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generate_xlsx.py
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import argparse
import math
import re
import typing
import os
import numpy
import pandas
from openpyxl import Workbook
from openpyxl.cell.cell import Cell
from openpyxl.styles import Alignment, Border, Fill, Font, PatternFill, Side, alignment, borders, colors, fills, numbers
from openpyxl.utils.dataframe import dataframe_to_rows
from openpyxl.worksheet.worksheet import Worksheet
# region Configuration
F_PATH = os.path.dirname(__file__)
F_NAME = f"{F_PATH}\\Pi.xlsx"
F_BASE = os.path.basename(F_NAME)
HIGH_NUMBER_MULTIPLIER = 3
OUTDATED = { # outdated or only available since
# "UP_CANN": "4.30", # examples
# "UP_CANNX": "5.50",
}
RE_LANGUAGE = re.compile("\(([A-Za-z1-9-]+)\)")
URL = "https://github.com/zencq/Pi"
VERSION = "5.50"
# endregion
# region Data
PRODUCT = [
"PROC_LOOT",
"PROC_HIST",
"PROC_BIO",
"PROC_FOSS",
"PROC_PLNT",
"PROC_TOOL",
"PROC_FARM",
"PROC_SEA",
"PROC_FEAR",
"PROC_SALV",
"PROC_BONE",
"PROC_DARK",
"PROC_STAR",
"PROC_EXH",
]
# order here defines how it will be in the generated file
TECHNOLOGY = {
"Suit": {
"UP_ENGY": ["X", "3"],
"UP_HAZ": ["X"],
"UP_JET": ["X", "4"],
"UP_SHLD": ["X", "4"],
"UP_SNSUIT": [""],
"UP_RBSUIT": [""],
"UP_UNW": ["3"],
"UP_RAD": ["3"],
"UP_TOX": ["3"],
"UP_COLD": ["3"],
"UP_HOT": ["3"],
},
"Ship": {
"UP_PULSE": ["X", "4"],
"UP_LAUN": ["X", "4"],
"UP_HYP": ["X", "4"],
"UP_S_SHL": ["X", "4"],
"UP_SGUN": ["X", "4"],
"UP_SLASR": ["X", "4"],
"UP_SSHOT": ["X", "4"],
"UP_SMINI": ["X", "4"],
"UP_SBLOB": ["X", "4"],
},
"AlienShip": {
"UA_PULSE": ["4"],
"UA_LAUN": ["4"],
"UA_HYP": ["4"],
"UA_S_SHL": ["4"],
"UA_SGUN": ["4"],
"UA_SLASR": ["4"],
},
"Weapon": {
"UP_LASER": ["X", "4"],
"UP_SCAN": ["X", "4"],
"UP_BOLT": ["X", "4"],
"UP_GREN": ["X", "4", "1"],
"UP_TGREN": ["X", "4"],
"UP_RAIL": ["X", "4"],
"UP_SHOT": ["X", "4"],
"UP_SMG": ["X", "4"],
"UP_CANN": ["X", "4"],
"UP_SENGUN": [""],
},
"Exocraft": {
"UP_EXGUN": ["4"],
"UP_EXLAS": ["4"],
"UP_BOOST": ["4"],
"UP_EXENG": ["4"],
},
"Submarine": {
"UP_EXSUB": ["4"],
"UP_SUGUN": ["4"],
},
"Mech": {
"UP_MCLAS": ["4"],
"UP_MCGUN": ["4"],
"UP_MCENG": ["4"],
"UP_MFIRE": ["4"],
},
"Freighter": {
"UP_FRHYP": ["4"],
"UP_FRSPE": ["4"],
"UP_FRFUE": ["4"],
"UP_FRCOM": ["4"],
"UP_FRTRA": ["4"],
"UP_FREXP": ["4"],
"UP_FRMIN": ["4"],
},
}
TECHNOLOGY_INDEX = [item_id for _, items in TECHNOLOGY.items() for item_id in items]
# endregion
# region Styles
ALIGNMENT_CENTER = Alignment(horizontal=alignment.horizontal_alignments[2]) # center
ALIGNMENT_LEFT = Alignment(horizontal=alignment.horizontal_alignments[1]) # left
BORDER_BOTTOM = Border(bottom=Side(border_style=borders.BORDER_MEDIUM, color=colors.BLACK))
BORDER_MIXED = Border(right=Side(border_style=borders.BORDER_DASHED, color=colors.BLACK), bottom=Side(border_style=borders.BORDER_MEDIUM, color=colors.BLACK))
BORDER_RIGHT = Border(right=Side(border_style=borders.BORDER_DASHED, color=colors.BLACK))
COLOR_COMMODITY = ("ffe8b3", "ffc94d", "ffbc25") # gold
COLOR_FLORA = ("c8fab7", "7ef457", "8af567") # green
COLOR_METAL = ("deddd3", "b3af98", "dbd9ce") # gray
COLOR_SPECIAL = ("cdb3ff", "8b4dff", "be9bff") # purple
FILL_RED = PatternFill(fill_type=fills.FILL_SOLID, start_color=colors.COLOR_INDEX[2]) # red
FONT_BOLD = Font(bold=True)
# endregion
# region Translation
LANGUAGES = [ # order defined by game
"Name (en)",
"Name (fr)",
"Name (it)",
"Name (de)",
"Name (es)",
"Name (ru)",
"Name (pl)",
"Name (nl)",
"Name (pt)",
"Name (es-419)",
"Name (pt-BR)",
"Name (ja)",
"Name (zh-Hans)",
"Name (zh-Hant)",
"Name (ko)",
]
TRANSLATION = {
# Inventories
"AlienShip": "Starship (Living)",
"Ship": "Starship (Regular)",
"Suit": "Exosuit",
"Weapon": "Multi-Tool",
