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Gorkowski/issue14 (#15)
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* Mie Calculations rough calculations place holder
Fixes #14

* kappa changed

* sizer update, still needs work
Fixes #14

* ran mean sizer

* modified loader
Fixes #14

* merged with plots branch
Fixes #14

* update mean prop

* update from chamber analysis

* fixed average start time

* merger and interp

* merger  passed tests

* acount for nan and full nan rows

* updated test

* working ntcdf

* added working folder and netcdf

* working on the arm data import from netcdf

* updated kappa steps

* added netcdf reader to datalake

* loaded arm aps, works

* working on time formats

* merge local

* working on epoch corrections

* merge

* working 2d import error

* fixed 2d imports

* working on loading more data

* fixed the general loader and aeronet import

* updated analysis

* updated analysis

* updated colors and hist plot

* graphing working

* full run

* updated plots

* full run

* updated graphs

* fixed time sp2

* updated gcvi

* updated chamber

* fixed test errors
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@Gorkowski
Gorkowski authored Nov 10, 2023
1 parent a682bda commit 6f00e94
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271 changes: 271 additions & 0 deletions datacula/colors.py
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@@ -0,0 +1,271 @@
"""Color Hex Codes"""

# Tailwind CSS Colors
# https://tailwindcss.com/docs/customizing-colors#default-color-palette

TAILWIND = {
"slate": {
"50": "#f8fafc",
"100": "#f1f5f9",
"200": "#e2e8f0",
"300": "#cbd5e1",
"400": "#94a3b8",
"500": "#64748b",
"600": "#475569",
"700": "#334155",
"800": "#1e293b",
"900": "#0f172a"
},
"gray": {
"50": "#f9fafb",
"100": "#f3f4f6",
"200": "#e5e7eb",
"300": "#d1d5db",
"400": "#9ca3af",
"500": "#6b7280",
"600": "#4b5563",
"700": "#374151",
"800": "#1f2937",
"900": "#111827"
},
"zinc": {
"50": "#fafafa",
"100": "#f4f4f5",
"200": "#e4e4e7",
"300": "#d4d4d8",
"400": "#a1a1aa",
"500": "#71717a",
"600": "#52525b",
"700": "#3f3f46",
"800": "#27272a",
"900": "#18181b"
},
"neutral": {
"50": "#fafafa",
"100": "#f5f5f5",
"200": "#e5e5e5",
"300": "#d4d4d4",
"400": "#a3a3a3",
"500": "#737373",
"600": "#525252",
"700": "#404040",
"800": "#262626",
"900": "#171717"
},
"stone": {
"50": "#fafaf9",
"100": "#f5f5f4",
"200": "#e7e5e4",
"300": "#d6d3d1",
"400": "#a8a29e",
"500": "#78716c",
"600": "#57534e",
"700": "#44403c",
"800": "#292524",
"900": "#1c1917"
},
"red": {
"50": "#fef2f2",
"100": "#fee2e2",
"200": "#fecaca",
"300": "#fca5a5",
"400": "#f87171",
"500": "#ef4444",
"600": "#dc2626",
"700": "#b91c1c",
"800": "#991b1b",
"900": "#7f1d1d"
},
"orange": {
"50": "#fff7ed",
"100": "#ffedd5",
"200": "#fed7aa",
"300": "#fdba74",
"400": "#fb923c",
"500": "#f97316",
"600": "#ea580c",
"700": "#c2410c",
"800": "#9a3412",
"900": "#7c2d12"
},
"amber": {
"50": "#fffbeb",
"100": "#fef3c7",
"200": "#fde68a",
"300": "#fcd34d",
"400": "#fbbf24",
"500": "#f59e0b",
