0.09+0.07j -0.08+0.07j -0.07+0.07j -0.05+0.07j -0.04+0.07j -0.03+0.07j -0.01+0.07j 0.00+0.07j 0.01+0.07j 0.03+0.07j 0.04+0.07j 0.05+0.07j 0.07+0.07j 0.08+0.07j 0.09+0.07j 0.11+0.07j 0.12+0.07j 0.13+0.07j 0.15+0.07j 0.16+0.07j 0.17+0.07j 0.19+0.07j
y=21: -0.20+0.08j -0.19+0.08j -0.17+0.08j -0.16+0.08j -0.15+0.08j -0.13+0.08j -0.12+0.08j -0.11+0.08j -0.09+0.08j -0.08+0.08j -0.07+0.08j -0.05+0.08j -0.04+0.08j -0.03+0.08j -0.01+0.08j 0.00+0.08j 0.01+0.08j 0.03+0.08j 0.04+0.08j 0.05+0.08j 0.07+0.08j 0.08+0.08j 0.09+0.08j 0.11+0.08j 0.12+0.08j 0.13+0.08j 0.15+0.08j 0.16+0.08j 0.17+0.08j 0.19+0.08j
y=22: -0.20+0.09j -0.19+0.09j -0.17+0.09j -0.16+0.09j -0.15+0.09j -0.13+0.09j -0.12+0.09j -0.11+0.09j -0.09+0.09j -0.08+0.09j -0.07+0.09j -0.05+0.09j -0.04+0.09j -0.03+0.09j -0.01+0.09j 0.00+0.09j 0.01+0.09j 0.03+0.09j 0.04+0.09j 0.05+0.09j 0.07+0.09j 0.08+0.09j 0.09+0.09j 0.11+0.09j 0.12+0.09j 0.13+0.09j 0.15+0.09j 0.16+0.09j 0.17+0.09j 0.19+0.09j
y=23: -0.20+0.11j -0.19+0.11j -0.17+0.11j -0.16+0.11j -0.15+0.11j -0.13+0.11j -0.12+0.11j -0.11+0.11j -0.09+0.11j -0.08+0.11j -0.07+0.11j -0.05+0.11j -0.04+0.11j -0.03+0.11j -0.01+0.11j 0.00+0.11j 0.01+0.11j 0.03+0.11j 0.04+0.11j 0.05+0.11j 0.07+0.11j 0.08+0.11j 0.09+0.11j 0.11+0.11j 0.12+0.11j 0.13+0.11j 0.15+0.11j 0.16+0.11j 0.17+0.11j 0.19+0.11j
y=24: -0.20+0.12j -0.19+0.12j -0.17+0.12j -0.16+0.12j -0.15+0.12j -0.13+0.12j -0.12+0.12j -0.11+0.12j -0.09+0.12j -0.08+0.12j -0.07+0.12j -0.05+0.12j -0.04+0.12j -0.03+0.12j -0.01+0.12j 0.00+0.12j 0.01+0.12j 0.03+0.12j 0.04+0.12j 0.05+0.12j 0.07+0.12j 0.08+0.12j 0.09+0.12j 0.11+0.12j 0.12+0.12j 0.13+0.12j 0.15+0.12j 0.16+0.12j 0.17+0.12j 0.19+0.12j
y=25: -0.20+0.13j -0.19+0.13j -0.17+0.13j -0.16+0.13j -0.15+0.13j -0.13+0.13j -0.12+0.13j -0.11+0.13j -0.09+0.13j -0.08+0.13j -0.07+0.13j -0.05+0.13j -0.04+0.13j -0.03+0.13j -0.01+0.13j 0.00+0.13j 0.01+0.13j 0.03+0.13j 0.04+0.13j 0.05+0.13j 0.07+0.13j 0.08+0.13j 0.09+0.13j 0.11+0.13j 0.12+0.13j 0.13+0.13j 0.15+0.13j 0.16+0.13j 0.17+0.13j 0.19+0.13j
y=26: -0.20+0.15j -0.19+0.15j -0.17+0.15j -0.16+0.15j -0.15+0.15j -0.13+0.15j -0.12+0.15j -0.11+0.15j -0.09+0.15j -0.08+0.15j -0.07+0.15j -0.05+0.15j -0.04+0.15j -0.03+0.15j -0.01+0.15j 0.00+0.15j 0.01+0.15j 0.03+0.15j 0.04+0.15j 0.05+0.15j 0.07+0.15j 0.08+0.15j 0.09+0.15j 0.11+0.15j 0.12+0.15j 0.13+0.15j 0.15+0.15j 0.16+0.15j 0.17+0.15j 0.19+0.15j
y=27: -0.20+0.16j -0.19+0.16j -0.17+0.16j -0.16+0.16j -0.15+0.16j -0.13+0.16j -0.12+0.16j -0.11+0.16j -0.09+0.16j -0.08+0.16j -0.07+0.16j -0.05+0.16j -0.04+0.16j -0.03+0.16j -0.01+0.16j 0.00+0.16j 0.01+0.16j 0.03+0.16j 0.04+0.16j 0.05+0.16j 0.07+0.16j 0.08+0.16j 0.09+0.16j 0.11+0.16j 0.12+0.16j 0.13+0.16j 0.15+0.16j 0.16+0.16j 0.17+0.16j 0.19+0.16j
y=28: -0.20+0.17j -0.19+0.17j -0.17+0.17j -0.16+0.17j -0.15+0.17j -0.13+0.17j -0.12+0.17j -0.11+0.17j -0.09+0.17j -0.08+0.17j -0.07+0.17j -0.05+0.17j -0.04+0.17j -0.03+0.17j -0.01+0.17j 0.00+0.17j 0.01+0.17j 0.03+0.17j 0.04+0.17j 0.05+0.17j 0.07+0.17j 0.08+0.17j 0.09+0.17j 0.11+0.17j 0.12+0.17j 0.13+0.17j 0.15+0.17j 0.16+0.17j 0.17+0.17j 0.19+0.17j
y=29: -0.20+0.19j -0.19+0.19j -0.17+0.19j -0.16+0.19j -0.15+0.19j -0.13+0.19j -0.12+0.19j -0.11+0.19j -0.09+0.19j -0.08+0.19j -0.07+0.19j -0.05+0.19j -0.04+0.19j -0.03+0.19j -0.01+0.19j 0.00+0.19j 0.01+0.19j 0.03+0.19j 0.04+0.19j 0.05+0.19j 0.07+0.19j 0.08+0.19j 0.09+0.19j 0.11+0.19j 0.12+0.19j 0.13+0.19j 0.15+0.19j 0.16+0.19j 0.17+0.19j 0.19+0.19j
How can I plot two or more overlapping Gaussian surfaces in the same graph, as below?