"Exocraft": "Exocraft",
"Submarine": "Submarine (Nautilon)",
"Mech": "Mech (Minotaur)",
"Freighter": "Freighter",
# Products
"PROC_LOOT": "Unearthed Treasure",
"PROC_HIST": "Historical Document",
"PROC_BIO": "Biological Sample",
"PROC_FOSS": "Fossil Sample",
"PROC_PLNT": "Delicate Flora",
"PROC_TOOL": "Lost Artifact",
"PROC_FARM": "Delicate Flora",
"PROC_SEA": "Aquatic Treasure",
"PROC_FEAR": "Terrifying Sample",
"PROC_SALV": "Salvaged Scrap",
"PROC_BONE": "Excavated Bones",
"PROC_DARK": "Terrifying Sample",
"PROC_STAR": "Ancient Skeleton",
"PROC_EXH": "Exhibit Fossil Sample",
# Stats
"Weapon_Laser_Damage": "Damage",
"Weapon_Laser_Mining_Speed": "Mining Speed",
"Weapon_Laser_HeatTime": "Heat Dispersion",
"Weapon_Laser_ReloadTime": "Overheat Downtime",
"Weapon_Laser_Drain": "Fuel Efficiency",
"Weapon_Laser_ChargeTime": "Time to Full Power",
"Weapon_Projectile_Damage": "Damage",
"Weapon_Projectile_Rate": "Fire Rate",
"Weapon_Projectile_ClipSize": "Clip Size",
"Weapon_Projectile_ReloadTime": "Reload Time",
"Weapon_Projectile_MaximumCharge": "Ion Spheres Created",
"Weapon_Projectile_BurstCap": "Shots Per Burst",
"Weapon_Projectile_BurstCooldown": "Burst Cooldown",
"Weapon_ChargedProjectile_ChargeTime": "Charging Speed",
"Weapon_ChargedProjectile_ExtraSpeed": "Ion Sphere Speed",
"Weapon_Grenade_Damage": "Damage",
"Weapon_Grenade_Radius": "Explosion Radius",
"Weapon_Grenade_Speed": "Projectile Velocity",
"Weapon_Grenade_Bounce": "Bounce Potential",
"Weapon_Scan_Radius": "Scan Radius",
"Weapon_Scan_Discovery_Creature": "Fauna Analysis Rewards",
"Weapon_Scan_Discovery_Flora": "Flora Analysis Rewards",
"Weapon_Scan_Discovery_Mineral": "Mineral Analysis Rewards",
"Weapon_FireDOT_Duration": "Impact Fire Duration",
"Suit_Armour_Health": "Core Health",
"Suit_Armour_Shield_Strength": "Shield Strength",
"Suit_Energy": "Life Support Tanks",
"Suit_Energy_Regen": "Solar Panel Power",
"Suit_Protection_Cold": "Cold Protection",
"Suit_Protection_Heat": "Heat Protection",
"Suit_Protection_Toxic": "Toxic Protection",
"Suit_Protection_Radiation": "Radiation Protection",
"Suit_Underwater": "Oxygen Tank",
"Suit_DamageReduce_Cold": "Cold Damage Shielding",
"Suit_DamageReduce_Heat": "Heat Damage Shielding",
"Suit_DamageReduce_Radiation": "Radiation Damage Shielding",
"Suit_DamageReduce_Toxic": "Toxic Damage Shielding",
"Suit_Protection_HeatDrain": "Heat Resistance",
"Suit_Protection_ColdDrain": "Cold Resistance",
"Suit_Protection_ToxDrain": "Toxic Resistance",
"Suit_Protection_RadDrain": "Radiation Resistance",
"Suit_Stamina_Strength": "Sprint Distance",
"Suit_Stamina_Recovery": "Sprint Recovery Time",
"Suit_Jetpack_Tank": "Jetpack Tanks",
"Suit_Jetpack_Drain": "Fuel Efficiency",
"Suit_Jetpack_Refill": "Recharge Rate",
"Suit_Jetpack_Ignition": "Initial Boost Power",
"Ship_Weapons_Guns_Damage": "Damage",
"Ship_Weapons_Guns_Rate": "Fire Rate",
"Ship_Weapons_Guns_HeatTime": "Heat Dispersion",
"Ship_Weapons_Lasers_Damage": "Damage",
"Ship_Weapons_Lasers_HeatTime": "Heat Dispersion",
"Ship_Weapons_ShieldLeech": "Shield recharge on impact",
"Ship_Armour_Shield_Strength": "Shield Strength",
"Ship_Hyperdrive_JumpDistance": "Hyperdrive Range",
"Ship_Hyperdrive_JumpsPerCell": "Warp Cell Efficiency",
"Ship_Launcher_TakeOffCost": "Launch Cost",
"Ship_Launcher_AutoCharge": "Automatic Recharging",
"Ship_PulseDrive_MiniJumpFuelSpending": "Pulse Drive Fuel Efficiency",
"Ship_Boost": "Boost",
"Ship_Maneuverability": "Maneuverability",
"Ship_BoostManeuverability": "Boost Maneuverability",
"Freighter_Hyperdrive_JumpDistance": "Hyperdrive Range",
"Freighter_Hyperdrive_JumpsPerCell": "Warp Cell Efficiency",
"Freighter_Fleet_Speed": "Expedition Speed",
"Freighter_Fleet_Fuel": "Expedition Efficiency",
