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Merge pull request #158 from monocongo/develop
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Additional test coverage, cleanups

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@monocongo
monocongo authored May 15, 2018
2 parents 59d7253 + f523499 commit 96067fb
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -39,7 +39,7 @@ with the following goals in mind:
documented code that is faithful to the relevant literature and
which produces scientifically verifiable results
- to provide a central, open location for participation and collaboration
among researchers, developers, and users of climate indices
for researchers, developers, and users of climate indices
- to facilitate standardization and consensus on best-of-breed
climate index algorithms and corresponding compliant implementations in Python
- to provide transparency into the operational code used for climate
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2 changes: 1 addition & 1 deletion climate_indices/palmer.py
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Expand Up @@ -1157,7 +1157,7 @@ def _pdsi_from_zindex(Z):
# # DEBUG only -- REMOVE
# #
# # this is left here to remind us to focus on the PMDI appearing to be
# # off my a month, something like this may fix things
# # off by a month, something like this may fix things
# #
# if k > 0:
# PMDI[k - 1] = _pmdi(Pe, X1, X2, X3) #TODO remove, testing only
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22 changes: 11 additions & 11 deletions docs/index.rst
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Expand Up @@ -59,7 +59,7 @@ with the following goals in mind:
documented code that is faithful to the relevant literature and
which produces scientifically verifiable results
- to provide a central, open location for participation and collaboration
among researchers, developers, and users of climate indices
for researchers, developers, and users of climate indices
- to facilitate standardization and consensus on best-of-breed
climate index algorithms and corresponding compliant implementations in Python
- to provide transparency into the operational code used for climate
Expand Down Expand Up @@ -247,7 +247,7 @@ These Python scripts are written to be run via bash shell commands, i.e.
Example Command Line Invocations
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

PET monthly:
PET monthly
""""""""""""

``$ python process_grid.py --index pet --time_series_type monthly --netcdf_temp
Expand All @@ -260,8 +260,8 @@ resolution nClimGrid temperature dataset provided as an example input). The inpu
dataset is monthly data and the calibration period used will be Jan. 1951 through
Dec. 2010. The output file will be `/data/nclimgrid_lowres_pet.nc`.

SPI (both gamma and Pearson III distribution fittings), daily:
""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
SPI daily
""""""""""

``$ python process_grid.py --index spi --time_series_type daily --netcdf_precip
../example_inputs/cmorph_lowres_daily_conus_prcp.nc --var_name_precip
Expand All @@ -278,8 +278,8 @@ Jan. 1st, 1998 through Dec. 31st, 2016. The index will be computed at 30-day and
`/data/cmorph_lowres_daily_conus_spi_pearson_30.nc`, and
`/data/cmorph_lowres_daily_conus_spi_pearson_90.nc`.

SPI (both gamma and Pearson III distribution fittings), monthly:
""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
SPI monthly
""""""""""""

``$ python process_grid.py --index spi --time_series_type monthly --netcdf_precip
../example_inputs/nclimgrid_lowres_prcp.nc --var_name_precip prcp
Expand All @@ -294,8 +294,8 @@ The output files will be `/data/nclimgrid_lowres_spi_gamma_06.nc`,
`/data/nclimgrid_lowres_spi_gamma_12.nc`, `/data/nclimgrid_lowres_spi_pearson_06.nc`,
and `/data/nclimgrid_lowres_spi_pearson_12.nc`.

SPEI (both gamma and Pearson III distribution fittings), monthly:
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
SPEI monthly
"""""""""""""

``$ python process_grid.py --index spei --time_series_type monthly --netcdf_precip
../example_inputs/nclimgrid_lowres_prcp.nc --var_name_precip prcp --netcdf_pet
Expand All @@ -310,8 +310,8 @@ datasets will be computed at 9-month and 18-month timescales. The output files w
`/data/nclimgrid_lowres_spi_gamma_09.nc`, `/data/nclimgrid_lowres_spi_gamma_18.nc`,
`/data/nclimgrid_lowres_spi_pearson_09.nc`, and `/data/nclimgrid_lowres_spi_pearson_18.nc`.

