#%% 

#%% 
arr = [1, 2, 3, 4, 5, 6]
num = 7
def summer(arr, num):
    idx = 0
    for i in range(len(arr)-1):
        for j in range(i+1, len(arr)):
            if arr[i] + arr[j] == num:
                print(i, j)





summer(arr, num)
#%% 
import requests
import json

def authenticate_user(username, password):
    auth_url = "https://api.spaceandtime.io/v1/user/authenticate"
    headers = {
        "Content-Type": "application/json"
    }
    data = {
        "username": username,
        "password": password
    }
    response = requests.post(auth_url, headers=headers, data=json.dumps(data))
    if response.status_code == 200:
        token = response.json()["token"]
        return token
    else:
        raise Exception("Failed to authenticate user")

#%% 
pip install cufflinks

#%% 
import cufflinks as cf
cf.go_offline()




#%% 
import seaborn as sns
iris = sns.load_dataset('iris')

#%% 
iris[['sepal_length', 'sepal_width']].iplot(kind='scatter', mode='markers')

#%% 
import pandas as pd
import numpy as np

# Create a sample dataset
dates = pd.date_range('2023-01-01', '2023-02-28')
prices = np.random.randint(100, 200, len(dates))
volumes = np.random.randint(10000, 50000, len(dates))
df = pd.DataFrame({'date': dates, 'open': prices, 'high': prices + 10, 'low': prices - 10, 'close': prices, 'volume': volumes})

# Save the dataset to a CSV file
df.to_csv('sample_data.csv', index=False)

#%% 
import pandas as pd
import cufflinks as cf
from plotly.offline import iplot
from plotly.subplots import make_subplots

# Load data from CSV file into a Pandas dataframe
df = pd.read_csv('sample_data.csv')

# Convert datetime column to a Pandas datetime format
df['date'] = pd.to_datetime(df['date'])

# Resample data to daily frequency and aggregate using OHLC
df = df.set_index('date').resample('D').agg({'open': 'first', 'high': 'max', 'low': 'min', 'close': 'last', 'volume': 'sum'})

# Create a subplots object with two rows and one column
fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.02)

# Add a Candlestick chart to the top row
fig.add_trace(
    cf.Candlestick(
        x=df.index,
        open=df['open'],
        high=df['high'],
        low=df['low'],
        close=df['close']
    ),
    row=1, col=1
)

# Add a Volume bar chart to the bottom row
fig.add_trace(
    cf.Bar(
        x=df.index,
        y=df['volume'],
        opacity=0.5
    ),
    row=2, col=1
)

# Set the chart layout
fig.update_layout(
    title='Stock Price',
    yaxis=dict(title='Price'),
    yaxis2=dict(title='Volume', overlaying='y', side='right'),
    xaxis_rangeslider_visible=False
)

# Show the chart
iplot(fig)

#%% 
import pandas as pd
import requests
from io import StringIO

# Download gene expression data from GEO database
url = 'https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE100186&format=file&file=GSE100186%5Fraw%5Fdata%2Etxt%2Egz'
r = requests.get(url)

# Decompress the downloaded data and load it into a Pandas dataframe
data = StringIO(r.content.decode('utf-8'))
df = pd.read_csv(data, delimiter='\t')

# Drop columns that are not gene expression data
df = df.drop(['ID_REF', 'VALUE', 'detection p-value'], axis=1)

# Transpose the dataframe so that genes are rows and samples are columns
df = df.transpose()

# Rename the columns to sample names
df.columns = df.iloc[0]
df = df.drop('IDENTIFIER')

# Save the dataframe to a CSV file
df.to_csv('gene_expression_data.csv')

#%% 
import pandas as pd
import requests
from io import StringIO

# Download gene expression data from GEO database
url = 'https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE100186&format=file&file=GSE100186%5Fraw%5Fdata%2Etxt%2Egz'
r = requests.get(url)

# Decompress the downloaded data and load it into a Pandas dataframe
data = StringIO(r.content.decode('utf-8'))
df = pd.read_csv(data, delimiter='\t')

# Drop columns that contain gene expression data
df = df.drop(['CONTROL_TYPE', 'CONTROL_TYPE_DESCRIPTION'], axis=1)

# Transpose the dataframe so that genes are rows and samples are columns
df = df.transpose()

# Rename the columns to sample names
df.columns = df.iloc[0]
df = df.drop('IDENTIFIER')

# Save the dataframe to a CSV file
df.to_csv('gene_expression_data.csv')

#%% 
import pandas as pd
import requests
from io import StringIO

# Download gene expression data from GEO database
url = 'https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE100186&format=file&file=GSE100186%5Fraw%5Fdata%2Etxt%2Egz'
r = requests.get(url)

# Decompress the downloaded data and load it into a Pandas dataframe
data = StringIO(r.content.decode('utf-8'))
df = pd.read_csv(data, delimiter='\t')

# Drop irrelevant columns
df = df.drop(['INDEX', 'GENE_SYMBOL'], axis=1)

# Transpose the dataframe so that genes are rows and samples are columns
df = df.transpose()

# Rename the columns to sample names
df.columns = df.iloc[0]
df = df.drop('IDENTIFIER')

# Save the dataframe to a CSV file
df.to_csv('gene_expression_data.csv')

#%%