"Freighter_Fleet_Combat": "Expedition Defenses",
"Freighter_Fleet_Trade": "Expedition Trade Ability",
"Freighter_Fleet_Explore": "Expedition Scientific Ability",
"Freighter_Fleet_Mine": "Expedition Mining Ability",
"Vehicle_EngineFuelUse": "Fuel Usage",
"Vehicle_EngineTopSpeed": "Top Speed",
"Vehicle_BoostSpeed": "Boost Power",
"Vehicle_BoostTanks": "Boost Tank Size",
"Vehicle_SubBoostSpeed": "Acceleration",
"Vehicle_LaserDamage": "Mining Laser Power",
"Vehicle_LaserHeatTime": "Mining Laser Efficiency",
"Vehicle_GunDamage": "Damage",
"Vehicle_GunHeatTime": "Weapon Power Efficiency",
"Vehicle_GunRate": "Rate of Fire",
# Technologies
"UP_COLD": "Cold Protection",
"UP_ENGY": "Life Support",
"UP_HAZ": "Hazard Protection",
"UP_HOT": "Heat Protection",
"UP_JET": "Movement System",
"UP_RAD": "Radiation Protection",
"UP_SHLD": "Defence Systems",
"UP_SNSUIT": "Sentinel Exosuit Fragment",
"UP_RBSUIT": "Rebuilt Exosuit Module",
"UP_TOX": "Toxic Protection",
"UP_UNW": "Aeration Membrane",
"UP_HYP": "Hyperdrive",
"UP_LAUN": "Launch Thruster",
"UP_PULSE": "Pulse Engine",
"UP_S_SHL": "Deflector Shield",
"UP_SBLOB": "Cyclotron Ballista",
"UP_SGUN": "Photon Cannon",
"UP_SLASR": "Phase Beam",
"UP_SMINI": "Infra-Knife Accelerator",
"UP_SSHOT": "Positron Ejector",
"UA_HYP": "Singularity Cortex",
"UA_LAUN": "Neural Assembly",
"UA_PULSE": "Pulsing Heart",
"UA_S_SHL": "Scream Suppressor",
"UA_SGUN": "Spewing Vents",
"UA_SLASR": "Grafted Eyes",
"UP_BOLT": "Boltcaster",
"UP_CANN": "Neutron Cannon",
"UP_GREN": "Plasma Launcher",
"UP_LASER": "Mining Beam",
"UP_RAIL": "Blaze Javelin",
"UP_SCAN": "Analysis Visor",
"UP_SENGUN": "Sentinel Weapons Shard",
"UP_SHOT": "Scatter Blaster",
"UP_SMG": "Pulse Spitter",
"UP_TGREN": "Geology Cannon",
"UP_BOOST": "Exocraft Boosters",
"UP_EXENG": "Fusion Engine",
"UP_EXGUN": "Mounted Cannon",
"UP_EXLAS": "Exocraft Mining Laser",
"UP_EXSUB": "Humboldt Drive",
"UP_SUGUN": "Nautilon Cannon",
"UP_MCENG": "Daedalus Engine",
"UP_MCGUN": "Minotaur Cannon",
"UP_MCLAS": "Minotaur Laser",
"UP_MFIRE": "Minotaur Flamethrower",
"UP_FRCOM": "Expedition Defenses Control",
"UP_FREXP": "Expedition Scientific Control",
"UP_FRFUE": "Expedition Efficiency Control",
"UP_FRHYP": "Freighter Hyperdrive",
"UP_FRMIN": "Expedition Mining Control",
"UP_FRSPE": "Expedition Speed Control",
"UP_FRTRA": "Expedition Trade Control",
}
def _(original: str) -> str:
return TRANSLATION.get(original, original)
# endregion
class Pi():
# region Property
def __init__(self):
super().__init__()
# argparse
self.debug = False
self._language = LANGUAGES[1] # english. name of column to use for the procedural names
# properties to work with
self.link_settings = {
"column_product": -1,
"column_technology": -1,
"row_first": -1,
}
self.workbook = Workbook()
@property
def language(self):
return self._language
@language.setter
def language(self, value):
self._language = [l for l in LANGUAGES if value in l][0]
@language.deleter
def language(self):
del self._language
# endregion
def add_products(self) -> None:
product_sheet = self.sheet_create("Product")
# insert header
product_sheet.append(["", "Seed", "Perfection", "Age", "Value"])
# insert data
for item_name in PRODUCT:
print("Product", item_name) # to show progress
product = self.get_product_with_pandas(item_name)
self.insert_product(product_sheet, item_name, product)
self.insert_in_overview_sheet("Product", item_name)
self.sheet_autofit_column_width(product_sheet)
self.column_3_set_border_right(product_sheet)
def add_technologies(self) -> None:
for inventory_type, items in TECHNOLOGY.items():
inventory_translation = _(inventory_type)
inventory_sheet = self.sheet_create(inventory_translation)
for item_id, qualities in items.items():
print(inventory_type, item_id) # to show progress