Palmers, monthly:
""""""""""""""""""""""""""""""""
Palmers monthly
""""""""""""""""
``$ python process_grid.py --index palmers --time_series_type monthly --netcdf_precip
../example_inputs/nclimgrid_lowres_prcp.nc --var_name_precip prcp --netcdf_pet
../example_inputs/nclimgrid_lowres_pet.nc --var_name_pet pet --netcdf_awc
Expand All @@ -328,7 +328,7 @@ data and the calibration period used will be Jan. 1951 through Dec. 2010. The ou
`/data/nclimgrid_lowres_pmdi.nc`, `/data/nclimgrid_lowres_scpdsi.nc`, and `/data/nclimgrid_lowres_zindex.nc`.

Get involved
------------
-------------

Please use, make suggestions, and contribute to this code. Without
diverse participation and community adoption this project will not reach
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3 changes: 0 additions & 3 deletions scripts/process_grid.py
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Expand Up @@ -687,9 +687,6 @@ def _process_latitude_palmers(self, lat_index):
not math.isnan(awc) and \
not math.isclose(awc, self.fill_value_awc):

# DEBUG ONLY -- REMOVE
_logger.debug('Computing Palmers for longitude index %s', lon_index)

# put precipitation and PET into inches, if not already
if self.units_precip in _POSSIBLE_MM_UNITS:
precip_time_series = precip_time_series * _MM_TO_INCHES_FACTOR
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127 changes: 127 additions & 0 deletions tests/fixtures.py
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Expand Up @@ -3548,6 +3548,133 @@ class FixturesTestCase(unittest.TestCase):
[1.69788135272, 0.407888998498, -0.408004835557, -0.523429338106, 0.00492069766551, -0.112960527545, -0.894426175186, 0.309923952877, -0.849070546489, 0.214937088911, -1.6499790112, 0.0238953042084], \
[0.14644073154, -0.200863345231, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN] ])