"600": "#d97706",
"700": "#b45309",
"800": "#92400e",
"900": "#78350f"
},
"yellow": {
"50": "#fefce8",
"100": "#fef9c3",
"200": "#fef08a",
"300": "#fde047",
"400": "#facc15",
"500": "#eab308",
"600": "#ca8a04",
"700": "#a16207",
"800": "#854d0e",
"900": "#713f12"
},
"lime": {
"50": "#f7fee7",
"100": "#ecfccb",
"200": "#d9f99d",
"300": "#bef264",
"400": "#a3e635",
"500": "#84cc16",
"600": "#65a30d",
"700": "#4d7c0f",
"800": "#3f6212",
"900": "#365314"
},
"green": {
"50": "#ecfdf5",
"100": "#d1fae5",
"200": "#a7f3d0",
"300": "#6ee7b7",
"400": "#34d399",
"500": "#10b981",
"600": "#059669",
"700": "#047857",
"800": "#065f46",
"900": "#064e3b"
},
"emerald": {
"50": "#ecfdf5",
"100": "#d1fae5",
"200": "#a7f3d0",
"300": "#6ee7b7",
"400": "#34d399",
"500": "#10b981",
"600": "#059669",
"700": "#047857",
"800": "#065f46",
"900": "#064e3b"
},
"teal": {
"50": "#f0fdfa",
"100": "#ccfbf1",
"200": "#99f6e4",
"300": "#5eead4",
"400": "#2dd4bf",
"500": "#14b8a6",
"600": "#0d9488",
"700": "#0f766e",
"800": "#115e59",
"900": "#134e4a"
},
"cyan": {
"50": "#ecfeff",
"100": "#cffafe",
"200": "#a5f3fc",
"300": "#67e8f9",
"400": "#22d3ee",
"500": "#06b6d4",
"600": "#0891b2",
"700": "#0e7490",
"800": "#155e75",
"900": "#164e63"
},
"sky": {
"50": "#f0f9ff",
"100": "#e0f2fe",
"200": "#bae6fd",
"300": "#7dd3fc",
"400": "#38bdf8",
"500": "#0ea5e9",
"600": "#0284c7",
"700": "#0369a1",
"800": "#075985",
"900": "#0c4a6e"
},
"blue": {
"50": "#eff6ff",
"100": "#dbeafe",
"200": "#bfdbfe",
"300": "#93c5fd",
"400": "#60a5fa",
"500": "#3b82f6",
"600": "#2563eb",
"700": "#1d4ed8",
"800": "#1e40af",
"900": "#1e3a8a"
},
"indigo": {
"50": "#eef2ff",
"100": "#e0e7ff",
"200": "#c7d2fe",
"300": "#a5b4fc",
"400": "#818cf8",
"500": "#6366f1",
"600": "#4f46e5",
"700": "#4338ca",
"800": "#3730a3",
"900": "#312e81"
},
"violet": {
"50": "#f5f3ff",
"100": "#ede9fe",
"200": "#ddd6fe",
"300": "#c4b5fd",
"400": "#a78bfa",
"500": "#8b5cf6",
"600": "#7c3aed",
"700": "#6d28d9",
"800": "#5b21b6",
"900": "#4c1d95"
},
"purple": {
"50": "#faf5ff",
"100": "#f3e8ff",
"200": "#e9d5ff",
"300": "#d8b4fe",
"400": "#c084fc",
"500": "#a855f7",
"600": "#9333ea",
"700": "#7e22ce",
"800": "#6b21a8",
"900": "#581c87"
},
"fuchsia": {
"50": "#fdf4ff",
"100": "#fae8ff",
"200": "#f5d0fe",
"300": "#f0abfc",
"400": "#e879f9",
"500": "#d946ef",
"600": "#c026d3",
"700": "#a21caf",
"800": "#86198f",
"900": "#701a75"
},
"pink": {
"50": "#fdf2f8",
"100": "#fce7f3",
"200": "#fbcfe8",
"300": "#f9a8d4",
"400": "#f472b6",
"500": "#ec4899",
"600": "#db2777",
"700": "#be185d",
"800": "#9d174d",
"900": "#831843"
},
"rose": {
"50": "#fff1f2",
"100": "#ffe4e6",
"200": "#fecdd3",
"300": "#fda4af",
"400": "#fb7185",
"500": "#f43f5e",
"600": "#e11d48",
"700": "#be123c",
"800": "#9f1239",
"900": "#881337"
},
}
77 changes: 74 additions & 3 deletions datacula/convert.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
"""conversion functions common for aerosol processing
"""

import warnings
from typing import Union, Tuple, Any, List, Dict
import numpy as np