technologies = self.get_technology_with_pandas(inventory_type, item_id, qualities)
anchor_coordinate = self.insert_technology(inventory_sheet, item_id, technologies)
self.insert_in_overview_sheet(inventory_translation, item_id, target_coordinate=anchor_coordinate)
self.sheet_autofit_column_width(inventory_sheet)
self.column_3_set_border_right(inventory_sheet)
@staticmethod
def get_quality_information(item_name: str) -> tuple:
# C -
# S Supreme
# X Illegal
# ? Forbidden (Sentinel)
# ! Salvaged (Autophage)
quality = item_name[-1]
# more general but defined case first
if quality == "X":
return "Illegal Upgrade (X)", COLOR_METAL
# only used for UP_GREN and there C class is 1
if quality == "1":
return "Upgrade (1)", COLOR_FLORA
# special cases in order of appearance
if item_name in ["UP_SNSUIT", "UP_SENGUN"]:
return "Forbidden Upgrade (?)", COLOR_SPECIAL
if item_name == "UP_RBSUIT":
return "Salvaged Upgrade (!)", COLOR_SPECIAL
# no special case, therefore it can only be this
return f"Supreme Upgrade ({quality})", COLOR_COMMODITY
def write_xlsx(self) -> None:
self.workbook.save(F_NAME)
# region Helper for OpenPyXL
@staticmethod
def cell_set_fill_color(cell: Cell, fill: Fill) -> None:
if not isinstance(fill, Fill):
fill = PatternFill(fill_type=fills.FILL_SOLID, start_color=fill)
cell.fill = fill
@staticmethod
def cell_set_hyperlink(cell: Cell, hyperlink: str) -> None:
cell.hyperlink = hyperlink
cell.style = "Hyperlink"
@staticmethod
def column_3_set_border_right(sheet: Worksheet) -> None:
for row in sheet.iter_rows(min_row=sheet.min_row, max_row=sheet.max_row - 1, min_col=3, max_col=3):
for cell in row:
cell.border = BORDER_MIXED if cell.border.bottom.style else BORDER_RIGHT
@staticmethod
def row_max_set_border_bottom(sheet: Worksheet) -> None:
for column in sheet.iter_cols(min_col=sheet.min_row, max_col=9, min_row=sheet.max_row, max_row=sheet.max_row): # max_col not guaranteed 9
for cell in column:
cell.border = BORDER_MIXED if cell.border.right.style else BORDER_BOTTOM
@staticmethod
def row_max_set_fill_color(sheet: Worksheet, fill: Fill) -> None:
if not isinstance(fill, Fill):
fill = PatternFill(fill_type=fills.FILL_SOLID, start_color=fill)
for column in sheet.iter_cols(min_col=sheet.min_column, max_col=9, min_row=sheet.max_row, max_row=sheet.max_row): # max_col not guaranteed 9
for cell in column:
cell.fill = fill
@staticmethod
def row_max_merge_columns(sheet: Worksheet, start_column: int, end_column: int) -> None:
sheet.merge_cells(start_row=sheet.max_row, start_column=start_column, end_row=sheet.max_row, end_column=end_column)
@staticmethod
def sheet_autofit_column_width(sheet: Worksheet) -> None:
for column in sheet.columns:
letter = column[0].column_letter
sheet.column_dimensions[letter].width = max(len(str(cell.value)) for cell in column if "in game version" not in str(cell.value)) + 1
def sheet_create(self, name: str) -> Worksheet:
index = 0 if self.debug else None # insert at first position to see it w/o clicking
return self.workbook.create_sheet(name, index=index)
# endregion
# region Helper for Pandas
def get_best_per_stat(self, dataframe_source: pandas.DataFrame) -> typing.Tuple[pandas.DataFrame, int, bool]:
columns = self.get_stat_column_names_from_dataframe(dataframe_source)
columns_max = [dataframe_source[column].max() for column in columns]
if all(dataframe_source[column].min() == columns_max[i] for i, column in enumerate(columns)):
return dataframe_source.tail(1), len(columns), True
result = pandas.DataFrame()
# add the absolute best per stat. taken from data to include all stats
for column in columns:
best = dataframe_source.sort_values(by=[column, "Perfection", "Seed"], ascending=False).head(1)
result = pandas.concat([result, best])