# array of expected transformed/fitted values corresponding to the input array of monthly precipitation values,
# with calibration period corresponding to the full period of record
fixture_transformed_pearson3_monthly_fullperiod = np.array([
0.065, 1.113, -0.475, 1.205, 0.952, -0.284, -0.244, 0.167, -0.545, 0.045, 0.875, -0.016, \
0.688, 0.420, -0.454, -1.295, -0.361, 1.342, -0.318, -0.701, -0.425, -0.726, 0.824, -0.104, \
-0.489, 1.039, -0.569, 0.555, -0.994, -0.180, 1.500, 0.354, 1.507, 0.150, -0.130, 0.329, \
-1.259, -0.812, -1.279, -1.141, -1.383, -0.666, 0.576, 1.786, 0.570, 1.157, 0.446, 0.689, \
1.188, 2.302, -0.541, 0.788, -1.436, 0.344, 0.173, -0.162, 0.476, 0.703, -1.132, -0.024, \
0.775, 1.004, 2.092, 0.807, 0.845, 0.335, 0.244, -0.422, -0.673, -0.054, -0.113, 1.320, \
0.249, 0.267, 0.706, -0.292, 0.200, 1.106, -0.029, 1.540, -0.145, -1.766, -0.632, 0.869, \
-0.661, 1.664, -0.367, -0.651, -1.038, -0.118, -0.919, -1.144, 0.415, 1.380, 0.101, 0.704, \
1.414, 1.524, 2.118, -1.843, -0.897, -0.101, 0.126, -0.290, 0.583, -1.648, 0.948, -0.005, \
0.909, 0.127, 0.022, -0.489, -0.086, 0.783, -0.839, -0.605, -1.158, 1.426, 0.748, -0.512, \
-1.141, -1.241, 0.137, 0.492, 0.579, -0.777, 1.370, 0.793, -0.310, -0.626, -1.386, 2.056, \
0.986, 0.281, 0.346, -0.855, 0.946, 0.820, 0.307, 0.951, -2.021, -0.377, -0.441, -3.291, \
-0.718, -1.093, -1.634, -0.692, 1.159, -0.130, -0.595, -0.319, -0.004, -1.395, -0.002, 0.931, \
0.241, -0.589, -1.199, -0.420, -0.202, -0.109, -0.561, 0.880, 1.685, 0.105, 0.155, -0.929, \
0.101, -1.065, -0.480, 0.601, 0.669, 0.292, 0.307, 1.323, -1.099, -0.248, -0.734, 0.281, \
-0.323, -0.224, -0.721, -1.279, 0.174, 0.145, -0.979, 0.110, -0.073, 1.579, -0.963, -1.242, \
-1.289, -1.959, -1.175, 0.355, 0.977, -0.649, 0.888, 0.060, -0.509, -0.312, 2.185, 0.447, \
1.364, 0.700, 0.667, 0.824, 0.897, -0.055, -0.857, -1.045, 0.183, 0.715, 2.325, -0.424, \
0.408, 0.317, 0.205, 0.704, 0.617, -0.121, -1.593, -0.544, -0.715, -1.527, 1.901, -0.634, \
0.224, -0.100, -0.684, 1.764, 0.258, -0.816, 1.829, -1.292, 0.297, 0.382, 1.100, 1.364, \
0.843, 0.921, -0.159, -0.487, 0.285, 1.040, 0.665, -1.504, -0.028, -0.105, 0.447, -0.243, \
-0.441, -0.045, -1.457, 0.631, 0.110, 0.697, 0.899, -1.245, -1.549, 1.229, 0.614, -1.048, \
-0.736, -1.288, 0.981, 0.146, -0.696, -0.203, 0.001, -0.128, 1.124, -0.019, -2.546, -0.869, \
0.208, -1.270, 0.448, 1.057, -0.465, 0.187, -0.508, -1.086, -0.508, 0.226, -0.544, 0.364, \
0.024, 0.654, 0.982, -0.003, 0.556, 0.557, -1.269, 0.817, -1.508, -1.305, 0.080, -0.465, \
-0.151, 0.193, -1.014, 0.836, 1.934, -1.461, 1.415, -2.863, 0.794, -0.267, 1.007, 0.389, \
-2.007, 0.491, 1.363, -1.314, -0.138, -1.845, -0.820, -1.017, -3.230, 1.934, -1.203, -0.567, \
-0.677, -0.854, -1.962, 0.183, 1.032, 0.282, -0.001, 0.822, 1.356, 2.469, 0.719, 0.091, \
-2.176, -0.732, -1.113, 0.988, 0.576, 0.202, 0.069, 0.110, -0.342, -0.493, -1.976, -1.796, \
1.306, 0.418, -1.273, 0.924, -0.209, -1.373, 0.439, 0.011, -0.799, 0.734, -1.628, -0.731, \