This is the code I have written, But the first surface is being covered by the second one. They are overlapping , But i want them to be displayed transparently
result obtained: https://i.stack.imgur.com/5LSsW.png
code :https://pastebin.com/embed_iframe/ms8cngXm
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def loaddata(filename,label):
file = open(filename, 'r')
text = file.read()
text=text.split('\n')
file.close()
dataset = list()
for line in text:
if len(line)>0:
value = line.split()
dataset.append([float(value[0]), float(value[1]), label])
return dataset
def multivariate_gaussian(pos, mu, Sigma):
n = mu.shape[0]
Sigma_det = np.linalg.det(Sigma)
Sigma_inv = np.linalg.inv(Sigma)
N = np.sqrt((2*np.pi)**n * Sigma_det)
fac = np.einsum('...k,kl,...l->...', pos-mu, Sigma_inv, pos-mu)
return np.exp(-fac / 2) / N
#this is just for maptype ignore this
_viridis_data = [[0.267004, 0.004874, 0.329415],
[0.268510, 0.009605, 0.335427],
[0.269944, 0.014625, 0.341379],
[0.271305, 0.019942, 0.347269],
[0.272594, 0.025563, 0.353093],
[0.273809, 0.031497, 0.358853],
[0.274952, 0.037752, 0.364543],
[0.276022, 0.044167, 0.370164],
[0.277018, 0.050344, 0.375715],
[0.277941, 0.056324, 0.381191],
[0.278791, 0.062145, 0.386592],
[0.279566, 0.067836, 0.391917],
[0.280267, 0.073417, 0.397163],
[0.280894, 0.078907, 0.402329],
[0.281446, 0.084320, 0.407414],
[0.281924, 0.089666, 0.412415],
[0.282327, 0.094955, 0.417331],
[0.282656, 0.100196, 0.422160],
[0.282910, 0.105393, 0.426902],
[0.283091, 0.110553, 0.431554],
[0.283197, 0.115680, 0.436115],
[0.283229, 0.120777, 0.440584],
[0.283187, 0.125848, 0.444960],
[0.283072, 0.130895, 0.449241],
[0.282884, 0.135920, 0.453427],
[0.282623, 0.140926, 0.457517],
[0.282290, 0.145912, 0.461510],
[0.281887, 0.150881, 0.465405],
[0.281412, 0.155834, 0.469201],
[0.280868, 0.160771, 0.472899],
[0.280255, 0.165693, 0.476498],
[0.279574, 0.170599, 0.479997],
[0.278826, 0.175490, 0.483397],
[0.278012, 0.180367, 0.486697],
[0.277134, 0.185228, 0.489898],
[0.276194, 0.190074, 0.493001],
[0.275191, 0.194905, 0.496005],
[0.274128, 0.199721, 0.498911],
[0.273006, 0.204520, 0.501721],
[0.271828, 0.209303, 0.504434],
[0.270595, 0.214069, 0.507052],
[0.269308, 0.218818, 0.509577],
[0.267968, 0.223549, 0.512008],
[0.266580, 0.228262, 0.514349],
[0.265145, 0.232956, 0.516599],
[0.263663, 0.237631, 0.518762],
[0.262138, 0.242286, 0.520837],
[0.260571, 0.246922, 0.522828],
[0.258965, 0.251537, 0.524736],
[0.257322, 0.256130, 0.526563],
[0.255645, 0.260703, 0.528312],
[0.253935, 0.265254, 0.529983],
[0.252194, 0.269783, 0.531579],
[0.250425, 0.274290, 0.533103],
[0.248629, 0.278775, 0.534556],
[0.246811, 0.283237, 0.535941],
[0.244972, 0.287675, 0.537260],
[0.243113, 0.292092, 0.538516],
[0.241237, 0.296485, 0.539709],
[0.239346, 0.300855, 0.540844],
[0.237441, 0.305202, 0.541921],
[0.235526, 0.309527, 0.542944],
[0.233603, 0.313828, 0.543914],
[0.231674, 0.318106, 0.544834],
[0.229739, 0.322361, 0.545706],
[0.227802, 0.326594, 0.546532],
[0.225863, 0.330805, 0.547314],
[0.223925, 0.334994, 0.548053],
[0.221989, 0.339161, 0.548752],
[0.220057, 0.343307, 0.549413],
[0.218130, 0.347432, 0.550038],
[0.216210, 0.351535, 0.550627],
[0.214298, 0.355619, 0.551184],
[0.212395, 0.359683, 0.551710],
[0.210503, 0.363727, 0.552206],
[0.208623, 0.367752, 0.552675],
[0.206756, 0.371758, 0.553117],
[0.204903, 0.375746, 0.553533],
[0.203063, 0.379716, 0.553925],
[0.201239, 0.383670, 0.554294],
[0.199430, 0.387607, 0.554642],
[0.197636, 0.391528, 0.554969],
[0.195860, 0.395433, 0.555276],
[0.194100, 0.399323, 0.555565],
[0.192357, 0.403199, 0.555836],