# drop duplicates
result.drop_duplicates(subset="Seed", inplace=True)
# only keep those with the highest number of perfect stats when there are any with more than one
subset = {}
max_stats_per_seed = 0
for j, column in enumerate(columns):
for k in range(len(result)):
row = result.iloc[k]
is_perfect = [row[c] == columns_max[i] for i, c in enumerate(columns)]
is_perfect_sum = sum(is_perfect)
max_stats_per_seed = max(max_stats_per_seed, sum(not math.isnan(row[c]) for c in columns))
if is_perfect[j] and is_perfect_sum > subset.get(column, (column, 0))[1]: # get a default tuple if necessary
subset[column] = (row["Seed"], is_perfect_sum)
return result[result["Seed"].isin(value[0] for value in subset.values())], max_stats_per_seed, False
@staticmethod
def get_stat_column_names_from_dataframe(dataframes: pandas.DataFrame) -> typing.Set[str]:
if isinstance(dataframes, pandas.DataFrame):
dataframes = [dataframes]
result = set()
for dataframe in dataframes:
result.update({column for column in dataframe if column not in ["Seed", "Perfection"] and not column.startswith("Name")})
return sorted(result)
# build meta data similar to the NMS.py mod script to be able to recalaculate perfection for Suit_Jetpack_Ignition Hi/Lo desirability
def prepare_ignition_meta(self, dataframe: pandas.DataFrame) -> typing.Dict[str, typing.List[float]]:
meta = {
stat: [dataframe[stat].min(), dataframe[stat].max(), dataframe[stat].max() - dataframe[stat].min()]
for stat in self.get_stat_column_names_from_dataframe(dataframe)
}
weighting = [stat[2] + 1 for stat in meta.values()] # max - min + 1
weighting_min = min(weighting)
# add weighting to each stat
meta = {key: value + [weighting[i] / weighting_min] for i, (key, value) in enumerate(meta.items())}
# adjust Suit_Jetpack_Ignition as half will be worst desirable now
meta["Suit_Jetpack_Ignition"][2] /= 2
# add threshold for Suit_Jetpack_Ignition
meta["Suit_Jetpack_Ignition"].append(meta["Suit_Jetpack_Ignition"][1] - meta["Suit_Jetpack_Ignition"][2])
return meta
# recalaculate perfection for Suit_Jetpack_Ignition Hi/Lo desirability
@staticmethod
def calculate_ignition_perfection(meta: typing.Dict[str, typing.List[float]], dataframe: pandas.DataFrame) -> pandas.Series:
result = []
for i in range(len(dataframe)):
perfection = []
weighting_total = 0
# calculate perfection for each seed
for stat_name, stat_meta in meta.items():
stat_value = dataframe.loc[i, stat_name]
if numpy.isnan(stat_value):
continue
weight = stat_meta[3]
weighting_total += weight
p = 1.0
if stat_name != "Suit_Jetpack_Ignition" or stat_value > stat_meta[4]:
p -= (stat_meta[1] - stat_value) / stat_meta[2]
else:
p -= (stat_meta[0] - stat_value) * -1 / stat_meta[2]
perfection.append(p * weight)
# add calculated perfection
result.append((sum(perfection) / weighting_total) * (len(perfection) / 4))
return pandas.Series(result)
@staticmethod
def remove_undesirable_weapon_boltcater_stats(dataframe: pandas.DataFrame) -> pandas.DataFrame:
dataframe_desirable = dataframe[dataframe["Weapon_Projectile_Rate"].isna()]
return dataframe_desirable[dataframe_desirable["Weapon_Projectile_ReloadTime"].isna()]
@staticmethod
def remove_undesirable_suit_movement_system_stats(dataframe: pandas.DataFrame) -> pandas.DataFrame:
dataframe_desirable = dataframe[dataframe["Suit_Jetpack_Drain"].isna()]
return dataframe_desirable[dataframe_desirable["Suit_Stamina_Strength"].isna()]
# add the remaining best without duplicates until the target size is reached
@staticmethod
def fill_without_duplicates(dataframe_fill: pandas.DataFrame, dataframe_source: pandas.DataFrame, max_rows: int) -> pandas.DataFrame:
dataframe_fill.drop_duplicates(subset="Seed", inplace=True)
if len(dataframe_fill) > max_rows:
return dataframe_fill.sort_values(by=["Perfection", "Seed"], ascending=False) # just sort here
dataframe_remaining = dataframe_source[~dataframe_source["Seed"].isin(dataframe_fill["Seed"].values)] # is not in
head = dataframe_remaining.sort_values(by=["Perfection", "Seed"], ascending=False).head(int(max_rows - len(dataframe_fill))) # take difference to max_rows
return pandas.concat([dataframe_fill, head]).sort_values(by=["Perfection", "Seed"], ascending=False) # sort by perfection for end result
# endregion
# region Sheets
def generate_overview_sheet(self) -> None:
# use first sheet for the overview
sheet = self.workbook.active
# set new title
sheet.title = "Overview"
# add description and prepare links to the items
overview = [
["Every Item Procedural", "by zencq"],
["Version", VERSION],
[],
["This file is a automatically generated compilation of all files available at GitHub in the repository zencq/Pi."],
["The repository contains a collection of files with values of stats for every procedural item in No Man's Sky. This includes mainly technology upgrades but also the products (treasures/artifacts in-game)."],
["Link to GitHub", URL],
[],
[],
[],
["Technologies", "", "", "", "Products"],
]
for row in overview:
sheet.append(row)
# make first line bold
for column in sheet.iter_cols(min_col=1, max_col=2, min_row=1, max_row=1):
for cell in column:
cell.font = FONT_BOLD
# autofit column width
sheet.column_dimensions["A"].width = len(str(sheet["A1"].value))
# set data format of version cell
sheet["B2"].number_format = numbers.FORMAT_TEXT
# set hyperlink attributes to url cell
self.cell_set_hyperlink(sheet["B6"], URL)
# merge reference cells as header
self.row_max_merge_columns(sheet, 1, 3) # technology
self.row_max_merge_columns(sheet, 5, 8) # product
# set link for product
self.cell_set_hyperlink(sheet.cell(row=sheet.max_row, column=5), f"{F_BASE}#'Product'!A1")
# store data for to properly add links later
self.link_settings = {
"column_product": len(overview[-1]),
"column_technology": 1,
"row_first": len(overview) + 1,
}
def insert_in_overview_sheet(self, inventory_translation: str, item_id: str, target_coordinate = None) -> None:
is_technology = inventory_translation != "Product"
column = self.link_settings["column_technology" if is_technology else "column_product"]
item_translation = _(item_id)
row = self.link_settings["row_first"] + (TECHNOLOGY_INDEX.index(item_id) if is_technology else PRODUCT.index(item_id))
sheet = self.workbook["Overview"]
cell = sheet.cell(row=row, column=column, value=f"{inventory_translation}, {item_translation}")
if target_coordinate:
self.cell_set_hyperlink(cell, f"{F_BASE}#'{inventory_translation}'!{target_coordinate}")
def insert_product(self, sheet: Worksheet, item_name: str, product: pandas.DataFrame) -> str:
item_translation = _(item_name)
# insert item_name
cell = sheet.cell(row=sheet.max_row + 1, column=1, value=f"{item_translation} ({item_name})")
# merge columns of row with item_name
self.row_max_merge_columns(sheet, 1, 3)
self.row_max_merge_columns(sheet, 4, 5)
# make item_name bold
cell.font = FONT_BOLD
if len(product):
for i, row in enumerate(dataframe_to_rows(product)):
if i == 0:
index_name = row.index(self.language)
index_seed = row.index("Seed")
index_perfection = row.index("Perfection")
index_age = row.index("Age")
index_value = row.index("Value")
continue
if i == 1: # ignore as it is empty
continue
# add data
sheet.append([
row[index_name],
row[index_seed],
row[index_perfection],
row[index_age],
row[index_value],
])
sheet.cell(row=sheet.max_row, column=3).number_format = "0%" # perfection
else:
# add note that product is not available
if item_name in OUTDATED:
value = f"This product is not yet available in game version {VERSION} but was added in {OUTDATED[item_name]}."