1.566, -0.334, 1.016, 0.092, 1.233, -0.224, -0.943, 1.099, -2.683, -2.860, 0.718, 1.673, \
1.891, -1.573, -1.758, 0.182, -0.574, 0.453, 1.541, 1.755, 1.568, -0.097, 0.108, -2.289, \
-0.304, 0.863, -0.487, 0.110, -1.783, -1.052, 0.062, -0.829, -0.371, 0.550, -0.382, -0.864, \
-0.870, 0.292, 0.124, -0.871, -0.035, -0.392, 0.261, 1.431, 0.928, -0.460, -0.455, 0.495, \
-0.254, -0.240, 0.228, -0.170, 0.347, -0.257, -0.301, -0.034, 0.949, 1.345, -0.188, 0.653, \
0.793, 0.301, 1.022, 0.842, 0.297, 1.370, -0.896, -0.680, -1.285, 0.391, -0.626, 0.045, \
1.820, 0.027, 1.340, 1.488, -0.613, -1.895, -1.454, -1.592, 0.528, -0.054, -0.251, -0.924, \
0.856, -1.101, -0.397, -0.387, 1.832, 0.410, -1.355, 1.046, -0.938, 0.873, 0.921, -0.661, \
-0.019, -1.514, 0.866, 1.500, 0.327, -1.075, 0.771, 0.666, -1.899, 2.015, -0.179, -1.427, \
0.168, -0.408, 0.055, 0.391, 2.930, -0.121, -0.883, -0.349, 0.538, -0.971, -1.558, -1.154, \
-1.444, -1.225, -1.405, 1.046, -0.411, 0.332, 0.281, -0.689, 1.726, 0.025, 0.928, -0.390, \
0.707, 1.426, 1.213, -0.810, 0.622, 1.481, 1.467, 1.624, -0.668, 0.238, 0.412, -0.292, \
-0.401, 0.842, 1.097, -0.737, 0.336, -0.218, -0.135, 0.230, 0.916, -0.098, -0.627, -0.712, \
-0.163, -0.745, -0.982, -1.274, 0.051, -0.720, 0.106, -3.715, -1.191, -0.482, -0.038, -0.509, \
0.372, -0.451, -0.293, 0.050, -0.089, -0.577, 0.840, -0.568, -1.050, 1.067, -0.609, 0.866, \
0.000, 0.128, 0.473, -0.307, -0.989, -0.712, 0.739, 0.320, 2.436, -0.951, -0.808, 1.152, \
0.994, 1.770, 1.618, 1.975, -1.639, -0.246, 1.332, -2.119, -0.312, -0.132, 1.130, 0.821, \
0.127, 0.311, 0.341, 1.955, -0.378, 1.074, -2.097, -1.217, -0.924, -0.939, 0.100, 1.580, \
-0.141, -1.145, -0.258, -0.796, -0.366, -1.104, 0.569, -0.024, -0.445, 1.149, 1.546, -1.024, \
0.227, -1.885, -0.404, -1.005, 0.490, -1.697, 1.427, -0.506, -0.417, 0.305, -3.291, -0.563, \
0.619, -1.514, -1.239, -0.523, -1.505, -1.751, -0.221, 0.666, 1.028, 0.645, -0.279, -0.023, \
-0.459, -0.494, 0.628, -0.403, 0.677, 0.006, 0.725, -0.651, -0.335, -0.645, 0.876, 0.915, \
0.194, 0.743, 0.247, -0.006, 0.015, 1.431, 1.813, 1.137, -0.270, 1.825, 0.625, 1.013, \
0.909, -1.055, 0.390, 0.486, -0.570, -1.196, 0.290, 1.012, 1.859, 0.901, -0.959, -0.343, \
-1.825, -1.207, 0.136, 0.725, 0.262, -0.073, 2.441, 0.465, 0.489, 1.197, 1.196, 0.525, \
-0.751, -0.264, -0.322, 0.013, -0.369, -2.442, 0.890, 0.300, -0.961, 1.218, -0.438, 0.780, \
-0.813, 0.032, -1.474, 1.566, -1.214, -1.895, 0.374, 0.393, -1.061, 0.087, -1.162, -1.559, \
-1.024, 0.654, 0.843, -0.039, -0.655, -0.902, 0.448, -0.038, -1.387, 1.419, -1.135, -1.009, \
1.517, 0.390, -0.488, -0.175, -1.429, 1.252, 1.119, 0.325, 0.795, 0.628, -0.169, -0.221, \
-0.244, 0.922, 1.055, 1.621, 0.978, 0.371, 0.325, -0.136, 0.253, -0.784, 0.411, -0.913, \
-0.876, -1.303, -0.941, 0.007, -0.436, 1.221, 0.296, -1.042, -0.074, -0.827, -1.197, 0.604, \
-0.841, -0.528, -2.033, -0.283, -1.337, -1.153, -1.705, -0.678, 0.100, 0.156, -1.559, -0.743, \
