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Panel_35 to use data from geocat.datafiiles instead of creating on the fly with generate_2d_array #559

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merged 12 commits into from
Jan 10, 2024
Merged

Panel_35 to use data from geocat.datafiiles instead of creating on the fly with generate_2d_array #559

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b3eef03
rm
jukent Nov 6, 2023
2a2fbee
fix readme
jukent Nov 6, 2023
f3c4a16
new line at end
jukent Nov 6, 2023
fd28bee
rm comp from conf
jukent Nov 6, 2023
dab885c
precommit
jukent Nov 6, 2023
1a0310d
del topo1
jukent Nov 8, 2023
19472f0
first step to using new gc.datafiles
jukent Nov 9, 2023
4f2920d
Merge branch 'NCAR:main' into panel_35
jukent Nov 9, 2023
7bf33d3
Merge branch 'main' into panel_35
jukent Jan 10, 2024
cc331d2
fix pre-commit
jukent Jan 10, 2024
7ab92e9
open dataarray not dataset
jukent Jan 10, 2024
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go back to cmap
jukent Jan 10, 2024
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202 changes: 9 additions & 193 deletions Gallery/Panels/NCL_panel_35.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,6 @@
- Attaching three filled contour plots along Y axes
- Adding a common colorbar to attached plots
- Adding a common title to attached plots
- Generating dummy data using "generate_2d_array"
- Drawing a custom colorbar
- Drawing a custom title
- Retrieving the bounding box of a plot
Expand All @@ -20,205 +19,22 @@

import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
import cmaps

import geocat.viz as gv
import geocat.datafiles as gcd

import math

################################################################
# Definition of generate_2d_array and helper functions from https://github.com/NCAR/pyngl/blob/develop/src/ngl/__init__.py

# Globals for random number generator for generat_2d_array

dfran_iseq = 0
dfran_rseq = [.749, .973, .666, .804, .081, .483, .919, .903, \
.951, .960, .039, .269, .270, .756, .222, .478, \
.621, .063, .550, .798, .027, .569, .149, .697, \
.451, .738, .508, .041, .266, .249, .019, .191, \
.266, .625, .492, .940, .508, .406, .972, .311, \
.757, .378, .299, .536, .619, .844, .342, .295, \
.447, .499, .688, .193, .225, .520, .954, .749, \
.997, .693, .217, .273, .961, .948, .902, .104, \
.495, .257, .524, .100, .492, .347, .981, .019, \
.225, .806, .678, .710, .235, .600, .994, .758, \
.682, .373, .009, .469, .203, .730, .588, .603, \
.213, .495, .884, .032, .185, .127, .010, .180, \
.689, .354, .372, .429 \
]

# Random number generator for generate_2d_array.


def _dfran():
global dfran_iseq
global dfran_rseq
dfran_iseq = dfran_iseq % 100
r = dfran_rseq[dfran_iseq]
dfran_iseq = dfran_iseq + 1
return r

def generate_2d_array(dims, num_low, num_high, minv, maxv, seed=0, \
highs_at=None, lows_at=None):
"""Generates smooth 2D arrays primarily for use in examples.

array = generate_2d_array(dims, num_low, num_high, minv, maxv, seed=0,
highs_at=None, lows_at=None)
dims -- a list (or array) containing the dimensions of the
two-dimensional array to be returned.
num_low, num_high -- Integers representing the approximate minimum
and maximum number of highs and lows that the
output array will have. They must be in the
range 1 to 25. If not, then they will be set to
either 1 or 25.
minv, maxv -- The exact minimum and maximum values that the output array
will have.
iseed -- an optional argument specifying a seed for the random number
generator. If iseed is outside the range 0 to 99, it will
be set to 0.
lows_at -- an optional argument that is a list of coordinate
pairs specifying where the lows will occur. If this
argument appears, then its length must equal num_low and
the coordinates must be in the ranges specified in dims.
highs_at -- an optional argument that is a list of coordinate
pairs specifying where the highs will occur. If this
argument appears, then its length must equal num_high and
the coordinates must be in the ranges specified in dims.
"""