else:
value = f"This product is not yet available in game version {VERSION}."
self.cell_set_fill_color(sheet.cell(row=sheet.max_row + 1, column=1, value=value), FILL_RED)
self.row_max_merge_columns(sheet, 1, 5)
sheet.append([""])
def insert_technology(self, sheet: Worksheet, item_id: str, data: typing.Dict[str, pandas.DataFrame]) -> str:
stats_raw = [stat for stat in self.get_stat_column_names_from_dataframe(data.values())]
# insert header
sheet.append([f"{_(item_id)} ({item_id})", "Seed", "Perfection"] + [_(stat) for stat in stats_raw])
# cache first row to later add it as reference in the overview sheet
anchor_cell = sheet.cell(row=sheet.max_row, column=1)
# make technology name bold
anchor_cell.font = FONT_BOLD
if not data:
# add note that technology is not available
if item_id in OUTDATED:
value = f"This technology is not yet available in game version {VERSION} but was added in {OUTDATED[item_id]}."
else:
value = f"This technology is not yet available in game version {VERSION}."
self.cell_set_fill_color(sheet.cell(row=sheet.max_row, column=4, value=value), FILL_RED)
sheet.cell(row=sheet.max_row, column=4).alignment = ALIGNMENT_CENTER
self.row_max_merge_columns(sheet, 4, 9)
else:
for (item_name, max_stats_per_seed, is_all_same), dataframe in data.items():
quality_translation, colors = self.get_quality_information(item_name)
outdated_item = item_id in OUTDATED and item_id or item_name in OUTDATED and item_name
if outdated_item:
# add note that technology is outdated
sheet.append([quality_translation, "", max_stats_per_seed, f"This technology is outdated in game version {VERSION} as it was changed in {OUTDATED[outdated_item]}."])
self.row_max_set_fill_color(sheet, colors[1])
self.cell_set_fill_color(sheet.cell(row=sheet.max_row, column=4), FILL_RED)
else:
sheet.append([quality_translation, "", max_stats_per_seed, "All seeds are the same." if is_all_same else ""])
self.row_max_set_fill_color(sheet, colors[1])
sheet.cell(row=sheet.max_row, column=3).alignment = ALIGNMENT_LEFT
sheet.cell(row=sheet.max_row, column=4).alignment = ALIGNMENT_CENTER
self.row_max_merge_columns(sheet, 4, 9)
for i, row in enumerate(dataframe_to_rows(dataframe)):
if i == 0:
index_name = row.index(self.language)
index_seed = row.index("Seed")
index_perfection = row.index("Perfection")
index_stats = {stat: row.index(stat) for stat in stats_raw}
continue
if i == 1: # ignore as it is empty
continue
# add data
sheet.append(
[
row[index_name],
row[index_seed],
row[index_perfection],
] + [row[index_stats[stat]] for stat in stats_raw]
)
# style added row
if i % 2 == 1:
self.row_max_set_fill_color(sheet, colors[0])
sheet.cell(row=sheet.max_row, column=3).number_format = "0.000%"
for column in sheet.iter_cols(min_col=4, max_col=4 + len(stats_raw) - 1, min_row=sheet.max_row, max_row=sheet.max_row):
for cell in column:
cell.number_format = "#,##0.00000"
# add darker color for best value per stat
if cell.value == dataframe[stats_raw[cell.col_idx - 4]].max():
self.cell_set_fill_color(cell, colors[1])
self.row_max_set_border_bottom(sheet)
sheet.append([""])
return anchor_cell.coordinate
# endregion
# region Pandas
def get_product_with_pandas(self, item_name) -> pandas.DataFrame:
f_name = f"{F_PATH}\\Product\\{item_name}.parquet"
if os.path.isfile(f_name):
data = pandas.read_parquet(f_name)
return data[data["Perfection"] == 1.0]
return pandas.DataFrame()
def get_technology_with_pandas(self, inventory_type, item_id, qualities: typing.List[str]) -> typing.Dict[str, pandas.DataFrame]:
result = {}
for i, quality in enumerate(qualities, 1):