-1.521, 1.412, 0.281, 0.402, 0.644, -1.277, 0.275, 0.451, -0.090, 0.300, 0.047, 1.304, \
2.240, -0.148, 2.353, -0.650, 1.930, 0.523, 0.973, -0.124, -0.927, -0.162, -0.124, 1.895, \
-0.337, 0.280, 1.435, -0.797, 1.267, 0.899, 1.442, -0.034, 0.807, 1.285, 1.292, 0.299, \
-1.441, 0.414, -1.047, 1.677, 1.553, 0.176, 0.703, 0.507, 2.158, 0.423, 0.425, -1.285, \
0.714, -0.225, -0.413, -1.807, 0.773, -0.937, -1.996, -1.024, -2.080, -0.367, -0.652, -1.058, \
0.464, -0.839, 0.470, -0.062, -1.533, 0.939, -0.311, 0.560, 0.185, -1.037, 0.810, -0.636, \
-0.561, 0.957, -1.771, -1.263, 0.339, -0.094, -1.785, -0.019, 1.633, -0.747, 0.667, 0.990, \
-1.099, 0.324, -0.473, 0.892, -0.543, 1.269, -0.528, -0.631, 0.027, 0.622, 0.266, 0.258, \
-0.372, 1.025, -0.560, -0.816, -1.395, -0.534, -1.373, -2.047, 1.073, 1.367, 0.089, -0.704, \
1.194, -0.065, 0.092, 0.030, 0.816, 2.116, 1.235, -0.634, 0.756, -0.009, -1.229, -0.396, \
0.497, -0.183, 0.074, -1.593, -1.164, 1.601, -0.973, -1.163, 0.684, 0.749, -0.082, 0.683, \
0.034, 0.749, -0.435, -1.272, 2.848, 1.868, 0.094, -0.369, 1.172, 1.294, -0.369, -3.291, \
1.484, 0.543, 0.464, 0.837, 0.893, 2.412, -0.185, 0.160, 0.690, 1.538, -0.403, -0.304, \
0.918, 0.663, 0.593, -1.849, 0.816, 0.175, 0.345, -1.412, -0.767, 0.261, -2.825, -1.750, \
-0.723, -0.534, -1.669, -1.731, -0.623, -0.141, -1.555, -0.214, -0.463, 0.439, -0.056, 1.201, \
-0.122, 0.776, -0.069, 1.247, 0.699, 0.752, 0.671, -0.715, 0.481, -0.382, 0.868, 0.161, \
0.570, 0.649, -0.122, -0.614, -1.123, 0.860, -0.282, 2.114, -0.210, -0.402, -0.977, 1.389, \
-0.076, -1.623, -1.459, -0.517, -0.289, -0.084, 0.249, -0.448, -1.006, -0.515, 0.069, 0.133, \
-1.055, -0.373, -1.164, -1.184, 0.702, 0.488, -0.193, -0.225, -0.117, -0.092, -0.573, -0.030, \
-0.468, 0.967, -1.162, 0.656, 1.041, 0.583, -1.392, 1.732, 0.027, -0.865, 0.419, 0.150, \
0.455, 0.129, -1.094, -0.389, 1.204, -0.472, -0.728, -0.033, 0.892, -0.960, 1.013, 0.913, \
0.501, 1.321, 0.766, 0.830, 0.379, 0.339, -0.939, 0.353, 0.056, 0.579, 0.118, 0.635, \
0.371, -1.119, -0.956, 2.015, 0.138, -0.839, 0.240, -0.875, 0.957, 0.312, -0.316, 1.256, \
0.387, 0.179, -0.243, 1.023, -0.516, 0.290, 0.718, 0.065, -0.265, -0.150, 1.950, 0.091, \
-1.067, 1.669, 0.011, -1.683, -0.811, -0.571, -0.466, 2.782, 1.581, -1.211, 0.180, -0.661, \
-0.939, -0.392, 1.254, 2.040, 1.030, 0.996, -1.404, -0.303, -0.612, -0.251, 1.451, -0.254, \
2.703, 2.752, 1.600, 0.491, -1.354, 0.971, -0.858, 0.286, -0.021, -0.199, 0.649, 1.203, \
-1.216, 0.091, 0.890, -0.121, 0.570, -0.685, 0.607, -0.090, -0.036, -1.443, 0.143, -1.214, \
-1.508, -1.157, 0.660, 0.164, -0.062, -1.117, 1.475, -0.993, -0.309, 0.021, 0.540, 0.815, \
0.539, 0.102, 1.593, -0.536, -1.378, -0.270, -1.305, -0.047, -0.942, -1.206, 0.592, 1.794, \
0.525, -0.030, 1.767, -1.712, 0.259, -2.063, -1.548, -1.350, 0.796, 0.125, 1.227, 0.461, \
0.410, -0.429, -0.924, -0.867, 0.590, 1.337, 2.247, 1.883, -1.469, -0.976, -0.169, -1.886, \
