campStack.py

import json

from helpers import post_query


# Constants
STEAM_ACCOUNT_ID = # TODO
SKIPS = [0, 100, 200]


def post_camp_stack_query(skip):
    """Execute the GraphQL query with a dynamic skip value."""
    query = """{
    player(steamAccountId:%d) {
        matches(request: {
            isParsed: true, isParty: false,
            positionIds: POSITION_1, lobbyTypeIds: 7,
            take: 100, skip: %d
        }) {
            id
            didRadiantWin
            startDateTime
            players {
                isRadiant
                steamAccountId
                position
                stats {
                    campStack
                }
            }
        }
    }
}""" % (
        STEAM_ACCOUNT_ID,
        skip,
    )

    return post_query(query)


all_matches = []

for skip_value in SKIPS:
    response_data = post_camp_stack_query(skip_value)
    matches = response_data["data"]["player"]["matches"]
    all_matches.extend(matches)

# Ensure the result is 300 matches long, or however many were fetched
print(f"Total matches fetched: {len(all_matches)}")

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import linregress

# Assuming `all_matches` is already filled with the processed matches

# Process the matches
for match in all_matches:
    shefeto = next(
        player
        for player in match["players"]
        if player["steamAccountId"] == STEAM_ACCOUNT_ID
    )
    for player in list(
        match["players"]
    ):  # Use list to clone as we're modifying the iterable
        # Step 1b: Add isAlly
        player["isAlly"] = player["isRadiant"] == shefeto["isRadiant"]
        # Step 1d: Update campStack to the final value
        player["stats"]["campStack"] = (
            player["stats"]["campStack"][-1] if player["stats"]["campStack"] else 0
        )
    # Step 1c: Remove players with position == POSITION_1
    match["players"] = [
        player for player in match["players"] if player["position"] != "POSITION_1"
    ]

# Step 2: Reverse the matches
all_matches.reverse()

# Prepare data for chart
ally_campstack = []
enemy_campstack = []
for match in all_matches:
    ally_stacks = [
        player["stats"]["campStack"] for player in match["players"] if player["isAlly"]
    ]
    enemy_stacks = [
        player["stats"]["campStack"]
        for player in match["players"]
        if not player["isAlly"]
    ]
    if ally_stacks:
        ally_campstack.append(np.mean(ally_stacks))
    if enemy_stacks:
        enemy_campstack.append(np.mean(enemy_stacks))

# Calculate and print overall averages
print("Overall average campStack for allies:", np.mean(ally_campstack))
print("Overall average campStack for enemies:", np.mean(enemy_campstack))

# Plotting
x = range(len(all_matches))
plt.plot(x, ally_campstack, label="Allies", color="blue")
plt.plot(x, enemy_campstack, label="Enemies", color="red")

# Adding lines of best fit
slope, intercept, _, _, _ = linregress(x, ally_campstack)
plt.plot(x, np.array(x) * slope + intercept, "b--")

slope, intercept, _, _, _ = linregress(x, enemy_campstack)
plt.plot(x, np.array(x) * slope + intercept, "r--")

plt.xlabel("Match Index")
plt.ylabel("Average CampStack")
plt.title("CampStack Analysis")
plt.legend()
plt.show()