[0.190631, 0.407061, 0.556089],
[0.188923, 0.410910, 0.556326],
[0.187231, 0.414746, 0.556547],
[0.185556, 0.418570, 0.556753],
[0.183898, 0.422383, 0.556944],
[0.182256, 0.426184, 0.557120],
[0.180629, 0.429975, 0.557282],
[0.179019, 0.433756, 0.557430],
[0.177423, 0.437527, 0.557565],
[0.175841, 0.441290, 0.557685],
[0.174274, 0.445044, 0.557792],
[0.172719, 0.448791, 0.557885],
[0.171176, 0.452530, 0.557965],
[0.169646, 0.456262, 0.558030],
[0.168126, 0.459988, 0.558082],
[0.166617, 0.463708, 0.558119],
[0.165117, 0.467423, 0.558141],
[0.163625, 0.471133, 0.558148],
[0.162142, 0.474838, 0.558140],
[0.160665, 0.478540, 0.558115],
[0.159194, 0.482237, 0.558073],
[0.157729, 0.485932, 0.558013],
[0.156270, 0.489624, 0.557936],
[0.154815, 0.493313, 0.557840],
[0.153364, 0.497000, 0.557724],
[0.151918, 0.500685, 0.557587],
[0.150476, 0.504369, 0.557430],
[0.149039, 0.508051, 0.557250],
[0.147607, 0.511733, 0.557049],
[0.146180, 0.515413, 0.556823],
[0.144759, 0.519093, 0.556572],
[0.143343, 0.522773, 0.556295],
[0.141935, 0.526453, 0.555991],
[0.140536, 0.530132, 0.555659],
[0.139147, 0.533812, 0.555298],
[0.137770, 0.537492, 0.554906],
[0.136408, 0.541173, 0.554483],
[0.135066, 0.544853, 0.554029],
[0.133743, 0.548535, 0.553541],
[0.132444, 0.552216, 0.553018],
[0.131172, 0.555899, 0.552459],
[0.129933, 0.559582, 0.551864],
[0.128729, 0.563265, 0.551229],
[0.127568, 0.566949, 0.550556],
[0.126453, 0.570633, 0.549841],
[0.125394, 0.574318, 0.549086],
[0.124395, 0.578002, 0.548287],
[0.123463, 0.581687, 0.547445],
[0.122606, 0.585371, 0.546557],
[0.121831, 0.589055, 0.545623],
[0.121148, 0.592739, 0.544641],
[0.120565, 0.596422, 0.543611],
[0.120092, 0.600104, 0.542530],
[0.119738, 0.603785, 0.541400],
[0.119512, 0.607464, 0.540218],
[0.119423, 0.611141, 0.538982],
[0.119483, 0.614817, 0.537692],
[0.119699, 0.618490, 0.536347],
[0.120081, 0.622161, 0.534946],
[0.120638, 0.625828, 0.533488],
[0.121380, 0.629492, 0.531973],
[0.122312, 0.633153, 0.530398],
[0.123444, 0.636809, 0.528763],
[0.124780, 0.640461, 0.527068],
[0.126326, 0.644107, 0.525311],
[0.128087, 0.647749, 0.523491],
[0.130067, 0.651384, 0.521608],
[0.132268, 0.655014, 0.519661],
[0.134692, 0.658636, 0.517649],
[0.137339, 0.662252, 0.515571],
[0.140210, 0.665859, 0.513427],
[0.143303, 0.669459, 0.511215],
[0.146616, 0.673050, 0.508936],
[0.150148, 0.676631, 0.506589],
[0.153894, 0.680203, 0.504172],
[0.157851, 0.683765, 0.501686],
[0.162016, 0.687316, 0.499129],
[0.166383, 0.690856, 0.496502],
[0.170948, 0.694384, 0.493803],
[0.175707, 0.697900, 0.491033],
[0.180653, 0.701402, 0.488189],
[0.185783, 0.704891, 0.485273],
[0.191090, 0.708366, 0.482284],
[0.196571, 0.711827, 0.479221],
[0.202219, 0.715272, 0.476084],
[0.208030, 0.718701, 0.472873],
[0.214000, 0.722114, 0.469588],
[0.220124, 0.725509, 0.466226],
[0.226397, 0.728888, 0.462789],
[0.232815, 0.732247, 0.459277],
[0.239374, 0.735588, 0.455688],
[0.246070, 0.738910, 0.452024],
[0.252899, 0.742211, 0.448284],
[0.259857, 0.745492, 0.444467],
[0.266941, 0.748751, 0.440573],
[0.274149, 0.751988, 0.436601],
[0.281477, 0.755203, 0.432552],
[0.288921, 0.758394, 0.428426],
[0.296479, 0.761561, 0.424223],
[0.304148, 0.764704, 0.419943],
[0.311925, 0.767822, 0.415586],
[0.319809, 0.770914, 0.411152],
[0.327796, 0.773980, 0.406640],
[0.335885, 0.777018, 0.402049],
[0.344074, 0.780029, 0.397381],
[0.352360, 0.783011, 0.392636],
[0.360741, 0.785964, 0.387814],
[0.369214, 0.788888, 0.382914],
[0.377779, 0.791781, 0.377939],
[0.386433, 0.794644, 0.372886],
[0.395174, 0.797475, 0.367757],
[0.404001, 0.800275, 0.362552],
[0.412913, 0.803041, 0.357269],