# Globals for random numbers.

global dfran_iseq
dfran_iseq = seed

# Check arguments.

try:
alen = len(dims)
except:
print(
"generate_2d_array: first argument must be a list, tuple, or array having two elements specifying the dimensions of the output array."
)
return None
if (alen != 2):
print(
"generate_2d_array: first argument must have two elements specifying the dimensions of the output array."
)
return None
if (int(dims[0]) <= 1 and int(dims[1]) <= 1):
print("generate_2d_array: array must have at least two elements.")
return None
if (num_low < 1):
print(
"generate_2d_array: number of lows must be at least 1 - defaulting to 1."
)
num_low = 1
if (num_low > 25):
print(
"generate_2d_array: number of lows must be at most 25 - defaulting to 25."
)
num_high = 25
if (num_high < 1):
print(
"generate_2d_array: number of highs must be at least 1 - defaulting to 1."
)
num_high = 1
if (num_high > 25):
print(
"generate_2d_array: number of highs must be at most 25 - defaulting to 25."
)
num_high = 25
if (seed > 100 or seed < 0):
print(
"generate_2d_array: seed must be in the interval [0,100] - seed set to 0."
)
seed = 0
if not lows_at is None:
if (len(lows_at) != num_low):
print(
"generate_2d_array: the list of positions for the lows must be the same size as num_low."
)
if not highs_at is None:
if (len(highs_at) != num_high):
print(
"generate_2d_array: the list of positions for the highs must be the same size as num_high."
)


# Dims are reversed in order to get the same results as the NCL function.

nx = int(dims[1])
ny = int(dims[0])
out_array = np.zeros([nx, ny], 'f')
tmp_array = np.zeros([3, 51], 'f')
fovm = 9. / float(nx)
fovn = 9. / float(ny)
nlow = max(1, min(25, num_low))
nhgh = max(1, min(25, num_high))
ncnt = nlow + nhgh

for k in range(num_low):
if not lows_at is None:
tmp_array[0,
k] = float(lows_at[k][1]) # lows at specified locations.
tmp_array[1, k] = float(lows_at[k][0])
tmp_array[2, k] = -1.
else:
tmp_array[0, k] = 1. + (float(nx) -
1.) * _dfran() # lows at random locations.
tmp_array[1, k] = 1. + (float(ny) -
1.) * _dfran() # lows at random locations.
tmp_array[2, k] = -1.
for k in range(num_low, num_low + num_high):
if not highs_at is None:
tmp_array[0, k] = float(highs_at[k - num_low][1]) # highs locations
tmp_array[1, k] = float(highs_at[k - num_low][0]) # highs locations
tmp_array[2, k] = 1.
else:
tmp_array[0, k] = 1. + (float(nx) -
1.) * _dfran() # highs at random locations.
tmp_array[1, k] = 1. + (float(ny) -
1.) * _dfran() # highs at random locations.
tmp_array[2, k] = 1.

dmin = 1.e+36
dmax = -1.e+36
midpt = 0.5 * (minv + maxv)
for j in range(ny):
for i in range(nx):
out_array[i, j] = midpt
for k in range(ncnt):
tempi = fovm * (float(i + 1) - tmp_array[0, k])
tempj = fovn * (float(j + 1) - tmp_array[1, k])
temp = -(tempi * tempi + tempj * tempj)
if (temp >= -20.):
out_array[i,j] = out_array[i,j] + \
0.5*(maxv - minv)*tmp_array[2,k]*math.exp(temp)
dmin = min(dmin, out_array[i, j])
dmax = max(dmax, out_array[i, j])

out_array = (((out_array - dmin) / (dmax - dmin)) * (maxv - minv)) + minv

del tmp_array

return np.transpose(out_array, [1, 0])


def _get_double(obj, name):
return (NhlGetDouble(_int_id(obj), name))


def _get_double_array(obj, name):
return (NhlGetDoubleArray(_int_id(obj), name))


###############################################################################
# Create dummy data
nx = 100
ny = 100
data1 = generate_2d_array((ny, nx), 10, 10, -19., 16., 0)
data2 = generate_2d_array((ny, nx), 10, 10, -28., 15., 1)
data3 = generate_2d_array((ny, nx), 10, 10, -25., 18., 2)
# Uploading random smooth 2d data from geocat.datafiles
data1 = xr.open_dataarray(gcd.get("netcdf_files/panel_35_data1.nc"),
engine='netcdf4')
data2 = xr.open_dataarray(gcd.get("netcdf_files/panel_35_data2.nc"),
engine='netcdf4')
data3 = xr.open_dataarray(gcd.get("netcdf_files/panel_35_data3.nc"),
engine='netcdf4')

###############################################################################
# Create figure and axes using gv
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