item_name = f"{item_id}{quality}"
f_name = f"{F_PATH}\\{inventory_type}\\{item_name}.parquet"
if not os.path.isfile(f_name):
continue
data = pandas.read_parquet(f_name)
n = math.floor(3 / i) # 3 for best, 1 for second
columns = self.get_stat_column_names_from_dataframe(data)
available_stats = len(columns)
f_result, max_stats_per_seed, is_all_same = self.get_best_per_stat(data)
if not is_all_same:
dataframe_source = data
# special desirability for certain items
if item_id == "UP_JET":
# * DHarhan: for Movement, Recharge Rate + Sprint Recovery are more desirable than Fuel Effic + Sprint Dist
# * DHarhan: Movemement mods with lowest Initial Boost bonus are just as desirable as highest
# low bonus only for X as 4 always has high values
if quality == "X":
meta = self.prepare_ignition_meta(data)
dataframe_source = dataframe_source.assign(Perfection=lambda row: self.calculate_ignition_perfection(meta, row))
dataframe_desirable = self.remove_undesirable_suit_movement_system_stats(dataframe_source)
# add 1/3 each for low and high bonus
ignition_l = dataframe_desirable[dataframe_source["Suit_Jetpack_Ignition"] < meta["Suit_Jetpack_Ignition"][4]].sort_values(by=["Perfection", "Seed"], ascending=False).head(n)
ignition_h = dataframe_desirable[dataframe_source["Suit_Jetpack_Ignition"] > meta["Suit_Jetpack_Ignition"][4]].sort_values(by=["Perfection", "Seed"], ascending=False).head(n)
f_result = pandas.concat([f_result, ignition_l, ignition_h])
dataframe_source = self.remove_undesirable_suit_movement_system_stats(data) # do with original data
columns.remove("Suit_Jetpack_Drain")
columns.remove("Suit_Stamina_Strength")
if item_id == "UP_BOLT":
# * DHarhan: Boltcaster mods with Dmg, Clip, ShotsPer, & BurstCool are desirable due to pairing well with ? mods with Dmg, Rate, Reload
dataframe_source = self.remove_undesirable_weapon_boltcater_stats(dataframe_source)
columns.remove("Weapon_Projectile_Rate")
columns.remove("Weapon_Projectile_ReloadTime")
if item_id == "UP_GREN":
# * DHarhan: for PlasmaGren, the most desirable mods are just Damage+Velocity bonuses, followed by Dmg+Vel+Radius, never bounce
dataframe_source = dataframe_source[dataframe_source["Weapon_Grenade_Bounce"].isna()]
columns.remove("Weapon_Grenade_Bounce")
# * DHarhan: C Class P.Gren mods are better than S/A/B, because A forces an undesirable stat on it and S forces both unwanteds.
if quality == "1":
dataframe_source = dataframe_source[dataframe_source["Weapon_Grenade_Radius"].isna()]
columns.remove("Weapon_Grenade_Radius")
# add the overall best per stat that is notna if max stats per seed is less than possible stats
if available_stats > max_stats_per_seed:
for column in columns:
best = dataframe_source[dataframe_source[column].notna()].sort_values(by=["Perfection", "Seed"], ascending=False).head(1)
f_result = pandas.concat([f_result, best])
n *= HIGH_NUMBER_MULTIPLIER
f_result = self.fill_without_duplicates(f_result, dataframe_source, n + available_stats)
result[(item_name, max_stats_per_seed, is_all_same)] = f_result
return result
# endregion
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Generates a compilation of all files available in this repository.")
parser.add_argument("-d", "--debug", action="store_true", help="Enable debug mode.")
parser.add_argument("-l", "--language", choices=[RE_LANGUAGE.findall(l)[0] for l in LANGUAGES], default="en", help="Language to use for procedural names.")
pi = parser.parse_args(namespace=Pi())
pi.generate_overview_sheet()
pi.add_technologies()
pi.add_products()
pi.write_xlsx()