[0.421908, 0.805774, 0.351910],
[0.430983, 0.808473, 0.346476],
[0.440137, 0.811138, 0.340967],
[0.449368, 0.813768, 0.335384],
[0.458674, 0.816363, 0.329727],
[0.468053, 0.818921, 0.323998],
[0.477504, 0.821444, 0.318195],
[0.487026, 0.823929, 0.312321],
[0.496615, 0.826376, 0.306377],
[0.506271, 0.828786, 0.300362],
[0.515992, 0.831158, 0.294279],
[0.525776, 0.833491, 0.288127],
[0.535621, 0.835785, 0.281908],
[0.545524, 0.838039, 0.275626],
[0.555484, 0.840254, 0.269281],
[0.565498, 0.842430, 0.262877],
[0.575563, 0.844566, 0.256415],
[0.585678, 0.846661, 0.249897],
[0.595839, 0.848717, 0.243329],
[0.606045, 0.850733, 0.236712],
[0.616293, 0.852709, 0.230052],
[0.626579, 0.854645, 0.223353],
[0.636902, 0.856542, 0.216620],
[0.647257, 0.858400, 0.209861],
[0.657642, 0.860219, 0.203082],
[0.668054, 0.861999, 0.196293],
[0.678489, 0.863742, 0.189503],
[0.688944, 0.865448, 0.182725],
[0.699415, 0.867117, 0.175971],
[0.709898, 0.868751, 0.169257],
[0.720391, 0.870350, 0.162603],
[0.730889, 0.871916, 0.156029],
[0.741388, 0.873449, 0.149561],
[0.751884, 0.874951, 0.143228],
[0.762373, 0.876424, 0.137064],
[0.772852, 0.877868, 0.131109],
[0.783315, 0.879285, 0.125405],
[0.793760, 0.880678, 0.120005],
[0.804182, 0.882046, 0.114965],
[0.814576, 0.883393, 0.110347],
[0.824940, 0.884720, 0.106217],
[0.835270, 0.886029, 0.102646],
[0.845561, 0.887322, 0.099702],
[0.855810, 0.888601, 0.097452],
[0.866013, 0.889868, 0.095953],
[0.876168, 0.891125, 0.095250],
[0.886271, 0.892374, 0.095374],
[0.896320, 0.893616, 0.096335],
[0.906311, 0.894855, 0.098125],
[0.916242, 0.896091, 0.100717],
[0.926106, 0.897330, 0.104071],
[0.935904, 0.898570, 0.108131],
[0.945636, 0.899815, 0.112838],
[0.955300, 0.901065, 0.118128],
[0.964894, 0.902323, 0.123941],
[0.974417, 0.903590, 0.130215],
[0.983868, 0.904867, 0.136897],
[0.993248, 0.906157, 0.143936]]
from matplotlib.colors import ListedColormap
viridis = ListedColormap(_viridis_data, name='viridis')
plt.register_cmap(name='viridis', cmap=viridis)
plt.set_cmap(viridis)
filename=r"C:/Users/santhoskumar/Desktop/random/pattern/class1_rw.txt"
label=0
dataset1= loaddata(filename,label)
print('Loaded data file {0} with {1} rows'.format(filename, len(dataset1)))
filename = r"C:/Users/santhoskumar/Desktop/random/pattern/class2_rw.txt"
label=1
dataset2 = loaddata(filename,label)
print('Loaded data file {0} with {1} rows'.format(filename, len(dataset2)))
filename = r'C:/Users/santhoskumar/Desktop/random/pattern/class3_rw.txt'
label=2
dataset3 = loaddata(filename,label)
print('Loaded data file {0} with {1} rows'.format(filename, len(dataset3)))
N = 600
X = np.linspace(200, 800, N)
Y = np.linspace(300, 1200, N)
X, Y = np.meshgrid(X, Y)
dataset=np.array(dataset1)
x,y,label=dataset.T
dat=x,y
dat=np.array(dat)
cov=np.cov(dat)
mu=np.mean(dat,axis=1)
print(mu)
# Pack X and Y into a single 3-dimensional array
pos = np.empty(X.shape + (2,))
pos[:, :, 0] = X
pos[:, :, 1] = Y
# The distribution on the variables X, Y packed into pos.
Z = multivariate_gaussian(pos, mu, cov)
minn=1e-15
for i in range(len(Z)):
for j in range(len(Z[i])):
Z[i][j]*=1e4
fig = plt.figure()
ax = fig.gca(projection='3d')
ax1=fig.gca(projection='3d')
ax.plot_surface(X, Y, Z,rstride=30,cstride=30, linewidth=1,antialiased=True,cmap=viridis)
cset = ax.contourf(X, Y, Z,zdir='z',offset=-0.4,cmap=viridis)
ax.set_zlim(-0.4,0.40)
ax.set_zticks(np.linspace(0,0.40,5))
ax.view_init(27, -21)
dataset=np.array(dataset2)
x,y,label=dataset.T
dat=x,y
dat=np.array(dat)
cov=np.cov(dat)
mu=np.mean(dat,axis=1)
print(mu)
# Pack X and Y into a single 3-dimensional array
pos = np.empty(X.shape + (2,))