Expand Down Expand Up @@ -384,7 +385,8 @@ def effective_refractive_index(

def convert_sizer_dn(
diameter: np.ndarray,
dn_dlogdp: np.ndarray
dn_dlogdp: np.ndarray,
inverse: bool = False
) -> np.ndarray:
"""
Converts the sizer data from dn/dlogdp to d_num.
Expand All @@ -398,6 +400,7 @@ def convert_sizer_dn(
diameter (np.ndarray): Array of particle diameters.
dn_dlogdp (np.ndarray): Array of number concentration of particles per
unit logarithmic diameter.
inverse (bool): If True, converts from d_num to dn/dlogdp.
Returns:
-----------
Expand All @@ -407,7 +410,8 @@ def convert_sizer_dn(
References:
-----------
Eq: dN/dlogD_p = dN/( log(D_{p-upper}) - log(D_{p-lower}) )
https://tsi.com/getmedia/1621329b-f410-4dce-992b-e21e1584481a/PR-001-RevA_Aerosol-Statistics-AppNote?ext=.pdf
https://tsi.com/getmedia/1621329b-f410-4dce-992b-e21e1584481a/
PR-001-RevA_Aerosol-Statistics-AppNote?ext=.pdf
# TODO: Address potential over-counting in last/first bin
"""
Expand All @@ -423,9 +427,12 @@ def convert_sizer_dn(
lower = diameter - delta/2
upper = diameter + delta/2

if inverse:
# Convert from dn to dn/dlogdp
return dn_dlogdp / np.log10(upper/lower)

# Convert from dn/dlogdp to dn
d_num = dn_dlogdp * np.log10(upper/lower)

return d_num


Expand Down Expand Up @@ -520,3 +527,67 @@ def get_values_in_dict(
f"{list(dict_to_check.keys())}"
)
return values


def data_shape_check(
time: np.ndarray,
data: np.ndarray,
header: list,
) -> np.ndarray:
"""
Check the shape of the input data and header list, and reshape the data if
necessary. The data array can be 1D or 2D. If the data array is 2D, the
time array must match the last dimensions of the data array. If the data
array is 1D, the header list must be a single entry.
Parameters:
----------
time (np.ndarray): 1D array of time values.
data (np.ndarray): 1D or 2D array of data values.
header (list): List of header values.
Returns:
-------
np.ndarray: Reshaped data array.
Raises:
-------
ValueError: If the length of the header list does not match the first
dimension of the data array.
"""

# Check if data_new is 2D or 1D
if len(data.shape) == 2:
# Check if time matches the dimensions of data
if len(time) == data.shape[0] and len(time) == data.shape[1]:
concatenate_axis_new = 0 # Default to the first axis
# Check if the last axis of data matches the length of time
if data.shape[-1] != len(time):
if data.shape[0] == len(time):
warnings.warn("Square data with time shape assumes time \
axis is the first axis in data.")
else:
warnings.warn("Inconsistent shapes between data and time.")
else:
# Find the axis that doesn't match the length of time
concatenate_axis_new = np.argwhere(
np.array(data.shape) != len(time)).flatten()[0]
# Reshape new data so the concatenate axis is the first axis
data = np.moveaxis(data, concatenate_axis_new, 0)

# check header list length matches data_new shape
if len(header) != data.shape[0]:
print(f'header len: {len(header)} vs. data.shape: \
{data.shape}')
print(header)
raise ValueError("Header list length must match the first \
dimension of data_new.")
else:
# check if header is a single entry
if len(header) != 1:
raise ValueError("Header list must be a single entry if data_new \
is 1D.")
# Reshape new data so the concatenate axis is the first axis
data = np.expand_dims(data, 0)

return data
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