-0.622, -1.111, 0.265, -0.353, -1.278, -0.832, -0.177, -0.045, -0.942, -1.065, -0.035, -0.243, \
-1.281, -0.705, 0.118, 0.023, 0.610, -0.381, -1.008, 0.420, -0.672, -0.434, 0.096, -0.436, \
0.656, -0.373, 1.020, 0.030, 0.721, 0.253, -0.281, 0.142, 0.533, 0.735, -0.941, -0.015, \
-0.146, 0.132, 0.551, 0.338, -1.760, 2.388, -0.942, 0.394, -0.005, -1.249, 2.001, -0.826, \
2.065, 0.179, 0.663, -0.069, -0.238, -0.580, -0.472, -0.345, -1.041, 0.508, -0.912, -0.619, \
0.847, 1.745, -0.512, 0.841, -0.262, -0.995, -0.860, -0.017, 1.032, 0.326, 1.717, 1.467, \
0.580, -0.839, 0.550, 0.747, -0.117, 1.535, 0.435, 1.378, -0.159, 1.206, -0.763, -0.345, \
0.288, -0.858, 0.527, -0.043, 0.635, 0.829, -1.520, 0.060, -0.228, 0.980, -0.886, -0.843, \
0.920, -0.814, 0.572, 0.004, 0.142, 1.711, 0.109, 0.132, 0.398, -1.002, 0.323, 2.173, \
0.387, 2.227, 2.018, -1.634, -0.603, -1.957, -0.242, -0.252, 1.756, -0.896, 1.853, -0.010, \
1.133, -1.444, -0.717, -0.643, -0.282, 0.412, -0.739, 0.788, 0.932, 1.851, 0.188, 0.055, \
-0.348, 0.187, 0.454, 0.271, -1.588, 0.420, -0.878, 0.679, -0.752, 0.699, -1.269, 0.747, \
-1.057, -1.890, 0.823, 0.319, -0.082, -0.430, 1.088, 1.200, 1.042, 0.990, 0.014, 0.727, \
0.127, 0.948, -0.249, -1.185, 0.401, 1.506, 1.517, -1.204, -0.850, -1.626, 0.056, 0.755, \
-0.913, -0.336, 2.035, 0.310, 1.247, -0.134, -1.677, 0.563, 0.866, -1.302, 1.300, -0.037, \
0.583, 0.777, -1.441, -0.256, -1.596, -2.349, 1.141, 0.411, -0.397, -0.289, -0.335, -1.698, \
-0.589, -1.665, 0.623, -0.760, -0.912, 1.157, -0.730, 1.462, 0.802, -0.693, -0.720, 0.059, \
-1.396, 0.550, -1.210, -0.689, -0.303, -0.508, 1.613, 0.539, 0.373, -1.832, -0.143, 0.834, \
0.138, 1.132, -0.395, 1.003, 0.018, 1.017, 0.203, -1.737, 0.335, 0.318, -1.296, 0.539, \
-0.965, 0.607, 0.695, 0.186, -1.763, -0.251, -0.422, 1.367, -0.520, -0.360, -1.148, -0.862, \
-1.802, -0.766, -0.313, -1.648, 0.789, 0.481, -0.740, -0.176, 0.300, -2.550, 1.176, 1.349, \
-0.037, 1.700, 0.331, 0.570, -1.077, -0.724, 0.350, 0.918, 1.969, -0.690, 0.468, -0.058, \
1.325, -1.620, 0.093, -0.366, -1.104, -0.612, 0.372, 1.713, 0.758, 1.478, -0.532, -0.917, \
-1.652, 0.886, -0.463, 2.139, 1.438, 0.126, 0.404, 1.169, -0.449, -0.250, -0.970, -0.354, \
-1.155, 0.051, -0.059, 1.267, 1.429, -0.170, 1.032, -1.213, -0.070, -1.180, 1.723, 0.382, \
0.548, 0.226, 0.539, -1.240, -0.852, 0.021, 0.411, -1.259, -0.930, -0.536, -0.098, 0.024, \
-0.720, 0.198, -0.058, 1.449, -1.483, -0.146, 0.461, 0.560, 0.687, -0.798, 0.987, 2.724, \
1.891, 0.440, 0.101, -0.586, -0.200, -0.078, -1.164, 0.531, -0.681, -0.209, -1.431, 0.082, \
-0.007, -0.348, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN ])

# array of monthly temperature observations (122 years and 2 months, Jan 1895 through Feb 2017)
fixture_temps_celsius = np.array(
[[ 19.650390625, 15.66015625, 21.1103515625, 22.2802734375, 25.5595703125, 27.009765625, 27.73046875, 27.83984375, 27.16015625, 24.51953125, 22.259765625, 18.3701171875], \
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