pos[:, :, 0] = X
pos[:, :, 1] = Y
# The distribution on the variables X, Y packed into pos.
Z = multivariate_gaussian(pos, mu, cov)
minn=1e-15
for i in range(len(Z)):
for j in range(len(Z[i])):
Z[i][j]*=1e4
ax.plot_surface(X, Y, Z,rstride=20,cstride=20, linewidth=1,antialiased=True,color='red',cmap=viridis)
cset1 = ax.contourf(X, Y, Z,zdir='z',offset=-0.4,cmap=viridis)
ax.set_zlim(-0.4,0.40)
ax.set_zticks(np.linspace(0,0.40,5))
ax.view_init(27, -21)
plt.subplots_adjust(hspace=0.5)
plt.show()
If I understand your question correctly, you just have to call the plotting method multiple times such as:
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_surface(x1, y1, z1,cmap='viridis',linewidth=0)
ax.plot_surface(x2, y2, z2,cmap='viridis',linewidth=0)
ax.set_xlabel('X axis')
ax.set_ylabel('Y axis')
ax.set_zlabel('Z axis')
plt.show()
I'm trying to fit a gaussian to this data
x = [4170.177259096838, 4170.377258006199, 4170.577256915561, 4170.777255824922, 4170.977254734283, 4171.177253643645, 4171.377252553006, 4171.577251462368, 4171.777250371729, 4171.977249281091, 4172.177248190453, 4172.377247099814, 4172.577246009175, 4172.777244918537, 4172.977243827898, 4173.17724273726, 4173.377241646621, 4173.577240555983, 4173.777239465344, 4173.977238374706, 4174.177237284067, 4174.377236193429, 4174.57723510279, 4174.777234012152, 4174.977232921513, 4175.177231830875, 4175.377230740236, 4175.577229649598, 4175.777228558959, 4175.977227468321, 4176.177226377682, 4176.377225287044, 4176.577224196405, 4176.777223105767, 4176.977222015128, 4177.17722092449, 4177.377219833851, 4177.577218743213, 4177.777217652574, 4177.977216561936, 4178.177215471297, 4178.377214380659, 4178.57721329002, 4178.777212199382, 4178.977211108743, 4179.177210018105, 4179.377208927466, 4179.577207836828, 4179.777206746189, 4179.977205655551, 4180.177204564912, 4180.377203474274, 4180.577202383635, 4180.777201292997, 4180.977200202357, 4181.17719911172, 4181.377198021081, 4181.577196930443, 4181.777195839804, 4181.977194749166, 4182.177193658527, 4182.377192567888, 4182.5771914772495, 4182.777190386612, 4182.9771892959725, 4183.177188205335, 4183.377187114696, 4183.577186024058, 4183.777184933419, 4183.9771838427805, 4184.177182752143, 4184.3771816615035, 4184.5771805708655, 4184.777179480228, 4184.977178389589, 4185.1771772989505, 4185.3771762083115, 4185.5771751176735, 4185.777174027035, 4185.977172936397, 4186.1771718457585, 4186.3771707551205, 4186.5771696644815, 4186.777168573843, 4186.977167483204, 4187.177166392566, 4187.377165301927, 4187.577164211289, 4187.77716312065, 4187.977162030013, 4188.177160939374, 4188.377159848735, 4188.577158758096, 4188.777157667458, 4188.977156576819, 4189.177155486181, 4189.377154395542, 4189.577153304904, 4189.777152214265, 4189.977151123627, 4190.177150032989, 4190.37714894235, 4190.577147851711, 4190.777146761073, 4190.977145670434, 4191.177144579796, 4191.377143489157, 4191.577142398519, 4191.77714130788, 4191.977140217242, 4192.177139126603, 4192.377138035965, 4192.577136945326, 4192.777135854688, 4192.977134764049, 4193.177133673411, 4193.377132582772, 4193.577131492134, 4193.777130401495, 4193.977129310857, 4194.177128220218, 4194.377127129579, 4194.577126038941, 4194.777124948303, 4194.977123857664, 4195.177122767026, 4195.377121676387, 4195.577120585749, 4195.77711949511, 4195.977118404472, 4196.177117313833, 4196.377116223195, 4196.577115132556, 4196.777114041918, 4196.977112951279, 4197.177111860641, 4197.377110770002, 4197.577109679364, 4197.777108588725, 4197.977107498087, 4198.177106407448, 4198.37710531681, 4198.577104226171, 4198.777103135533, 4198.977102044893, 4199.177100954256, 4199.377099863617, 4199.577098772979, 4199.77709768234, 4199.977096591702, 4200.177095501063, 4200.377094410424, 4200.5770933197855, 4200.777092229148, 4200.9770911385085, 4201.177090047871, 4201.377088957232, 4201.577087866594, 4201.7770867759555, 4201.9770856853165, 4202.177084594679, 4202.377083504041, 4202.5770824134015, 4202.777081322764, 4202.977080232125, 4203.1770791414865, 4203.377078050848, 4203.5770769602095, 4203.777075869571, 4203.9770747789335, 4204.1770736882945, 4204.3770725976565, 4204.5770715070175, 4204.777070416379, 4204.97706932574, 4205.177068235102, 4205.377067144463, 4205.577066053825, 4205.777064963186, 4205.977063872549, 4206.17706278191, 4206.377061691271, 4206.577060600632, 4206.777059509994, 4206.977058419355, 4207.177057328717, 4207.377056238078, 4207.57705514744, 4207.777054056801, 4207.977052966163, 4208.177051875525, 4208.377050784886, 4208.577049694247, 4208.777048603609, 4208.977047512971, 4209.177046422332, 4209.377045331693, 4209.577044241055, 4209.777043150416, 4209.977042059778, 4210.177040969139, 4210.377039878501, 4210.577038787862, 4210.777037697224, 4210.977036606585, 4211.177035515947, 4211.377034425308, 4211.57703333467, 4211.777032244031, 4211.977031153393, 4212.177030062754, 4212.377028972116, 4212.577027881477, 4212.777026790839, 4212.9770257002, 4213.177024609562, 4213.377023518923, 4213.577022428285, 4213.777021337646, 4213.977020247008, 4214.177019156369, 4214.377018065731, 4214.577016975092, 4214.777015884454, 4214.977014793814, 4215.177013703177, 4215.377012612538, 4215.5770115219, 4215.777010431261, 4215.977009340623, 4216.177008249984, 4216.377007159345, 4216.577006068707, 4216.777004978069, 4216.977003887429, 4217.177002796792, 4217.377001706153, 4217.577000615515, 4217.776999524876, 4217.976998434238, 4218.176997343599, 4218.37699625296, 4218.5769951623215, 4218.776994071684, 4218.9769929810445, 4219.176991890407, 4219.376990799769, 4219.5769897091295, 4219.7769886184915, 4219.9769875278525, 4220.176986437215, 4220.376985346577, 4220.5769842559375, 4220.7769831653, 4220.9769820746615, 4221.1769809840225, 4221.376979893384, 4221.5769788027455, 4221.776977712107, 4221.9769766214695, 4222.17697553083, 4222.3769744401925, 4222.576973349554, 4222.776972258915, 4222.976971168276, 4223.176970077638, 4223.376968986999, 4223.576967896361, 4223.776966805722, 4223.976965715085, 4224.176964624445, 4224.376963533807, 4224.576962443168, 4224.77696135253, 4224.976960261891, 4225.176959171253, 4225.376958080614, 4225.576956989976, 4225.776955899337, 4225.976954808699, 4226.17695371806, 4226.376952627422, 4226.576951536783, 4226.776950446145, 4226.976949355506, 4227.176948264868, 4227.376947174229, 4227.576946083591, 4227.776944992952, 4227.976943902314, 4228.176942811675, 4228.376941721037, 4228.576940630398, 4228.776939539759, 4228.976938449121, 4229.176937358483, 4229.376936267844, 4229.576935177205, 4229.776934086567, 4229.976932995929]
y = [1.0063203573226929, 0.9789621233940125, 0.9998905658721924, 0.9947001934051514, 1.023498773574829, 1.0001505613327026, 0.9659610986709596, 1.0141736268997192, 0.9910064339637756, 0.961456060409546, 0.9808377623558044, 0.9717124700546264, 1.0020164251327517, 0.9276596307754515, 1.0044682025909424, 0.9898168444633484, 1.0139398574829102, 1.016809344291687, 0.9985541105270386, 1.0404949188232422, 1.0104306936264038, 1.0101377964019775, 1.0228283405303955, 1.014385461807251, 0.9949180483818054, 0.9398794174194336, 1.0047662258148191, 1.0185784101486206, 0.9942153096199036, 1.0496678352355957, 0.929694890975952, 1.0259612798690796, 1.0174839496612549, 0.9557819366455078, 1.009858012199402, 1.0258405208587646, 1.0318727493286133, 0.9781686067581176, 0.9566296339035034, 0.9626089930534364, 1.040783166885376, 0.9469046592712402, 0.9732370972633362, 1.0082777738571167, 1.0438332557678225, 1.067220687866211, 1.0809389352798462, 1.0122139453887942, 0.995375156402588, 1.025692343711853, 1.0900095701217651, 1.0033329725265503, 0.9947514533996582, 0.9366152882575988, 1.0340673923492432, 1.0574461221694946, 0.9984419345855712, 0.9406535029411316, 1.0367794036865234, 1.0252420902252195, 0.9390246868133544, 1.057265043258667, 1.0652446746826172, 1.0001699924468994, 1.0561981201171875, 0.9452269077301024, 1.0119216442108154, 1.000349760055542, 0.9879921674728394, 0.9834288954734802, 0.976799249649048, 0.9408118724822998, 1.0574927330017092, 1.0466219186782837, 0.97526878118515, 0.9811903238296508, 0.9985196590423584, 0.9862677454948424, 0.964194357395172, 1.0116554498672483, 0.9122620820999146, 0.9972245693206788, 0.9447768926620485, 1.0320085287094116, 1.0034307241439822, 0.965615689754486, 1.0228805541992188, 0.9555847048759459, 1.00389301776886, 0.9856386780738832, 0.9894683361053468, 1.0711736679077148, 0.990192711353302, 1.016653060913086, 1.0263935327529907, 0.9454292058944702, 0.9236765503883362, 0.9511216878890992, 0.9773555994033812, 0.9222095608711244, 0.9599731564521791, 1.0067923069000244, 1.0022263526916504, 0.9766445159912108, 1.0026237964630127, 1.010635256767273, 0.9901092052459716, 0.9869268536567688, 1.0354781150817869, 0.9797658920288086, 0.9543874263763428, 0.9747632145881652, 0.9942164421081544, 1.008299469947815, 0.9546594023704528, 1.0318409204483032, 1.0383642911911009, 1.0332415103912354, 1.0234425067901611, 1.0186198949813845, 1.0179851055145264, 1.0760197639465332, 0.9456835985183716, 1.0079874992370603, 0.9838529229164124, 0.8951097726821899, 0.9530791640281676, 0.9732348322868348, 0.9659185409545898, 1.0089071989059448, 0.963958203792572, 1.0035384893417358, 0.9776629805564879, 0.964256465435028, 0.9468261599540709, 1.0145124197006226, 1.0375784635543823, 0.992344319820404, 0.9584225416183472, 1.0427420139312744, 0.9997742176055908, 0.9584409594535828, 1.0051720142364502, 0.9606672525405884, 0.9797580242156982, 0.9900978207588196, 0.943138301372528, 0.9368865489959716, 0.9272330403327942, 0.9655094146728516, 0.9074565172195436, 0.97406405210495, 0.8742623329162598, 0.9219859838485718, 0.9126378297805786, 0.8354664444923401, 0.9138413667678832, 0.9268960952758788, 0.8841327428817749, 0.9733222126960754, 0.8825243711471558, 0.9243521094322203, 0.9403685927391052, 0.8782523870468141, 0.9003781080245972, 0.8850597143173218, 0.9231640696525574, 0.931676983833313, 0.8601804971694946, 0.8312444686889648, 0.9361259937286376, 0.9289224147796632, 0.8919285535812378, 0.8838070034980774, 0.9187015891075134, 0.9484543204307556, 0.8572731018066406, 0.8458079099655151, 0.92625629901886, 0.9748064875602722, 0.9674397706985474, 0.9326313138008118, 0.9933922290802002, 1.0025516748428345, 0.9956294894218444, 0.8995802998542786, 0.9598655700683594, 1.0185420513153076, 0.9935647249221802, 0.9689980745315552, 0.9919951558113098, 1.0028616189956665, 1.0252325534820557, 1.0221387147903442, 1.009528875350952, 1.0272767543792725, 0.9865442514419556, 0.9821861386299132, 0.95982563495636, 0.9557262063026428, 0.9864148497581482, 1.0166704654693604, 1.0599093437194824, 1.0000406503677368, 0.9622656106948853, 1.0044697523117063, 1.0404677391052246, 1.0023702383041382, 0.9803014993667604, 1.0197279453277588, 0.9902933835983276, 0.998839259147644, 0.966608464717865, 1.0340296030044556, 0.9632315635681152, 0.9758646488189696, 0.9757773876190186, 0.9818265438079834, 1.0110433101654053, 1.0131133794784546, 1.0256367921829224, 1.0690158605575562, 0.9764784574508668, 0.9947471022605896, 0.9979920387268066, 0.9850373864173888, 0.9165602922439576, 0.9634824395179749, 1.052489995956421, 0.9370544552803041, 1.0348092317581177, 1.0473220348358154, 0.9566289782524108, 0.9579214453697203, 0.972671627998352, 0.9536439180374146, 0.9755330085754396, 0.9753606915473938, 0.9924075603485109, 0.9893715381622314, 0.9780346751213074, 1.0207450389862058, 0.9914312362670898, 0.9940584301948548, 1.0417673587799072, 0.977041721343994, 1.0113568305969238, 1.030456304550171, 1.0540854930877686, 0.9963837265968324, 1.002269268035889, 0.9528346061706544, 0.9132148027420044, 1.0386162996292114, 0.9384365677833556, 1.0175614356994631, 1.0362330675125122, 0.9502999186515808, 1.0015273094177246, 0.987025022506714, 0.9869014024734496, 0.9577396512031556, 0.9633736610412598, 1.0747206211090088, 1.1858476400375366, 0.9917531609535216, 1.0963184833526611, 0.9528627991676332, 0.9999563694000244, 1.0115929841995241, 1.0094747543334959, 0.9977090358734132, 0.9800350666046144, 1.0336441993713381, 1.0021690130233765, 0.9629588127136229, 0.9191407561302184, 0.9930744767189026, 1.0318671464920044, 0.975939691066742, 0.9548277258872986, 1.0113637447357178, 0.9920935630798341, 0.9777255654335022, 0.9780721664428712, 0.9507009387016296, 0.9387223720550536, 1.0220414400100708, 1.019809007644653, 0.9822806715965272, 1.0380866527557373, 1.0477066040039062, 1.0222935676574707, 1.0258997678756714, 1.027082443237305, 1.0487046241760254, 0.9292799830436708, 0.999277114868164, 1.044923186302185, 1.0261610746383667]
e = [3.865531107294373e-05, 3.866014958475717e-05, 3.866496626869776e-05, 3.8669764762744314e-05, 3.867453415296041e-05, 3.8679270801367245e-05, 3.8683978345943615e-05, 3.868864223477431e-05, 3.8693269743816934e-05, 3.8697849959135056e-05, 3.870237924274989e-05, 3.8706857594661415e-05, 3.871127773891203e-05, 3.871564331348054e-05, 3.871994340443053e-05, 3.872417437378317e-05, 3.8728336221538484e-05, 3.8732425309717655e-05, 3.8736438000341884e-05, 3.874037065543234e-05, 3.8744219637010247e-05, 3.874798130709678e-05, 3.8751652027713135e-05, 3.875523543683812e-05, 3.8758716982556514e-05, 3.876210394082591e-05, 3.8765389035688706e-05, 3.8768568629166105e-05, 3.87716390832793e-05, 3.877460039802827e-05, 3.877745257341303e-05, 3.878018469549716e-05, 3.8782800402259454e-05, 3.878529605572112e-05, 3.8787664379924536e-05, 3.878991265082732e-05, 3.8792029954493046e-05, 3.8794016290921725e-05, 3.879586802213453e-05, 3.8797588786110275e-05, 3.879916766891256e-05, 3.8800608308520175e-05, 3.88019070669543e-05, 3.880306030623615e-05, 3.880407166434452e-05, 3.8804930227342986e-05, 3.8805643271189176e-05, 3.880619988194667e-05, 3.880660733557306e-05, 3.8806854718131945e-05, 3.8806945667602115e-05, 3.88068801839836e-05, 3.880665099131875e-05, 3.8806265365565196e-05, 3.880571239278652e-05, 3.880499571096152e-05, 3.880410804413259e-05, 3.880305666825734e-05, 3.8801834307378165e-05, 3.8800444599473856e-05, 3.87988802685868e-05, 3.879714495269582e-05, 3.8795235013822094e-05, 3.879315045196563e-05, 3.879089126712642e-05, 3.8788453821325675e-05, 3.8785838114563376e-05, 3.878304414683953e-05, 3.8780071918154135e-05, 3.877691779052839e-05, 3.877357812598348e-05, 3.877006747643463e-05, 3.8766367651987814e-05, 3.876248956657946e-05, 3.875842594425194e-05, 3.8754180422984064e-05, 3.8749749364797026e-05, 3.874513640766963e-05, 3.8740334275644266e-05, 3.873535024467856e-05, 3.8730184314772493e-05, 3.872482920996845e-05, 3.871929220622405e-05, 3.871356602758169e-05, 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3.829932757071219e-05, 3.828611079370603e-05, 3.827275213552639e-05, 3.825925523415208e-05, 3.8245623727561906e-05, 3.8231850339798264e-05, 3.821794962277636e-05, 3.820391066255979e-05, 3.818974801106378e-05, 3.817545439233072e-05, 3.816103708231821e-05, 3.814649244304746e-05, 3.8131831388454884e-05, 3.811704664258287e-05, 3.810214911936782e-05, 3.8087135180830956e-05, 3.807200482697226e-05, 3.805676897172816e-05, 3.804142033914104e-05, 3.802596984314732e-05, 3.801041020778939e-05, 3.799475598498248e-05, 3.7978999898768955e-05, 3.7963145587127656e-05, 3.794720396399498e-05, 3.793116411543451e-05, 3.791503331740387e-05, 3.789882612181827e-05, 3.78825279767625e-05, 3.786614615819417e-05, 3.784968066611327e-05, 3.783314969041385e-05, 3.781653504120186e-05, 3.7799851270392544e-05, 3.7783102015964694e-05, 3.776627636398189e-05, 3.774939614231698e-05, 3.773245043703355e-05, 3.77154428861104e-05, 3.769838440348394e-05, 3.7681271351175376e-05, 3.7664103729184724e-05, 3.764688881346956e-05, 3.762963024200872e-05, 3.7612328014802194e-05, 3.759498213184997e-05, 3.7577603507088504e-05, 3.756018850253895e-05, 3.754274075618014e-05, 3.752526026801206e-05, 3.7507754313992336e-05, 3.749022653209977e-05, 3.747267328435555e-05, 3.7455109122674905e-05, 3.7437519495142624e-05, 3.741991895367392e-05, 3.740230749826878e-05, 3.7384688766906045e-05, 3.736707003554329e-05, 3.7349444028222933e-05, 3.733181438292377e-05, 3.731419565156102e-05, 3.729656964424066e-05, 3.727895818883553e-05, 3.726136128534563e-05, 3.724377893377096e-05, 3.72262074961327e-05, 3.7208654248388484e-05, 3.719112282851711e-05, 3.717361323651858e-05, 3.71561327483505e-05, 3.713868500199169e-05, 3.712127363542095e-05, 3.710389137268066e-05, 3.708654185174965e-05, 3.7069235986564315e-05, 3.7051977415103465e-05, 3.70347588614095e-05, 3.7017580325482406e-05, 3.7000467273173854e-05, 3.698339060065337e-05, 3.6966375773772604e-05, 3.694941915455274e-05, 3.6932531656930216e-05, 3.69156914530322e-05, 3.68989203707315e-05, 3.688222204800695e-05, 3.686558557092212e-05, 3.684902549139224e-05, 3.68325381714385e-05, 3.681613088701852e-05, 3.679980000015348e-05, 3.67835491488222e-05, 3.6767385608982295e-05, 3.675130210467614e-05, 3.6735313187818974e-05, 3.6719411582453176e-05, 3.670359728857875e-05, 3.668788122013211e-05, 3.6672267015092075e-05, 3.6656743759522215e-05, 3.6641329643316574e-05, 3.6626006476581103e-05, 3.661079972516745e-05, 3.65956875612028e-05, 3.658069908851757e-05, 3.656581247923896e-05, 3.655103500932455e-05, 3.653637395473197e-05, 3.652183659141883e-05, 3.650740836746991e-05, 3.649310383480042e-05, 3.647892663138919e-05, 3.6464858567342155e-05, 3.645092147053219e-05, 3.6437118978938095e-05, 3.642343290266581e-05, 3.64098850695882e-05, 3.6396453651832423e-05, 3.638317502918653e-05, 3.637001282186248e-05, 3.635699613369071e-05, 3.6344117688713595e-05, 3.633138112490997e-05, 3.631877552834339e-05, 3.6306315450929105e-05, 3.62940008926671e-05, 3.628181730164215e-05, 3.626977922976948e-05, 3.6257904866943136e-05, 3.6246172385290265e-05, 3.623457087087445e-05, 3.622312215156853e-05, 3.6211833503330126e-05, 3.620068309828639e-05, 3.6189692764310166e-05, 3.617885158746503e-05, 3.616817775764503e-05, 3.615764217101969e-05, 3.6147263017483056e-05, 3.613704757299274e-05, 3.6126984923612326e-05, 3.611708234529942e-05, 3.6107321648159996e-05, 3.609772466006689e-05, 3.60882913810201e-05, 3.6079025448998436e-05, 3.606990867410786e-05, 3.606095197028481e-05, 3.605215169955045e-05, 3.6043515137862414e-05, 3.603503864724189e-05, 3.6026725865667686e-05, 3.6018584069097415e-05, 3.601058415370062e-05, 3.600275158532895e-05, 3.599507545004599e-05, 3.598758848966099e-05, 3.598022158257663e-05, 3.597304748836905e-05, 3.5966018913313746e-05, 3.595916132326238e-05, 3.59524528903421e-05, 3.594591180444695e-05, 3.593953078961931e-05, 3.5933309845859185e-05, 3.592724533518776e-05, 3.5921337257605046e-05, 3.591560744098388e-05, 3.591001950553619e-05, 3.590458072721958e-05, 3.5899327485822134e-05, 3.589421248761937e-05, 3.588925756048411e-05]
I have tried the examples given in
Python gaussian fit on simulated gaussian noisy data, and Fitting (a gaussian) with Scipy vs. ROOT et al without luck.
I'm looking to do this with lmfit because it has several advantages. This attempt was done following lmfit documentation, here is the code and plot
from numpy import sqrt, pi, exp
from lmfit import Model
import matplotlib.pyplot as plt
def gaussian(x, amp, cen, wid):
"1-d gaussian: gaussian(x, amp, cen, wid)"
return (amp/(sqrt(2*pi)*wid)) * exp(-(x-cen)**2 /(2*wid**2))
gmodel = Model(gaussian)
result = gmodel.fit(y, x=x, amp=-0.5, cen=4200, wid=2)
plt.plot(x, y,'ro', ms=6)
plt.plot(x, result.init_fit, 'g--', lw=2)
plt.plot(x, result.best_fit, 'b-', lw=2)
So in green is the fit with the initial parameters, and in blue is what should be the best fit, and as you can see I get a gaussian shifted from my points and a straight line.
Also, the third row of my data are the errors in the y axis. How can I take the errors into account when fitting the data with lmfit?
The easiest way to do this is probably to make use of the built-in models and combine the GaussianModel and ConstantModel. You can use the errors in the fitting using the keyword 'weights' as described here.
You'll probably want to do something like this:
import numpy as np
from lmfit import Model
from lmfit.models import GaussianModel, ConstantModel
import matplotlib.pyplot as plt
xval = np.array(x)
yval = np.array(y)
err = np.array(e)
peak = GaussianModel()
offset = ConstantModel()
model = peak + offset
pars = offset.make_params(c=np.median(y))
pars += peak.guess(yval, x=xval, amplitude=-0.5)
result = model.fit(yval, pars, x=xval, weights=1/err)
print(result.fit_report())
plt.plot(xval, yval, 'ro', ms=6)
plt.plot(xval, result.best_fit, 'b--')